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spotfire s+! - TIBCO Product Documentation

1. To edit plots in individual panels 1 In the left pane of the Object Explorer select Graphs 2 Expand the branches until you see the plot panels appear as individual objects 3 Double click on the panel you wish to edit to open its property dialog Make the desired changes and click OK Figure 1 1 The Spotfire S help window Using the toolbar Table 1 1 lists the four main buttons on the help window toolbar in some cases you may see more Table 1 1 Help window toolbar buttons Button Name Description Hide or Show If the button is labeled Hide it hides the left pane expanding the right pane to the full width of the help window If the button is labeled Show it shows the left pane and partitions the help window accordingly Back Returns to previously viewed help topic Forward Moves to next help topic Help Support and Learning Resources Table 1 1 Help window toolbar buttons Continued Button Name Description Print Prints the current help topic Using the left pane Like the help window itself the left pane is divided into three parts the Contents tab the Index tab and the Search tab The Contents tab organizes help topics by category so that related help files can be found easily These categories appear as small book icons labeled with the name of the category To open a category
2. Horizontal stacked bar plot a 143 2 Click the E button on the Plots 2D palette Vertical stacked bar plot ln 15 107 5 0 L Y XXX A w ASt i S Nh N g 8 WN WS o 3 XG ISS 7 f Y RNY a amp SSV_ SS D g KS N l S gt T 3 NS 3 S S WN KK z WSN S 2 S Nl om Ly wW WN H ino 20 S z A S a 9 oO S s n Q n e 2 2 5 34 go oO 7 2 2 a 3 B of 3 qe TTE o v O 8 e fas H S Qa rs 3S 4 gt EB amp ge x l ie 5 2 F 5 g a Q od 2 ia a 3 a a a nn SSO o amp H s zo S gB 38 Ss om a p y SN oe w nn oO 3 ass v Fa TES S Gad Ag S MSG a g Be WN 2 g A gt gt US 8g h AAS 3 u B gt 5 s aaan S a g 6S CDW ov L Q 8 MASS par H s B N t5 x a S WOK o YJ lt 8 S WN 2 8 S QQ gt Ga ADL S S WR ANN 3 3 AC s ae SSG W MRA GG DBRECOK K o a z Chapter 4 Creating Plots PLOTTING MULTIDIMENSIONAL DATA 3D Scatter and 3D scatter and line plots display multidimensional data in three Line Plots dimensional space 3D regression plots which are just special kinds of 3D scatter and line plots draw a regression plane through the data points To create any of the 3D scatter line plots 1 Select the x y and z columns 2 Click the Plots 3D palet
3. Gain in Main Residential New Housing Starts Telephone Extensions 0 14 1 496 0 07 1 611 0 05 1 654 0 31 1 573 0 12 1 689 0 23 1 850 0 05 1 587 0 03 1 493 0 62 2 049 0 29 1 942 0 32 1 482 0 71 1 382 The data are best represented as a data set with two variables 1 Click the New Data Set button B on the Standard toolbar 2 Enter the 14 observations listed above Change the column variable names from the default V1 and V2 to diff hstart and tel gain respectively double click V1 and V2 to change the variable names 3 Rename the data set by double clicking the top shaded cell in the upper left hand corner of the Data window typing exmain in the Name field and clicking OK 61 Chapter 3 Exploring Data Scatter Plots 62 If you are responsible for planning how many new residence extensions you need to install next year and you can get an estimate of new housing starts for next year then you will naturally be interested in whether or not there is a strong relationship between diff hstart the increase in new housing starts each year and tel gain the increase in residence telephone extensions each year that is whether or not you can use diff hstart to predict tel gain As a first step in assessing whether or not there appears to be a strong relationship between these two variables we make a scatter plot as shown in Figure 3 5 2 0 4 T T T T T f T 0 85 0 60 0
4. fi 2 M AME IES AN JAN W Ne NN An UNOY Was Poa Figure 3 47 Surface plot of the exsurf data Using the same procedure as for 3D scatter plots rotate the plot to examine it from multiple angles Contour Plots Visualizing Multidimensional Data Creating a filled surface Next we will create a filled surface 1 Click a line in the 3D mesh to select the plot 2 Click the 16 Color Surface button on the Plots 3D palette to convert the surface to a filled surface plot with color varying by z value 3 Click the Color Scale Legend button W on the Graph toolbar to add a legend The resulting graph is shown in Figure 3 48 5 400 V3 3 600 W 800 Figure 3 48 Filled surface plot of the exsurf data Contour plots are flat two dimensional representations of three dimensional data They are often used for data collected on a grid If the data available are not on a grid interpolation is used to fit contours over a grid which are then plotted Creating a contour plot 1 Select all three columns in the exsurf Data window 111 Chapter 3 Exploring Data 112 2 Click the Contour button 2 on the Plots 2D palette The resulting graph is shown in Figure 3 49 Figure 3 49 Contour plot of the exsurf data The lines on a contour plot indicate locations of equal magnitude Contour plots are read in the same manner as contour maps They point out minima and maxima as wel
5. Start i i I absent present Kyphosis Figure 6 47 Box plots of the Kyphosis data Kyphosis is a postoperative spinal deformity We are interested in exploring how the covariates influence whether or not the deformity occurs Both Start and Number show strong location shifts with respect to the presence or absence of Kyphosis The Age variable does not show such a shift in location We can use logistic regression to quantify the influence of each covariate upon the likelihood of deformity 1 Open the Logistic Regression dialog 2 Type kyphosis in the Data Set field 3 Specify Kyphosis Age Number Start in the Formula field 4 Click OK A summary of the logistic regression appears in the Report window The summary contains information on the residuals coefficients and deviance The high value for Start indicates that it has a significant Regression influence upon whether kyphosis occurs The t values for Age and Number are not large enough to display a significant influence upon the response kk Generalized Linear Model Call glm formula Kyphosis Age Number Start family binomial link logit data kyphosis na action na exclude control list epsilon 0 9001 maxit 50 trace Fy Deviance Residuals Min 10 Median 30 Max 2 0123603 0 5484308 0 3631876 0 1658653 2 16133 Coefficients Value Std Error t value Intercept 2 03693225 1 44918287
6. Figure 4 3 Long form stacked data for creating a grouped box plot In multiple y form data see Figure 4 4 the x column determines the grouping levels of the data in two or more y columns When your data are structured in this way you select the x yl y2 y3 y4 and y5 columns to create the grouped box plot fi grouped box multiple _ oy x T yl 10 91 16 45 17 18 12 20 9 22 2 00 10 37 16 20 14 00 9 00 12 51 3 00 13 00 10 26 8 71 12 09 7 70 J gt Figure 4 4 Multiple y form data for creating a grouped box plot For insight into the data structure appropriate for any given plot type open the Insert Graph dialog refer to Figure 4 2 Following each plot type is a parenthetical listing of the various ways in which your data may be structured to produce that particular plot 121 Chapter 4 Creating Plots PLOTTING ONE DIMENSIONAL DATA Box Plots A box plot displays the locations of the basic features of the distribution of one dimensional data the median the upper and lower quartiles outer fences that indicate the extent of your data beyond the quartiles and outliers if any A box plot allows you to quickly grasp the location scale width and rough shape of the distribution of your data For example if the upper and lower quartiles of the box plot are about the same distance from the median then the distribution of your data is approximately symmetric
7. Distribution Line gt ate Style ma J 7 Color Me gt z eoo j Weight fi 7 z Std Dey i Function boo n nder boo Shape 1 TETN T Shape 2 E location ale Userkunctio E d 1 5o Other Arguments d2 We oa OK Cancel Apply current Help o Figure 3 4 The Distribution page of the QQ Plot dialog Does your t distribution QQ plot look any more linear Try QQ plots for some other distributions such as Uniform Keep in mind that the sample size is very small and you may wonder about the intrinsic variability of a normal QQ plot from sample to sample A useful exercise is to simulate samples of normal random numbers with each sample having the same length as your data 20 in 58 Visualizing One Dimensional Data the case of exmichel compute a QQ plot for each simulated normal random vector and observe the variability in the QQ plots Simulating random numbers is described in Chapter 6 59 Chapter 3 Exploring Data VISUALIZING TWO DIMENSIONAL DATA The Main Gain Data 60 In the previous section you learned how to make several types of plots that provide quick visual insight into the shape of the distribution of one dimensional data In this section you expand your toolkit of visual exploratory data analysis tools by learning how to make scatter plots line plots and some other types of plots of two dimensional data 2D plots Two dimensional data are often called biv
8. To produce a grouped bar with error plot using a palette button you must first stack all of your y data into a single column Then create a z column of the same length containing the values to use for the error bars Note that error bars cannot be automatically calculated for grouped bar plots To create a grouped bar with error plot H grouped bar with error oy x 1 Select the x y and z columns 141 Chapter 4 Creating Plots 2 Click the Mili button on the Extra Plots palette Grouped bar with error plot 8 T f 1998 Stacked Bar A stacked bar plot displays data in stacks of bars The x values are the Plots labels Multiple y columns determine the bar segment heights that is the height of the bottom segment in each stack is determined by the values in the first y column the height of the middle segment in each stack by the values in the second y column etc Note that error bars cannot be displayed in stacked bar plots To create a vertical stacked bar plot 1 Select the x and multiple y columns 2 Click the An button on the Plots 2D palette To create a horizontal stacked bar plot 1 Select the multiple y columns first then CTRL click to select the x column last 142 Plotting Two Dimensional Data
9. 1 Select the x open close high and low data in that order x open close high low Fi 1 0 2588 81 2619 37 2639 64 2584 01 1 2 00 2591 39 2547 75 2619 59 2534 23 1 3 00 2596 81 2615 77 2628 83 2546 17 t ed 4 ta inal aren ar area sal areas cal arar onl 2 Click the f button on the Plots 2D palette Candlestick plot ut 2620 4 2580 25404 2500 T T T T T T T 1 1 00 1 2 00 1 3 00 1 4 00 1 5 00 1 6 00 1 7 00 Error Bar Plots An error bar plot displays a range of error around plotted data points The x values determine the positions of the bars along the x axis If your data set contains an x column and multiple y columns Spotfire S automatically calculates and displays error bars See the online help for details To create a vertical error bar plot y z 1 Select the x y and z 479 9 09 9 61 57 65 16 96 columns to create an a se error bar plot of y using the z data to display error bars 2 Click the all button on the Plots 2D palette To create a horizontal error bar plot select the x and y columns in reverse order and click the button 148 Plotting Multidimensional Data I wz Vertical error bar plot Ei Horizontal error bar plot E 150 7 e EPEE 40 1007 507 F li i 30 F E 07 7 i 207 e 507 i l 10 i 100 7 ie 150 T T
10. 500 Typically you should not select an entire column in Excel as part of a data specification for a Spotfire S graph because Excel sends all rows of this column to Spotfire S for graphing even if the rows are empty This may cause errors in Spotfire S or a failure to create the graph The Excel add in fully supports multiple column and row selections and noncontiguous block selections in Excel to specify data for a Spotfire S graph For example consider the following data in Excel gt bi Sheet1 Sheet2 Sheets 4 Using Spotfire S With Microsoft Excel If you want to create two line plots in a graph you can select the data The Create Spotfire S Graph wizard treats the A column in this selection as the x data and the B and C columns as the y data In this case two line plots are created the first with x data as the A column and y data as the B column and the second with x data as the A column and y data as the C column You can also select the same data using noncontiguous column selection 7 Id 4 gt pi Sheet1 Sheet2 Sheets f 4 In this example rows 1 through 6 in column A are first selected then the CTRL key is held down and the block from B1 to C6 is selected This selection produces the same graph with two plots as in the first example If a plot expects only one column of data for a given dimension such as the x data for a line plot and more than one column is
11. Cancel Appt current Help Figure 6 49 The ANOVA dialog Example In One Way Analysis of Variance on page 234 we performed a simple one way ANOVA on the blood data set listed in Table 6 2 These data give the blood coagulation times for four different diets In general the ANOVA dialog can handle far more complicated designs than the one way ANOVA dialog In addition it generates diagnostic plots and provides more information on the results of the Random Effects ANOVA Analysis of Variance analysis We use the ANOVA dialog to reproduce the results of the earlier example We also generate some diagnostic plots to see how well our model suits our data 1 Ifyou have not done so already create the blood data set with the instructions given on page 236 2 Open the ANOVA dialog Enter blood as the Data Set 4 Enter the formula time diet for the one way ANOVA we are going to perform Alternatively select time as the Dependent variable and diet as the Independent variable As a third way of generating a formula click the Create Formula button select time as the Response variable and diet as a Main Effect You can use the Create Formula button to create complicated linear models and learn the notation for model specifications The online help discusses formula creation in detail 5 Click on the Plot page and check all seven possible plots 6 Click OK to do the analysis Spotfire S generates seven diag
12. Other Period fio Cancel Apply f current Figure 6 82 The ARIMA Modeling dialog Example In Autocorrelations on page 359 we computed autocorrelations for the lynx df time series The autocorrelation plot in Figure 6 81 displays correlations between observations in the lynx df data with a ten year cycle to the correlations We can model this as an autoregressive model with a period of 10 1 If you have not done so already choose File gt Load Library to load the example5 library The example5 library contains the lynx df data Open the ARIMA Modeling dialog Type lynx df in the Data Set field Select lynx as the Variable Specify an Autoregressive Model Order of 1 oO oe wo bh Select Other as the Seasonality Lag Plot Time Series 7 Specify a Period of 10 8 Click OK Summaries for the ARIMA model are displayed in the Report window xkk ARIMA Model Fitted to Series lynx df L lynx Call arima mle x lynx df lynx model model xreg xreg max iter nlmin max iter max fcal nimin max fcal Method Maximum Likelihood Model 100 Period 10 Coefficients AR 0 73883 Variance Covariance Matrix ar 10 ar 10 0 004366605 Optimizer has converged Convergence Type relative function convergence AIC 1793 16261 The Lag Plot dialog plots a time series versus lags of the time series Creating a lag plot From the main menu choose Statistics gt Ti
13. M Save Dissimilarities Cancel Apply K current Help Figure 6 67 The Agglomerative Hierarchical Clustering dialog 335 Chapter 6 Statistics Divisive Hierarchical Clustering 336 Example In K Means Clustering on page 330 we clustered the information in the state df data set using the k means algorithm In this example we use an agglomerative hierarchical method 1 If you have not done so already choose File gt Load Library to load the examp1e5 library This library contains a few example data sets that are not in the main Spotfire S databases including state df 2 Open the Agglomerative Hierarchical Clustering dialog 3 Type state df in the Data Set field 4 CTRL click to select the Variables Population through Area 5 Click OK A summary of the clustering appears in the Report window Example 2 In Compute Dissimilarities on page 329 we calculated dissimilarities for the fuel frame data set In this example we cluster the fuel frame dissimilarities using the agglomerative hierarchical algorithm 1 If you have not already done so create the object fuel diss from the instructions on page 330 2 Open the Agglomerative Hierarchical Clustering dialog 3 Select the Use Dissimilarity Object check box 4 Select fuel diss as the Saved Object 5 Click OK A summary of the clustering appears in the Report window Hierarchical algorithms proceed by combining or div
14. 1 Select the x and y columns 2 Click the button on the Extra Plots palette Smith reflection plot In the Smith impedance plot the x values are resistance data and the y values are reactance data To create a Smith impedance plot l Select the x and y columns Plotting Multidimensional Data Click the D button on the Extra Plots palette Right click a plot element and select Options from the shortcut menu In the Data Options group select Impedance in the Data Type field Click OK Smith impedance plot In the Smith circle plot the x values which must be positive specify the distance from the center of the Smith plot to the center of the circle you want to draw The y values are angles measured clockwise from the horizontal The z values are radii and must also be positive To create a Smith circle plot 1 Select the x y and z columns Click the gD button on the Extra Plots palette Right click a plot element and select Options from the shortcut menu In the Data Options group select Circle in the Data Type field Click OK 157 Chapter 4 Creating Plots Projection Plots 158 Smith circle plot Most of the 2D plot types can be project
15. For small samples n lt 50 or if there are substantial serial correlations between observations close in x value a prespecified fixed span smoother should be used Spline Smoother Supersmoother Smoothing Spline smoothers are computed by piecing together a sequence of polynomials Cubic splines are the most widely used in this class of smoothers and involve locally cubic polynomials The local polynomials are computed by minimizing a penalized residual sum of squares Smoothness is assured by having the value slope and curvature of neighboring polynomials match at the points where they meet Connecting the polynomials results in a smooth fit to the data The more accurately a smoothing spline fits the data values the rougher the curve and vice versa The smoothing parameter for splines is called the degrees of freedom The degrees of freedom controls the amount of curvature in the fit and corresponds to the degree of the local polynomials The lower the degrees of freedom the smoother the curve The degrees of freedom automatically determines the smoothing window by governing the trade off between smoothness of the fit and fidelity to the data values For n data points the degrees of freedom should be between 1 and n 1 Specifying n 1 degrees of freedom results in a curve that passes through each of the data points exactly Spotfire S uses 3 degrees of freedom by default which corresponds to cubic splines
16. Viewing and Formatting Data Opening For large data sets it is often convenient to display several different Concurrent Views views of the data in separate Data windows of a Data Set To open concurrent views of a data set do the following 1 Use the Select Data dialog to display the data in a Data window 2 From the main menu choose Window gt New Window Note You can edit your data in the original or any replicated Data window Any changes you make are immediately reflected in all the Data windows The name of the data set as it appears in the title bar of the original Data window becomes temporarily appended with 1 In the second Data window the name is appended with 2 This temporary naming convention continues as additional windows are opened However when you close the replicated windows the original name of the data set is restored Navigating a Data Spotfire S provides a number of useful keyboard and mouse Window shortcuts for quickly navigating a Data window These shortcuts are listed in Table 2 1 below Table 2 1 Keyboard and mouse shortcuts for navigating a Data window Action Keyboard Mouse Moves the screen left CTRL LEFT ARROW Click left scroll bar arrow Moves the screen right CTRL RIGHT ARROW Click right scroll bar arrow Moves to first column first row CTRL HOME Drag sliders to top and left arrows and click the cell Moves to last column last row CTRL EN
17. A a Figure 9 4 Opening a script file 465 Chapter 9 Using the Script and Report Windows Running a Script You can use the Run button or the Run menu option to execute From a Script Window 466 your scripts To run a script do one of the following Click the Run button on the Script window toolbar From the main menu choose Script gt Run To run a portion of a script do the following 1 Select the lines in the script that you want to run 2 From the main menu choose Script gt Run or click the Run button on the Script window toolbar When you run your script the Script window title changes to the name of the script followed by running When the script is stopped or has ended execution the title changes back to program To save a script in a script file do the following 1 Click the Save button on the Standard toolbar or from the File menu choose the Save or Save As menu item or type CTRL S 2 If you choose Save As or if the Script window has never been saved before a browser window will appear 3 Navigate to the folder of your choice and change the File name text field to the file name of your choice for instance savetrees ssc The Script Window 4 Click Save to create a new script file named savetrees ssc Save in E dates amp Al l 5 simplel ssc heal simple2 ssc simple3 ssc File name savetrees ssc Cancel Figure 9 5 Saving a script
18. Compare Samples 2 Enter the nineteen observations listed in Table 6 1 Change the column names by first double clicking on V1 and typing in gain high and then double clicking on V2 and typing in gain low Press ENTER or click elsewhere in the Data window to accept the changes 3 Rename the data set by double clicking in the upper left corner of the Data window In the dialog that appears type weight gain in the Name field and click OK Exploratory data analysis To begin we want to evaluate the shape of the distribution to see if both our variables are normally distributed To do this create the following plots for each of the variables a boxplot a histogram a density plot and a QO normal plot You can create these plots by selecting a column in the weight gain Data window opening the Plots 2D palette and clicking the appropriate plot buttons 80 100 120 140 160 180 gain high Figure 6 14 Exploratory data analysis plots for the high protein data 227 Chapter 6 Statistics 0 020 4 0 015 7 0 010 4 0 005 4 1607 1407 nD S fi gain high 1007 0 000 r T 70 90 110 T T T T T T T T T 130 150 170 190 2 of 0 1 2 gain high Normal Distribution Figure 6 14 Exploratory data analysis plots for the high protein data Continued 228 The plots for the high protein group are similar to the ones shown in Figure 6 14 They indicate that the data come
19. Data Set ffuel frame X Save As Statistic to Estimate Expression mean Mileage Figure 6 78 The Jackknife Inference dialog 353 Chapter 6 Statistics Example We obtain jackknife estimates of mean and variation for the mean of Mileage in the fuel frame data 1 Open the Jackknife Inference dialog 2 Type fuel frame in the Data Set field 3 Type mean Mileage in the Expression field 4 Click on the Plot page and notice that the Distribution of Replicates plot is selected by default 5 Click OK A jackknife summary appears in the Report window and a histogram with a density line is plotted in a Graph Sheet Example 2 In this example we obtain jackknife estimates of mean and variation for the coefficients of a linear model The model we use predicts Mileage from Weight and Disp in the fuel frame data set 1 Open the Jackknife Inference dialog 2 Type fuel frame in the Data Set field 3 Type the following in the Expression field coef Im Mileage Weight Disp data fuel frame 4 Click on the Plot page and notice that the Distribution of Replicates plot is selected by default 5 Click OK A jackknife summary appears in the Report window In addition three histograms with density lines one for each coefficient are plotted in a Graph Sheet 354 SMOOTHING Smoothing Smoothing techniques model a univariate response as a smooth function of a univariate predictor With stand
20. If parameter values for the theoretical distribution are not available they may be estimated from the observations automatically as part of the test for normal Gaussian or exponential distributions For other distributions the chi square test must be used if parameters are to be estimated In this case the parameters are estimated from the data separately from the test and then entered into the dialog 219 Chapter 6 Statistics Performing a one sample Kolmogorov Smirnov goodness of fit test From the main menu choose Statistics gt Compare Samples gt One Sample gt Kolmogorov Smirnov GOF The One sample Kolmogorov Smirnov Goodness of Fit Test dialog opens as shown in Figure 6 11 One sample Kolmogorov Smirnoy Goodness of Fit Test BBE m Data r Distribution Parameters Data Set facc process x Miniman Variable fx v Mazmun J Hypotheses Mean Alternative Hypothesis Stil Devistior it 4 Jtwo sided x n Distribution normal hd cin en Ress o i Save As t IV Print Results i Cancel Appt current Figure 6 11 The One sample Kolmogorov Smirnov Goodness of Fit Test dialog Example We create a data set called qcc process that contains a simulated process with 200 measurements Ten measurements per day were taken for a total of twenty days We use the Random Numbers dialog to generate the data set from a Gaussian distribution For more details on
21. 2 Ensure that of Conditioning Columns is set to 1 With these settings multiple panels will be created with the data grouped based on the values of the last selected column 3 Select V1 and then CTRL click to select columns V2 and V3 4 Click the Scatter button E on the Plots 2D palette The resulting graph is shown in Figure 3 44 v2 o o J o o o 0 2 eo Qo lt a o e o 0 9 7 E o 2o v1 Figure 3 44 Trellis plot of the sliced ball data 5 Turn conditioning mode off by again clicking the Set Conditioning Mode button amp on the Standard toolbar If you skip this step you may inadvertently have conditioning mode on in the subsequent plot 107 Chapter 3 Exploring Data 108 The data points are divided into four groups Data points with values of V3 between 0 9 and 0 4 are shown in the lower left panel Data points with the next highest values of V3 are shown in the panel to the right Notice that there is a diagonal gap running through the data This is also evident in the upper left panel This suggests that the data are not in fact random throughout the sphere 3D scatter plots To explore further we can create a 3D scatter plot This is perhaps the most natural plot to use for these data since the data represent points in three dimensional space 1 Select all three columns in the Data window 2 Click the 3D Scatter button amp on the Plots 3D palette The resulting grap
22. 29 Chapter 2 Working With Data Special note points in mind Formatting Columns Changing a Column Name 30 The key characteristic of CTRL click selection is that it imposes order on the selection process By contrast when dragging the cursor or using SHIFT click the order of selection is interpreted by default as left to right for columns or top to bottom for rows no matter how the action itself is actually performed Therefore when using these methods to select data keep the following e You must use CTRL click when you need to select noncontiguous columns or rows but be conscious of the order in which you make your selections e You must use CTRL click when you need to select a group of columns or rows in a specific order even if the columns or rows are contiguous e You can drag the cursor or use SHIFT click to select blocks of contiguous columns or rows as long as a left to right or top to bottom selection order is what you intend A column in a data set is a vertical group of cells that typically contains the data for a given variable Because Spotfire S is column oriented formatting and data manipulation tools operate on a column as a unit Spotfire S automatically numbers each column in a data set The column number is displayed in the column header and indicates the column s position in the Data window As soon as you enter a data value in an empty column Spotfire S automatically gives the
23. A Variable Span Smoother Technical Report No 5 Laboratory for Computational Statistics Department of Statistics Stanford University California Laird N M amp Ware J H 1982 Random Effects Models for Longitudinal Data Biometrics 38 963 974 Lindstrom MJ amp Bates D M 1990 Nonlinear Mixed Effects Models for Repeated Measures Data Biometrics 46 673 687 Snedecor G W amp Cochran W G 1980 Statistical Methods 7th ed Ames Iowa Iowa State University Press Venables W N amp Ripley B D 1999 Modern Applied Statistics with S PLUS 3rd ed New York Springer WORKING WITH OBJECTS AND DATABASES Introduction Understanding Object Types and Databases S PLUS Object Types Databases Introducing the Object Explorer Inserting and Deleting Explorer Pages Inserting and Deleting Folders Customizing the Object Explorer Working With Objects Finding Objects Filtering on Objects Manipulating Objects Organizing Your Work Using Project Folders Working With Chapters Attaching a Chapter 378 379 379 381 385 388 389 390 396 396 397 402 406 406 407 408 377 Chapter 7 Working With Objects and Databases INTRODUCTION 378 The Spotfire S environment is object oriented meaning everything in Spotfire S is a distinct editable object from data sets Graph Sheets and functions to menus dialogs toolbars and toolbar buttons Some of these objects such as data sets an
24. L L L L Po O AOO OSES S OEA S A S OR L L L L L 4 Fee o SF bos 4 ed 8 e Los J V1 a 0o00 o e L O 0 4 Fad i L P joz 1 27 o o o of 1 04 SB Ft a 8583 amp 0 8 0 64 8 V2 0920 8 8 8 L oad P g Pl E 0 24 a oF go wo oF 1 2 4 O o OD 1 0 J 8 oE g 3 g 8 Pps A ee o ae 3 ee o 53 o Lo 4 0 2 7 0 0 08 7 G Fe F 0 67 a cee o oFf 044 e a 9 000 V4 o L 02 oF gE Pa h 0 05 y m m a 2 F1 0 fo i 7 e ae o oo o o amp fos 4 8 0 6 4 0 4 0 2 087 ee Ro Po o r 067 ae o eop 0474 eo aw C a e Tal F a o Pal C 0 0 J oe Be Bo 5 o fos 4 ar o 08 eo a FOT JE F F oT cam 0 3 084 oe BS e 099 o of a o ow o o oa 000 e V8 024 Pa a L 0 0 0 2 04 0 6 0 8 0 00 2 0 40 6 0 8 1 01 2 0 2 04 0 6 0 8 1 0 0 3 05 07 0 9 Figure 3 36 Scatterplot matrix of eight different pollution sensors Examine the plot If the sensors vary in their ability to detect pollution we would expect to see some nonlinear relationships Look closely at the relationship between sensors 4 and 5 5 and 6 6 and 7 and 7 and 8 Do you see the nonlinear relationships the curve in the lines It seems that some of these sensors vary in their ability to detect gases so adding more than one sensor may increase detectability of the gases Trellis Graphs Trellis graphs allow you to view relationships between different variables in your data set through conditioning
25. Note Spotfire S does not support Win32s that is Windows 3 1x nor does it support Windows NT 3 51 Running Spotfire S Welcome to Spotfire S Super VGA or most other Windows compatible graphics cards and monitors with a resolution of 800x600 or better Microsoft mouse or other Windows compatible pointing device Windows compatible printer optional A connection to the Internet including a Web browser The following list describes the ways that you can launch Spotfire S in Windows Start the Spotfire S for Windows Graphical User Interface GUI from the Start menu Start the Spotfire S Workbench from the Start menu Start the Windows Console from the Start menu Start the Windows Console from a DOS command line for interactive use Run the Console version from a Windows batch file using Sqpe infile outfile Run the Spotfire S GUI version from a Windows batch file using Spotfire S BATCH Run the Spotfire S GUI version from Automation via the Excel add in SPSS add in or a custom Automation application such as from PharSight Run the Spotfire S GUI version via DDE Run the Console version via Connect C or Connect Java You can run Spotfire S for 32 bit Windows on a 64 bit Windows computer Chapter 1 Introduction Important Spotfire S for 64 bit Windows e Includes support for Spotfire S Batch the Spotfire S Console and the Spotfire S Workbench I
26. Robust MM regression Predicting a continuous response using an MM based robust fitting criterion Robust LTS regression Predicting a continuous response using a least trimmed squares fitting criterion Stepwise linear regression Selecting which variables to employ in a linear regression model using a stepwise procedure Generalized additive models Predicting a general response as a sum of nonparametric smooth univariate functions of the predictors Local loess regression Predicting a continuous response as a nonparametric smooth function of the predictors using least squares Nonlinear regression Predicting a continuous response as a nonlinear function of the predictors using least squares Generalized linear models Predicting a general response as a linear combination of the predictors using maximum likelihood Log linear Poisson regression Predicting counts using Poisson maximum likelihood Linear Regression Regression e Logistic regression Predicting a binary response using binomial maximum likelihood with a logistic link e Probit regression Predicting a binary response using binomial maximum likelihood with a probit link Linear regression is used to describe the effect of continuous or categorical variables upon a continuous response It is by far the most common regression procedure The linear regression model assumes that the response is obtained by taking a specific linear combination of the pre
27. Symbol Style Diamond Erm SymbolCoorg uses z Symbol Style5 V Triangle Dn Z Symbol Colors Uses gt Symbol Style 6 Circle Soid Symbol Colors Muse7 z Symbol Style 7 A Triange Up Z SymbolColrz Muses z Symbol Style 8 M Box Soid SymbolColrs Muse Symbol Style 3 Diamond Sc SymbolColrs Muses gt Symbol Style 10 Y Triangle Dn Z SymbolColor10 Muses gt Hep Figure 11 17 The Symbols page of the Color Style dialog 544 Pattern Color Changing Defaults and Settings The Symbols page has ten style and ten color fields Use these fields to select symbol styles and colors in the order in which you want them to cycle The Pattern Color page of the Color Style dialog is shown in Figure 11 18 Options Lines Symbols Pattern Color Fill Color Fill Pattern 1 Plot Default x Pattern Color 1 Plot Defaut x Fill Pattern 2 Patten Color 2 Blue X Fill Pattern 3 Pattem Color CLG gt Fill Pattern 4 Pattern Color 4 EE Magenta Fill Pattern 5 Pattern Color 5 C Briw Fill Pattern 6 Pattern Color6 HE Blue X Fill Pattern 7 Pattern Color 7 E gt Fill Pattern 8 Pattem Colors Meck gt Fill Pattern 3 Pattem Color 3 I Blue X Fill Pattern 10 N 7 Pattern Color 10 E Lt Gray x A Figure 11 18 The Pattern Color page of the Color Style dialog The Pattern Color page has ten fill pattern and ten pattern color fields Use these fields
28. The data you are interested in may have been created in Spotfire S but more likely it came to you in some other form perhaps as an ASCII file or perhaps from someone else s work in another software package such as SAS You can read data from a variety of sources using the S PLUS function importData For example say you have a SAS file named test sd2 in your Spotfire S working directory To import that file using the importData function you must supply that function s two required arguments file the name of the file to read and type the type of file to read gt myData lt importData file test sd2 type SAS When Spotfire S reads the data file it creates the myData data frame and displays it in a Data window To get a small data set into Spotfire S create an S PLUS data object using the function scan with no argument mydata lt scan where mydata is any legal data object name Spotfire S prompts you for input as described in the following example We enter 14 data values and assign them to the object diff hs At the Spotfire S prompt type the name diff hs and assign to it the results of the scan command Spotfire S responds with the prompt 1 which means that you should enter the first value You can enter as many values per line as you like separated by spaces When you press RETURN Spotfire S prompts with the index of the next value it is waiting for In the following example Spotfire S respo
29. s Guide The simplest type of data object is a one way array of values all of which are numbers logical values or character strings but not a combination of those For example you can have an array of numbers 2 0 3 1 5 7 7 3 Or you can have an array of logical values T T F T F T F F where T stands for TRUE and F stands for FALSE Or you can have an ordered set of character strings sharp claws COLD PAWS These simple one way arrays are called vectors when stored in Spotfire S The class vector is a virtual class encompassing all basic classes whose objects can be characterized as one way arrays In a vector any individual value can be extracted and replaced by referring to its index or position in the array The length of a vector is the number of values in the array valid indices for a vector object x are in the range 1 1ength x Most vectors belong to one of the following classes numeric integer logical or character For example the vectors described above have length 4 8 and 2 and class numeric logical and character respectively Spotfire S assigns the class of a vector containing different kinds of values in a way that preserves the maximum amount of information character strings contain the most information numbers contain somewhat less and logical values contain still less Spotfire S coerces less informative values to equivalent values of the more informative type gt cf17 TRUE FALSE lj 17 1
30. which denotes a skipped column One of the characters specified in the Delimiter field must separate each specification in the string For example the format string AS BT br bf imports the first column of the data file as type character and the second and fourth columns as type numeric and skips the third column altogether 191 Chapter 5 Importing and Exporting Data If you specify a variable to be type numeric and the value of any cell cannot be interpreted as a number that cell is filled with a missing value Incomplete rows are also filled with missing values Note Some dates in text files may be imported automatically as numbers After importing data that contain dates you should check the class of each column and if necessary change it to the appropriate data type 192 Format strings and field width specifications are irrelevant for regular ASCII files and are therefore ignored For FASCII files however you can specify an integer that defines the width of each field For example the format string 4f 6S 23 6f imports the first four entries in each row as a numeric column The next six entries in each row are read as characters the next three are skipped and then six more entries are imported as another numeric column Format strings for exporting data When exporting data to a FASCII file the syntax accepted by the Format string field is similar to that for importing data Howeve
31. 1 405573 Age 0 01093048 0 00644419 1 696175 Number 0 41060098 0 22478659 1 826626 Start 0 20651000 0 06768504 3 051043 Dispersion Parameter for Binomial family taken to be 1 Null Deviance 83 23447 on 80 degrees of freedom Residual Deviance 61 37993 on 77 degrees of freedom Number of Fisher Scoring Iterations 5 Probit The Probit Regression dialog fits a probit response model This is a Regression variation of logistic regression suitable for binomial response data Fitting a probit regression model From the main menu choose Statistics Regression Probit The Probit Regression dialog opens as shown in Figure 6 48 297 Chapter 6 Statistics 298 Probit Regression BEI Model Options Results Plot Predict m Data Data Set kyphosis x Model Weights x Link pot Subset Rows with fi z Save Model Object M Omit Rows with Missing Values Save As Variables Dependent kyphosis Independent Formula kyphosis Age NumbersStat 0 Create Formula Cancel Apply f current The Probit Regression dialog ee Figure 6 48 Example In this example we fit a probit regression model to the kyphosis data set 1 Open the Probit Regression dialog 2 Type kyphosis in the Data Set field 3 Specify Kyphosis Age Number Start in the Formula field 4 Click OK A summary of the model is printed in the Report window kk Generalized Linear
32. 182 Importing From and Exporting to ODBC Tables The Filter page of the Import From ODBC dialog is shown in Figure 5 8 Import From ODBC 5 x ODBC Fiter Filter expression a i Cancel Appt current Figure 5 8 The Filter page of the Import From ODBC dialog Filter expression By specifying a query or filter expression you can subset the data you import By default this field is blank resulting in all the data being imported For more information on the syntax accepted by this field see Filter Expressions on page 187 Exporting to The Export to ODBC dialog shown in Figure 5 9 below consists of an ODBC Table two tabbed pages labeled ODBC and Filter To open the dialog do the following 183 Chapter 5 Importing and Exporting Data e From the main menu choose File gt Export Data gt To ODBC Connection Export to ODBC o x ODBC Fiter From Dat frame bd To Data Target lt SELECTTARGET gt O Add Targets Modify Target Table Name Cancel Appt current Figure 5 9 The ODBC page of the Export to ODBC dialog ODBC page From group Data frame Type or select the name of the data set you want to export ODBC page To group Data Target Select the desired data target from the dropdown list in this field Note A data target is the counterpart when exporting to a data source when importing If the desired data target does not app
33. 4 Click OK A summary of the test appears in the Report window The p value of 0 0056 indicates that we reject the null hypothesis of equal proportions parameters Hence we cannot conclude that all groups have the same probability that a patient is a smoker Fisher s exact test is a test for independence between the row and column variables of a contingency table When the data consist of two categorical variables a contingency table can be constructed reflecting the number of occurrences of each factor combination Fisher s exact test assesses whether the value of one factor is independent of the value of the other For example this might be used to test whether political party affiliation is independent of gender Certain types of homogeneity for example homogeneity of proportions in a kX2 table are equivalent to the independence hypothesis Hence this test may also be of interest in such cases As this is an exact test the total number of counts in the cross classification table cannot be greater than 200 In such cases the chi square test of independence is preferable Performing Fisher s exact test From the main menu choose Statistics gt Compare Samples gt Counts and Proportions Fisher s Exact Test The Fisher s Exact Test dialog opens as shown in Figure 6 23 BE EI E Fisher s Exact Test Data Data Set r Results ffisher trial E Save As 7 V Print Results Wearable 1 vanane
34. Formula Formula censorldays event voltage Create Formula Help Cancel Appt current The Life Testing dialog Example We use the Life Testing dialog to examine how voltage influences the probability of failure in the capacitor data set 1 2 3 Open the Life Testing dialog Type capacitor in the Data Set field Enter the Formula censor days event voltage or click the Create Formula button to construct the formula The censor function creates a survival object which is the appropriate response variable for a survival formula It is similar to the Surv function but provides more options for specifying censor codes To use the Formula builder click the Create Formula button In the dialog that appears highlight the days variable and click the Time 1 button Highlight the event variable click the Censor Codes button and then click Add Response Finally highlight the voltage variable and click Main Effect This generates the formula censor days event type right voltage Survival By default the censoring argument type is set to right when only one time variable is given Thus this formula is equivalent to censor days event voltage 4 Click OK A summary of the fitted model appears in the Report window 321 Chapter 6 Statistics TREE Tree Models 322 Tree based models provide an alternative to linear and additive models for regression problems and to linear an
35. In the first expression you are supplying the optional arguments by value When supplying optional arguments by value you must supply all the arguments in the order they are given in the help file USAGE statement In the second through fourth expressions above you are supplying the optional arguments by name When supplying arguments by name order is not important However we recommend that for consistency of style you supply optional arguments after required arguments The third and fourth expressions illustrate that you can abbreviate the formal argument names of optional arguments for convenience so long as the names are uniquely identified You will find that supplying arguments by name is convenient because you can then supply them in any order Of course you do not need to specify all of the optional arguments For instance the following are two equivalent ways to produce 50 random normal numbers with mean 0 the default and standard deviation 5 gt rnorm 50 m 0 s 5 gt rnorm 50 s 5 437 Chapter 8 Using the Commands Window IMPORTING AND EDITING DATA Reading a Data File Entering Data From the Keyboard 438 There are many kinds and sizes of data sets that you may want to work on in Spotfire S The first step is to get your data into Spotfire S in appropriate data object form In this section we show you how to import data sets that exist as files and how to enter small data sets from your keyboard
36. ODBC standard Designed to provide a unified standard way to exchange data Note As of Spotfire S version 8 2 native database drivers are deprecated In lieu of these drivers you should use JDOBC ODBC drivers for all supported database vendors between databases ODBC has become widely supported Each application typically has an ODBC driver that allows the application to accept or distribute data via the ODBC interface Spotfire S supports ODBC versions 2 0 and 3 0 Spotfire S provides convenient dialogs for importing data from and exporting data to ODBC databases and applications that support the ODBC specification By default Spotfire S imports an entire table into an S PLUS data frame and displays it in a Data window Similarly Spotfire S exports an entire data set into an ODBC table unless you specify otherwise Note If you prefer not to have your imported data automatically displayed in a Data window choose Options gt General Settings from the main menu click the Data tab and clear the Show Imported Data in View check box The ODBC Data The ODBC Data Source Administrator manages database drivers Source Administrator and data sources You can find the ODBC Data Source Administrator from the Windows Start menu Click Control Panel gt Administrative Tools and look for the Administrator icon Data Sources ODBC 179 Chapter 5 Importing and Exporting Data ODBC Drivers Defin
37. column to plot its values along the vertical axis against an integer sequence along the horizontal or select both x and y to plot y against x 2 Click the Plots 2D palette button corresponding to the desired plot To create the high density line Y zero plot click the E button on the Extra Plots palette Scatter plot Ls Line plot Vas Line with scatter plot Z 0 48 4 0 46 4 0 44 7 0 42 4 0 40 4 0 38 Vertical step plot Lea 0 48 4 0 46 7 gt 0 44 4 0 42 7 0 40 7 0 38 T T T T Vertical high density line plot Lil 0 48 7 0 46 7 gt 0 444 0 42 7 0 38 T 10 20 30 40 Plotting Two Dimensional Data Line with isolated points plot Ka KS ka EF 0 48 4 0 46 4 0 44 7 0 42 4 Z ONANAN 0 40 7 0 38 Horizontal step plot E 0 48 7 0 46 7 gt 0 44 7 0 42 7 0 40 7 0 38 Horizontal high density line plot E 0 48 T 0 46 7 0 44 7 0 42 0 40 0 38 133 Chapter 4 Creating Plots High density line Y zero plot al to 0 4 7 Curve Fitting A curve fitting plot displays a regression line with a scatter plot of the Plots associated data points Regression lines are gener
38. enzyme Historical information indicates that the average enzyme level is 120 with a standard deviation of 15 A difference in average level of 10 or more between the treatment and control groups is considered to be of clinical importance The scientist wants to determine what sample sizes are necessary for various combinations of alpha the probability of falsely claiming the groups differ when they do not and power the probability of correctly claiming the groups differ when they do The Normal Power and Sample Size dialog produces a table of sample sizes for various combinations of alpha and power 1 Open the Normal Power and Sample Size dialog Binomial Proportion Power and Sample Size 2 Select Two Sample as the Sample Type Enter 120 as Mean1 130 as Mean2 and 15 for both Sigmal and Sigma2 4 Enter 0 025 0 05 0 1 for Alpha s and enter 0 8 0 9 for Power s We calculate equal sample sizes for all combinations of these alpha and power values 5 Click OK A power table is displayed in the Report window The table indicates what sample sizes n1 and n2 are needed for each group at various levels of alpha and power For example the scientist needs 36 subjects per group to determine a difference of 10 at an alpha of 0 05 and power of 0 8 eee Power fable meanl sdl mean2 sd2 delta alpha power nl n2 1 129 w 1 15 10 0 025 0 8 43 43 i120 15 130 15 10 0 050 0 8 36 36 3 120 15 130 15 10 0 100 0 8 28 28 4 1
39. folder When you create a new project folder using the Open Spotfire S Project dialog these folders are created for you automatically Working With In Spotfire S databases are associated with chapters Each chapter Chapters folder contains its own Data folder for holding the database objects In addition to the working data Data folder associated with a 407 Chapter 7 Working With Objects and Databases particular project folder you may have other databases that you would like to access during a session To access the objects contained in a database simply attach the database by attaching its chapter Attaching a To attach a chapter or to simultaneously create and attach a new Chapter chapter open the Attach Create Chapter dialog by doing one of the following e In the left pane of the Object Explorer right click the SearchPath object s icon or a database icon and select Attach Create Chapter from the shortcut menu e From the main menu choose File gt Chapters gt Attach Create Chapter The Attach Create Chapter dialog opens as shown in Figure 7 17 Attach Create Chapter xj m Chapter Folder D mpdata H To create and attach a new chapter enter a folder name in the field above Jv Attach read onit Label inydata Position ma OK Cancel Help Figure 7 17 The Attach Create Chapter dialog 1 In the Chapter Folder text box do one of the following e To attach an existing ch
40. lt ALL gt in the Variables field 4 Click OK The Report window displays the correlations between the four variables wee Correlations for data in air ozone radiation temperature wind ozone 1 0000000 0 4220130 0 7531036 0 5989776 radiation 0 4220130 1 0000000 0 2940876 0 1273656 temperature 0 7531038 0 2940876 1 0000000 0 4971459 wind 0 5989278 0 1273656 0 4971459 1 0000000 Note the strong correlation of 0 75 between ozone and temperature as temperature increases so do the ozone readings The negative correlation of 0 60 between ozone and wind indicates that ozone readings decrease as the wind speed increases Finally the correlation Tabulate Summary Statistics of 0 50 between wind and temperature indicates that the temperature decreases as the wind increases or that the temperature increases as the wind decreases The Tabulate dialog creates a tabular summary of data from a data set Creating a table From the main menu choose Data gt Tabulate The Tabulate dialog opens as shown in Figure 6 7 Tabulate lolx Data r Options Data Set parley hd Maximum Unique Numeric Values Variables 10 Number of Bins for Numeric Values 6 r Results VV Print Table Save In Cancel Appt K current Figure 6 7 The Tabulate dialog Example The barley data set contains observations from a 1930s agricultural field trial that studied barley crops At six sites in Minnes
41. such labels have been defined So to display the above value which happens to be in the row named Arizona and the column named Area use the following command gt state x77 Arizona Area 1 113417 To select sequential rows and or columns from a matrix object use the operator for both the row and or the column index The following expression selects the first 4 rows and columns 3 through 5 for assignment to object x gt X lt State x77 134 375 2X Illiteracy Life Exp Murder Alabama Zal 69 05 15 4 Alaska leS 69 31 1133 Arizona Lee 70 55 7 8 Arkansas 1 9 70 66 10 1 The c function can be used to select rows and or columns of matrices just as it was used for vectors above For instance the following expression selects rows 5 22 and 44 and columns 1 4 and 7 of state x 77 gt state x77 c 5 22 44 c 1 4 7 Population Life Exp Frost California 21198 71T 20 Michigan 9111 Uses 125 Utah 1203 72 90 137 As before if row or column names have been defined they can be used in place of the index numbers gt state x77 Lec California Michigan Utah e Population Life Exp Frost J Population Life Exp Frost California 21198 Pisa 20 Michigan 9111 eS 125 Utah 1203 72 90 137 444 Extracting Subsets of Data To select all rows leave the expression before the comma blank to select all columns leave the expression after the comma blank The following expression selects all columns for the
42. we ve seen a variety of functions for data generation data manipulation and statistics You may find yourself using some combination of these functions repeatedly sometimes typing a long list of options over and over again You can increase your productivity and avoid typographical errors by incorporating these repetitive tasks into a single function To define a new function you type an expression of the following form newfunction lt function arguments body of definition where newfunction is the name you ve chosen for your new function arguments are the names of the arguments if any and body of definition contains one or more valid S PLUS expressions separated by semicolons or newlines For example suppose you are obtaining weather information from volunteer reporters Each reporter sends you once a month a listing of the daily high and low temperatures in his or her town Since the reporters are volunteers they are less than perfect in making and recording their observations So most of your listings contain missing values You patiently enter all their observations including the missing values using the value NA and then want to analyze the data You d like to obtain mean temperatures for each location and you find that the mean function has an argument na rm that you can use to remove the NAs before computing the various means But you have twenty locations for which you want to compute the means this ca
43. which you can make your selections When Preview column names is not selected you must type a column list of names see Creating a Column List on page 32 in this field Keep selected When selected Spotfire S exports only the columns you specify in the Select columns dropdown list Drop selected When selected Spotfire S exports all columns except those you specify in the Select columns dropdown list Filter page Filter rows group Preview row names When selected Spotfire S populates the Select Rows dropdown list with the row names of the data set you are exporting 177 Chapter 5 Importing and Exporting Data 178 Select Rows Specify the rows you want to export in this field When Preview row names is selected this field becomes a dropdown list from which you can make your selections When Preview row names is not selected you must type a row list of numbers see Creating a Row List on page 36 in this field Filter expression By specifying a query or filter expression you can subset the data you export By default this field is blank resulting in all the data being exported For more information on the syntax accepted by this field see Filter Expressions on page 187 Importing From and Exporting to ODBC Tables IMPORTING FROM AND EXPORTING TO ODBC TABLES Applications such as Microsoft Access and Excel as well as most commercial databases generically known as data sources support the Open DataBase Connectivity
44. 0 BS g lB 009 F 2g 5 v2 B OGO Bo 5 Mo an Pope PIP MB an 88 te gS Bo 8 Sog 2 R 98 w of amp ape oo ogaao 0 57 go o BPL Po amp OHS be 2 H amp Po w8 a p o Soh Be 855 0 90 2 op gpg 0 ag goo o Og 1 07 S J a8 a o EG Pa og 1 0 O o Qo 8 Pee Pay 29 Soh go SE Bee o D o e o D Sa g so BEB ox Ssi o gt Ke o Q 3 0 Be S Bo 6 By PS SQ og e 290 g OB Pog6 o OS 4 Der Oyo Boog F BS o Soogo S 3 L o 88 ABE 0 go o Og OF ay 0 Go aug v3 0 0 PESEE atte ce a F Be LBAS 08 P coe oy e 00m neg B So k Ta H CEB amp o 008 0 go 0 5 o Sg a Bo gso 0 y 000 PSR o La T T T T T T T T T T T T T T T 1 0 0 5 0 0 0 5 1 0 1 0 0 5 0 0 0 5 1 0 Figure 3 43 Scatterplot matrix of the sliced ball data There are no obvious relationships between any two columns of data so the bivariate plots suggest that the data are randomly distributed within the sphere No other relationships are apparent 106 Visualizing Multidimensional Data Trellis plot The limitation of bivariate plots is that they do not allow us to look at interactions between variables For example how does the relationship between V1 and V2 depend on V3 We can use Trellis plots to look for such interactions 1 Turn on conditioning mode by clicking the Set Conditioning Mode button on the Standard toolbar The small yellow bars at the top of each of the icons on the Plots 2D palette indicate that conditioning mode is on
45. 10 gt Bra 1 63 The symbols and represent S PLUS operators for addition and multiplication respectively In addition to the usual arithmetic and logical operators S PLUS also has special operators for special purposes For example the colon operator is used to obtain sequences gt 1 7 lL 22a es ey The 1 in each of the output lines is the index of the first element in the S PLUS return value If S PLUS is responding with a long vector of results each line is preceded by the index of the first response of that line gt 1230 fl 123 4 6 y 7 amp 8 10 U1 17 13 4 15 16 17 18 19 19 20 21 22 23 24 25 26 27 28 29 30 Basic Syntax Spaces Uppercase and Lowercase Commands Window Basics The most common S PLUS expression is the function call An example of a function in S PLUS is the c function used for combining comma separated lists of items into a single item Functions calls are always followed by a pair of parentheses with or without any arguments in the parentheses C1344 Piya 1 6 6 In all of our examples to this point S PLUS has simply returned a value which is printed in your Commands window To reuse the value of a S PLUS expression you must assign it with the lt operator For example to assign the above expression to an S PLUS object named newvec you d type the following gt newvec lt c 3 4 1 6 S PLUS creates the object newvec and returns a prompt
46. 103 gt Chl aketalel el La 7 FP eT gt c sharp teeth COLD PAWS 1 sharp teeth COLD PAWS gt c sharp teeth COLD PAWS 1 sharp teeth COLD PAWS The last example illustrates that either the double quote character or the single quote character can be used to delimit character strings Usually you want to assign the result of the c function to an object with another name that is permanently saved until you remove it For example gt weather lt c hot day COLD NIGHT 431 Chapter 8 Using the Commands Window Operators 432 gt weather 1 hot day COLD NIGHT Some functions in S PLUS are commonly used with no arguments For example recall that you quit Spotfire S by typing q The parentheses are still required so that Spotfire S can recognize that the expression is a function When you accidentally leave off the when typing a function name the function text is displayed on the screen Typing any object s name causes Spotfire S to print that object a function object is simply the definition of the function To call the function you need to retype the function name with parentheses For instance if you accidentally type q instead of q when you want to quit Spotfire S the body of the function q is displayed In this case the body of the function is only two lines long gt q functiones internal tals 7a S dummy Te 33 gt No harm has been done
47. 300 Ss 1994 1995 1996 1997 1998 1999 2000 x Scatterplot Matrices A scatterplot matrix is an array of pairwise scatter plots showing the relationship between any pair of variables in a multivariate data set To create a scatterplot matrix 1 Select the x y and z columns 2 Click the button on the Plots 2D palette Scatterplot matrix 0 2 04 06 08 10 po or o i i i i J H2 J J gt Hio e s e s J e o e F08 eee ee 4 x 0 6 ee s 4 cae ee wa eg sa 4 a6 So P Yea 0 2 4 0 0 1 04 gis s ieee Od ta os L e e a 0 64 a Dai y a L gree wt wt oe 0 2 ot 8 L ra 0 9 0 E 4 e 0 7 et t e e z gee oa P s 0 5 to es P o S A Li mr T r T T T T T T 0 0 02 04 06 08 10 12 0 3 0 5 07 0 9 151 Chapter 4 Creating Plots Contour Levels 2D contour levels plots are representations of three dimensional data Plots in a two dimensional plane Each contour line represents a level or 152 height from the corresponding three dimensional surface Filled contour plots use color between contour lines to differentiate between the levels 3D contour plots are identical to 2D contour plots except that the contour lines are drawn in three dimensional space You can create 2D and 3D contour plots from either gridded or irregular data For more information see the online help To create any of the contour l
48. 329 we calculated dissimilarities for the fuel frame data set In this example we cluster the fuel frame dissimilarities using fuzzy partitioning 1 If you have not already done so create the object fuel diss from the instructions on page 330 Agglomerative Hierarchical Clustering 2 3 4 5 Cluster Analysis Open the Fuzzy Partitioning dialog Select the Use Dissimilarity Object check box Select fuel diss as the Saved Object Click OK A summary of the clustering appears in the Report window Hierarchical algorithms proceed by combining or dividing existing groups producing a hierarchical structure that displays the order in which groups are merged or divided Agglomerative methods start with each observation in a separate group and proceed until all observations are in a single group Performing agglomerative hierarchical clustering From the main menu choose Statistics Cluster Analysis gt Agglomerative Hierarchical The Agglomerative Hierarchical Clustering dialog opens as shown in Figure 6 67 Agglomerative Hierarchical Clustering Model Resuts Plot Data Dissimilarity Measure Data Set state df ne Metric euclidean hd Variables Standardize Variables m Options Linkage Type average b Subset Rows with rs paek Save As M Omit Rows with Missing Values M Save Data r Dissimilarity Object F Use Dissimilarity Object Saved Object 7
49. 6 4 summarizes Compare Samples the results of four different studies of lung cancer patients as presented by Fleiss 1981 Each study has a certain number of patients and for each study a certain number of the patients were smokers Table 6 4 Four different studies of lung cancer patients Smokers Patients 83 86 90 93 129 136 70 82 Setting up the data We create a cancer data set containing the information in Table 6 4 1 Open an empty data set by clicking the New Data Set button E on the Standard toolbar 2 Enter the two columns of observations listed in Table 6 4 Change the column names by first double clicking on V1 and typing in smokers and then double clicking on V2 and typing in patients Press ENTER or click elsewhere in the Data window to accept the changes 3 Rename the data set by double clicking in the upper left corner of the Data window In the dialog that appears type cancer in the Name field and click OK Statistical inference For the cancer data we are interested in whether the probability of a patient being a smoker is the same in each of the four studies That is we wish to test whether each of the studies involve patients from a homogeneous population 1 Open the Proportions Test dialog 2 Type cancer in the Data Set field 247 Chapter 6 Statistics Fisher s Exact Test 248 3 Select smokers as the Success Variable and patients as the Trial Variable
50. 8 88 09 8 8 g 8 g foo 4 o 9a OF e o 20 59 8 z Lo500 08 3g 60 6 4 a ed ao 8 2000 30074 7 000 ooo o o 2507 o o o o o o 2007 ae oa Disp 80 880 o o 008 of o i of 1507 Q Peo o 0o66 88 o o o 0800600 8 pe 1004 p 0888o 28o o8 888 8 88 o e 507 M o o o 450 o m35 ogo 88 f o fo for o 8 2o l 2 8 30 J g 88 ga arn Mileage K g 8 8 Los fo o Sage Rie M i o8 Bo Bo g So 8 8 B20 5 54 o O 00 o o o o 15 o Ko o o o e o o m0 fe o oO o o O o o o0 00 oO o o fo 4 57 Sg obo S 8 o o5 o 8 5 fe 3 o amp o 6 Fuel 8 35 5 as 2200 e 8 H 2 08 og O09 o g o 2 547 E oo oo 000 ooo M6 4 Oo o OM o 00 MM oO o0 o 0000 oO O 0000 O oo 5 4 0 apo fox e 3X0 O00 CO 90000 O oa oO 00 F4 4 000 a O aoo ao oO 0000 o000 Type 3 4 o o o oO oo o o o o 2 4 a00 aD OD oO O00000 o0000 O F1 T T T T S 4 0 E Go T T T T T T E N T E E E 20002500300035004000 15 20 25 30 35 1 2 3 4 5 6 Figure 3 30 Scatterplot matrix of the fuel frame data A number of strong relationships appears Examine the plot You can immediately see a number of strong linear relationships For example as might be expected the weight of the car and its fuel consumption have a positive linear relationship as Weight increases so does Fuel The scatterplot matrix gives you a quick and concise overview of the more obvious two variable relationships in the data Adding histograms Now le s add histograms
51. All you need to do now is correctly type q and you will exit Spotfire S gt q An operator is a function with at most two arguments that can be represented by one or more special symbols appearing between the arguments For example the usual arithmetic operations of addition subtraction multiplication and division are represented by the operators and respectively Here are some simple calculations using the arithmetic operators gt 3 71 1 74 gt S i21 1 363 2 6 5 ays Ll 8 S PLUS Language Basics The exponentiation operator is which can be used as follows p23 1 8 Some operators work with only one argument and hence are called unary operators For example the subtraction operator can act as a unary operator ae Lay 3 The colon is an important operator for generating sequences of integers gt 1e10 fl 1 23 4 6 6 F 8 9 10 Table 8 2 lists the S PLUS operators for comparison and logic Comparisons are among the most common sources for logical data gt 1 10 gt 5 1 FF FFFTTTTT Comparisons and logical operations are frequently convenient for extracting subsets of data and conditionals using logical comparisons play an important role in flow of control in functions Table 8 2 Logical and comparison operators Operator Explanation Operator Explanation Equal to t Not equal to a Greater than lt Less than E Greater than or e
52. Chapter 7 Working With Objects and Databases As shown in Figure 7 1 the right pane of the Object Explorer displays the names or full pathname in the case of the working data and search path positions in the Pos column of all the attached S PLUS databases a Object Explorer ioj x Contents of SearchPath Data Obiect Pos Data Class Dimensions Graphs amp D Program Files Ins attached Reports E splus attached Scripts attached a oe J attached attached attached attached attached attached attached Figure 7 1 The search path displayed in the right pane of the Object Explorer Hint If the name or pathname of a database is truncated as in Figure 7 1 simply pause your cursor over it to display a ToolTip containing the full text The Working The most important thing to notice in Figure 7 1 is the database in Data position one Pos 1 of the search path By definition the database in position one is the working data the database in which Spotfire S automatically saves all the data and function objects you create or modify Note You can find the Windows folder named Data that corresponds to the working data by using the full pathname displayed in the search path The remaining databases in the search path in Figure 7 1 are the Spotfire S system databases To see the objects stored in a system database do the following 1 Expand the SearchPath objec
53. Covariance 2 Subset Rows with C Correlation M Omit Rows with Missing Values Save Model Obiect T Use Covariance List as Input Save As l Covariance List Include Scores al L 4 Formula Variables Figure 6 72 The Principal Components Analysis dialog Example In Factor Analysis on page 341 we performed a factor analysis for the testscores df data set In this example we perform a principal components analysis for these data 1 If you have not done so already choose File gt Load Library to load the examp1e5 library This library contains a few example data sets that are not in the main Spotfire S databases including testscores df 2 Open the Principal Components dialog 3 Type testscores df in the Data Set field 4 Select lt ALL gt in the Variables field 343 Chapter 6 Statistics MANOVA 344 5 Click on the Plot tab and check the Screeplot box 6 Click OK A summary of the principal components analysis appears in the Report window and a bar plot of eigenvalues for each principal component is displayed in a Graph Sheet Multivariate analysis of variance known as MANOVA is the extension of analysis of variance techniques to multiple responses The responses for an observation are considered as one multivariate observation rather than as a collection of univariate responses If the responses are independent then it is sensible to just perform un
54. Figure 6 40 Generalized Additive Models x Model Options Results Plot Predict m Data r Model Data Set air E Family gaussian bf Weights E Link identity ba Subset Rows with Variance Function constant 7 M Omit Rows with Missing Values Save Model Object Save As Formula Eormula ozone s radiation s temperature s wind Create Formula Cancel Apply current Help Figure 6 40 The Generalized Additive Models dialog 285 Chapter 6 Statistics Local Loess Regression 286 Example We fit an additive model for the air data 1 Open the Generalized Additive Models dialog 2 Type air in the Data Set field 3 Specify ozone s radiation s temperature s wind as the Formula 4 On the Plot page of the dialog select the Partial Residuals and Include Partial Fits check boxes This indicates that we want plots of the partial residuals and partial fits for each predictor 5 Click OK A summary of the additive model appears in the Report window A multipage Graph Sheet appears with one partial residual plot on each page Local regression is a nonparametric generalization of multivariate polynomial regression It is best thought of as a way to fit general smooth surfaces A wide variety of options are available for specifying the form of the surface Regression Fitting a local regression From the main menu choose Statistics gt Re
55. Graph button and navigate to a saved Graph Sheet to add it to the presentation e Select a Graph Sheet in the window and click the Remove Graph button to delete it from the presentation e Click the Load List button to load a previously saved presentation list e Click the Save List button to save the current presentation list e Click the Clear List button to reset the contents of the presentation list 518 Using Spotfire S With Microsoft PowerPoint To rearrange the order of your Graph Sheets use the Up and Dn buttons Note If any of your Graph Sheets contain multiple pages you can select any or all of the pages for inclusion in your PowerPoint presentation 5 Click Next E PowerPoint Presentation Wizard L lx My ie The wizard is now ready to use PowerPoint to create a presentation based on the graphs you ve chosen earlier Presentation Name Untitled Status Click on Finish button to create a PowerPoint presentation now Cancel Figure 10 22 Preparing to create the presentation Einish 519 Chapter 10 Using Spotfire S With Other Applications 520 6 Click Finish E PowerPoint Presentation Wizard Lix E Me v The wizard is now ready to use PowerPoint to create a presentation based on the graphs you ve chosen earlier Presentation Name Untitled Status Presentation creation complete sa ces E Figure
56. Object Explorer For complete details on using the Object Explorer see Chapter 7 Saving Your Data The easiest and most efficient way to save your data sets is to let in External Files Spotfire S save them for you as discussed above Allowing Spotfire S to store your data objects in the working data puts all the power of the Object Explorer at your disposal 22 Entering Editing and Saving Data However as with other standard Windows products Spotfire S does allow you to save your data sets in external sdd files by using the File menu Although we do not recommend this approach if you prefer to manage your data this way you will need to reset some option defaults as follows 1 Open the General Settings dialog to the General page as described above 2 In the Prompts Closing Documents group do the following Select the Prompt to Save Data Files check box e Inthe Remove Data from Database dropdown list select Always Remove Data 3 Click OK Setting these preferences causes Spotfire S to prompt you with the following message whenever you close a Data window displaying a new or modified data set S PLUS x AN Save changes to SDF1 in a file Clicking Yes in the dialog opens the Save Data Set As dialog To save your data in a file simply name the data set navigate to the desired folder and click Save 23 Chapter 2 Working With Data VIEWING AND FORMATTING DATA Displaying a As mentione
57. Panel Rotation button on the Plots 3D palette The resulting graph is displayed in Figure 3 46 Figure 3 46 Multiple panel 3D scatter plot of the sliced ball data The multiple panel 3D scatter plot is particularly clear in uncovering the slice in the sliced ball data Viewing the data in 3D allows us to look at angles other than those displayed in the bivariate plots This helps us to discover higher dimensional structure 109 Chapter 3 Exploring Data Another way to examine these data is using brush and spin This is discussed in Dynamic Graphics on page 113 Surface Plots Surface plots are three dimensional surfaces reflecting the value of a variable over different values of two other variables Data used in a surface plot are often over a grid of values If the data available are not on a grid interpolation is used to fit a surface over a grid which is then plotted Example surface data The sample data set exsurf contains two columns representing a grid of values and a third column that is the value of a function over the grid The help file for exsurf describes the equation used Creating surface plots First we will create a surface plot of these data 1 4 110 Use the Select Data dialog to display the exsurf data in a Data window Select all three columns in the Data window Click the Data Grid Surface button g on the Plots 3D palette The resulting graph is displayed in Figure 3 47
58. Plots Using Spotfire S With SPSS Creating a graph Spotfire S graphs created with the SPSS add in are placed in an output document You have the choice of creating a new output document or using an existing one To create a new Spotfire S graph with the currently selected variables in the data editor do the following 1 2 Select variables in the data editor you want to graph Click the Create Graph button on the Spotfire S toolbar or choose Spotfire S gt Create Graph from the SPSS main menu Follow the instructions in the Create Spotfire S Graph wizard to create the graph Modifying a graph layout To modify the layout properties of the currently selected Spotfire S graph do the following l Select a Spotfire S graph in your output document by clicking once on it If you double click on a graph you will activate it and start editing in place Click the Modify Graph Layout button on the Spotfire S toolbar or choose Spotfire S gt Modify Graph Layout from the SPSS main menu A Spotfire S Graph Sheet dialog opens in SPSS allowing you to modify the layout properties of this graph Modifying a plot To modify the properties of a plot in the currently selected Spotfire S graph do the following 1 Select a Spotfire S graph in your output document by clicking once on it Click the Modify Plots button on the Spotfire S toolbar or choose Spotfire S Modify Plots from the SPSS main men
59. Proportions dialog Type batch qcc in the Data Set field Select NumBad as the Variable Select NumSample as the Size Column Select Number np as the Chart Type Click OK A Shewhart chart of the NumBad data with group size indicated by NumSample appears in a Graph Sheet RESAMPLE Bootstrap Inference Resample In statistical analysis the researcher is usually interested in obtaining not only a point estimate of a statistic but also the variation in the point estimate as well as confidence intervals for the true value of the parameter For example a researcher may calculate not only a sample mean but also the standard error of the mean and a confidence interval for the mean The traditional methods for calculating standard errors and confidence intervals generally rely upon a statistic or some known transformation of it being asymptotically normally distributed If this normality assumption does not hold the traditional methods may be inaccurate Resampling techniques such as the bootstrap and jackknife provide estimates of the standard error confidence intervals and distributions for any statistic To use these procedures you must supply the name of the data set under examination and an S PLUS function or expression that calculates the statistic of interest In the bootstrap a specified number of new samples are drawn by sampling with replacement from the data set of interest The statistic of interest is calculated
60. Range field select a different range and click OK in the dialog prompt Note The wizard converts column labels to legal Spotfire S variable names Row labels are used for annotations in Spotfire S plots 5 Click Next 6 The second page of the wizard gives you an idea of what the S PLUS data frame will look like see Figure 10 5 Excel to S PLUS Link Wizard x Enter the Data types The wizard has determined the following to be the S PLUS data types To change a data type double click it Po Tris Tit o o l Data Types gt Decimal Decimal 12 97 12 97 12 98 12 98 wil Finish Cancel Help Figure 10 5 Selecting a different column type The wizard attempts to guess what the column types in the data frame should be If a guess is incorrect you can change it by clicking the cell containing the incorrect column type and selecting a different type from the dropdown list that appears 488 Using Spotfire S With Microsoft Excel Note The wizard examines the first cell in a column to determine that column s data type If the first cell in the column is N A or is empty the wizard then loops over the rest of the column looking for a real value If such a value is found its type becomes the data type for the column If only N A or empty cells are found the wizard sets the column type to character Table 10 1 below lists the Excel data types alo
61. Removing the SPSS Add In If you choose not to install the SPSS add in during the installation of Spotfire S you can install it at any later time by running Setup choosing the Custom install option and selecting SPSS Add in from the list of components to install To remove the SPSS add in do the following 1 Run Spotfire S Setup choosing the Modify option 2 Select SPSS Add in from the list of components 503 Chapter 10 Using Spotfire S With Other Applications 3 Follow the on screen instructions Note If you install the SPSS add in during the installation of Spotfire S and later remove Spotfire S the SPSS add in is removed automatically Using the SPSS Add In 504 With the SPSS add in installed whenever you have the SPSS data editor open a Spotfire S menu and toolbar appear as shown in Figure 10 12 The same menu and toolbar are also available whenever you have an output document open Untitled SPSS Data Editor oj x File Edit View Data Transform Analyze Graphs Utilities S PLUS Window Help eof S 2 Elk al safes S Sess i Modify S PLUS Plots About S PLUS Add in Variable View SPSS Processor is ready Figure 10 12 The Spotfire S menu and toolbar for the SPSS add in The menu and toolbar give you the following options e Create Spotfire S Graph e Modify Graph Layout e Modify Spotfire S
62. Script Text Number of Inputs fl 4 Number of Outputs T omes Figure 10 19 The Spotfire S Script Setup Wizard 4 Type S PLUS language commands in the S PLUS Script Text field For example you might enter the following series of commands Imobj lt Im Mileage Weight data fuel frame out0 lt Imobj fitted outl lt Imobj resid Note By default component output variables have the names out0 out1 etc To change the default output variable names see Modifying a component on page 515 5 In the Number of Inputs field specify the number of values 514 to be passed to Spotfire S from Mathcad For the example script shown in Step 4 the number of inputs is 0 In the Number of Outputs field specify the number of values to be passed from Spotfire S to Mathcad For the example script shown in Step 4 the number of outputs is 2 Click Finish to insert the component The placeholders for input appear at the bottom of the component and the placeholders for output appear at the top left Using Spotfire S With MathSoft Mathcad 8 Enter Mathcad variable names for the inputs and or outputs if any In our example you might use fitted and resid as the output variable names The script component appears in your worksheet as shown below fitted resid a S PLUS Script Imobj lt Im Mileage VVeight data fuel frame out lt Imobjffitted outi lt Imobj resid 9
63. Set r Confidence Interval fexmichel fd Confidence Level 0 95 speed x r Results z Save As Print Results Variable Hypotheses Mean Under Null Hypothesis 990 Alternative Hypothesis two sided x Cancel Appt current Figure 6 8 The One sample t Test dialog Example In 1876 the French physicist Cornu reported a value of 299 990 km sec for c the speed of light In 1879 the American physicist A A Michelson carried out several experiments to verify and improve Cornu s value Michelson obtained the following 20 measurements of the speed of light 850 740 900 1000 980 930 1070 930 850 650 950 980 980 880 760 810 1000 1000 960 960 To obtain Michelson s actual measurements add 299 000 km sec to each of the above values In Chapter 3 Exploring Data we created an exmichel data set containing the Michelson data The 20 observations can be thought of as observed values of 20 random variables with a common but unknown mean value location U If the experimental setup for measuring the speed of light is free of bias then it is reasonable to assume that u is the true speed of light In evaluating these data we seek answers to at least four questions 1 What is the speed of light u 2 Has the speed of light changed relative to our best previous value Uy 299 990 km sec 3 What is the uncertainty associated with our answers to 1 and 2 4 What is
64. Sheet fast x Cancel Figure 4 2 The Insert Graph dialog To open the Insert Graph dialog do one of the following e From the main menu choose Graph and select a graph type 2D Plot 3D Plot or Multipanel Graph e With a Data window open choose Insert gt Graph from the main menu Double clicking an existing plot or creating a plot through the Insert Graph dialog without first selecting your data opens a plot properties dialog specific to a particular group of plots You can use these dialogs to create or modify your plots For a complete discussion of the plot properties dialogs see the online help Because some plot types require data to be structured in a particular way in the sections that follow a sample data set is shown for each of the various plot types For many plots however the data can be formatted in a number of different ways For example data for creating grouped box plots may be structured in one of two ways as long form stacked data or as multiple y form data Whichever form your data are in the same grouped box plot is produced Introduction In long form stacked data see Figure 4 3 the x column is a column of integers that assigns each y value to a group and determines the placement of the boxes along the x axis When your data are structured in this way you simply select the x and y columns to create the grouped box plot H grouped box long _ oy x a JE
65. Small 30 4 7 v Sporty Van A v jd oy v v 25 7 3 o o o a o o o om f o oo o o 20 4 eave A v eo ee a 15 T T T T T 1700 2200 2700 3200 3700 4200 Weight Figure 3 29 Color plot of Mileage by Weight with color and symbol style varying by Type for the fuel frame data Notice that over all cars mileage tends to decrease as automobile weight increases However the relationship varies between car types For example Sporty cars have a wide range of weights and mileages Scatterplot Next we will create a scatterplot matrix viewing the relationships Matrices between all the variables in the data set simultaneously Generating a scatterplot matrix 1 Since we want to plot all the variables in the data set we select all the variables by clicking in the upper left hand corner of the Data window every column will turn black Note The order in which you select the data columns determines the order in which they appear on the scatterplot matrix In the above case we did not care about the order but if we did we could CTRL click the columns and select them in the desired order 89 Chapter 3 Exploring Data 2 Click the Scatter Matrix button on the Plots 2D palette The resulting scatterplot matrix is shown in Figure 3 30 50 100 150 200 250 300 i 25 36 45 5 5 ioe oo J M4 soo 8 Cg o e T ae a a oo 000 O00 6 0 o o oge 5 ooge o o O ye J Weight o
66. Snap to Grid group Grids per Inch Specify the number of invisible grid lines to use for the Snap to Grid option The default is 12 grid lines per inch or 5 grid lines per centimeter When Snap to Grid is enabled objects will snap to the closest intersection of these invisible horizontal and vertical grid lines Traditional Graphics group Auto Pages Select Within Expression to have pages automatically added by default when a series of plots is created within an S PLUS function Note that you can override this setting in the Page Creation field on the Options page of the Graph Sheet dialog Create Editable Graphics When selected plots created within S PLUS functions are translated into editable graphical objects when placed in a Graph Sheet Note that with this option on creation of graphs can be very slow With this option off a composite graph object is placed in the Graph Sheet but you can convert it into an editable graphical object at any time by right clicking and selecting Convert to Objects from the shortcut menu Suppress Warnings When selected all new graphsheets created by default set par err 1 which suppresses warnings such as points out of bounds messages Use Printer Page Defaults When selected printer page defaults are used Statistics Dialogs Graphics group Create New Graph Sheet When selected a new Graph Sheet is created when a plot is created using one of the statistics dialogs 539 Cha
67. Suppose you have a data set based on multiple variables and you want to see how plots of two variables change with variations in a third conditioning 96 The Ethanol Data Visualizing Multidimensional Data variable By using Trellis graphics you can view your data in a series of panels where each panel contains a subset of the original data divided into intervals of the conditioning variable A wide variety of graphs can be conditioned using Trellis graphics The sample data set ethanol contains measurements of pollutants in automobile exhaust Data were collected to analyze oxides of nitrogen in the exhaust An experiment was done on a one cylinder engine fueled by ethanol Two engine factors were studied the equivalence ratio E a measure of the richness of the air and fuel mixture and the compression ratio to which the engine is set C There were 88 runs of the experiment Here we will examine the relationship between the oxides of nitrogen NOx and the compression ratio Creating a loess plot 1 Use the Select Data dialog to open the ethanol data ina Data window 2 Select the C column Press the CTRL key and select the NOx column 3 Click the Loess button on the Plots 2D palette to create a scatter plot with a loess smooth as shown in Figure 3 37 ow o ow o o 00 Figure 3 37 Compression ratio versus nitrogen oxide in the ethanol data set default smoothing 97 Chapter 3 Expl
68. T 600 T T T T T 1000 1100 1200 2 i 0 i 2 Normal Distribution Figure 6 9 Exploratory data analysis plots for the Michelson data Continued 216 We want to evaluate the shape of the distribution to see if our data are normally distributed These plots reveal a distinctly skewed distribution toward the left that is toward smaller values The distribution is thus not normal and probably not even nearly normal We should therefore not use Student s t test for our statistical inference since it requires normality for small samples The solid horizontal line in the box plot is located at the median of the data and the upper and lower ends of the box are located at the upper and lower quartiles of the data respectively To obtain precise values for the median and quartiles use the Summary Statistics dialog 1 2 3 Open the Summary Statistics dialog Enter exmichel as the Data Set Click on the Statistics tab and deselect all options except Mean Minimum First Quartile Median Third Quartile and Maximum Click OK The output appears in the Report window xxx Summary Statistics for data in exmichel speed Min 650 000 Ist Qu 850 000 Mean 909 000 Median 940 000 Compare Samples 3rd Qu 980 000 Max 1070 000 The summary shows from top to bottom the smallest observation the first quartile the mean the median the third quartile and the largest observation From this summary you can comp
69. T T 0 To create a plot with both vertical and horizontal error bars ffi error bar both Of x 1 Select the x y z and w columns to create an error bar plot using the z data to display horizontal error bars and the w data to display vertical error bars 2 Click the Be button on the Plots 2D palette Error bar both plot Ez 150 1004 LL e aT a gt y H H P 100 4 o 10 20 30 150 T T 40 50 x Vector Plots A vector plot displays the direction and velocity of flow at positions in the x y plane You can also use vector plots to draw any group of arrows using the data in a data set 149 Chapter 4 Creating Plots To create a vector plot Area Charts 1 Select the x y z and values magnitude columns angle W values Click the ee button on the Plots 2D p alette Vector plot be gt 0 97 A ee A A s 1 1 5 An area chart is useful for showing how each series in a set of data affects the whole over time To create an area chart 1 2 Click the M button on the Plots 2D palette 150 Select the x column and a single y column to draw an x y curve and fill the area beneath the curve Select x and multiple y columns to draw a curve for each set of values and fill the area beneath each curve Plotting Multidimensional Data Area chart Eal
70. Table 6 7 For more information on this dialog see Orthogonal Array on page 265 1 Select Statistics Design Orthogonal Array Design from the main menu In the Levels field type 2 2 2 This step specifies 2 levels each for the first three columns in our data set We now need to name the levels in our three factor columns In the Factor Names field type the following expression c Yes No c Case Control c Yes No Type mantel trial in the Save In field and click OK In the fourth column of the mantel trial Data window enter the eight Number values from Table 6 7 Rename the columns Smoker Group Passive and Number so that they match the names in the table Chi Square Test Compare Samples The mantel trial data set has eight rows representing the eight possible combinations of three factors with two levels each However the Mantel Haenszel Chi Square Test dialog requires data to be in its raw form and does not accept data in a contingency table We can use the Subset dialog to recreate the raw data as follows 1 From the main menu choose Data gt Subset 2 Type mantel trial in the Data Set field 3 Type rep 1 8 Number in the Subset Rows with field This replicates each of the integers 1 to 8 as many times as indicated by the corresponding count in the Number column 4 Type mantel raw in the Save In field and click OK The first three columns of the mantel raw data set represent the u
71. The supersmoother is a highly automated variable span smoother It obtains fitted values by taking a weighted combination of smoothers with varying bandwidths The smoothing parameter for supersmoothers is called the span The span is a number between 0 and 1 representing the percentage of points that should be included in the fit for a particular smoothing window Smaller values result in less smoothing and very small values close to 0 are not recommended If the span is not specified an appropriate value is computed using crossvalidation For small samples n lt 50 or if there are substantial serial correlations between observations close in x value a prespecified fixed span smoother should be used 357 Chapter 6 Statistics Examples 358 The air data set contains 111 observations rows and 4 variables columns It is taken from an environmental study that measured the four variables ozone solar radiation temperature and wind speed for 111 consecutive days We create smooth plots of ozone versus radiation 1 Choose Statistics gt Smoothing Kernel Smoother Type air as the Data Set Select radiation as the x Columns ozone as the y Columns and then click OK A Graph Sheet is created containing a plot of ozone versus radiation with a kernel smooth Choose Statistics gt Smoothing gt Loess Smoother Type air as the Data Set Select radiation as the x Columns ozone as the y Columns and then click OK A Gra
72. To view the contents of the newly created object just type its name gt newvec 1 3416 This section introduces basic typing syntax and conventions in Spotfire S S PLUS ignores most spaces For example 2 OF i 1 10 However do not put spaces in the middle of numbers or names Also you should always put spaces around the two character assignment operator lt otherwise you may perform a comparison instead of an assignment Unlike Windows and DOS S PLUS is case sensitive All S PLUS objects arguments names etc are case sensitive You will get an error message if you do not type the name of an S PLUS object exactly being careful to match all uppercase and lowercase letters For example gt newvec 1 34 1 6 417 Chapter 8 Using the Commands Window Special Characters Continuation 418 gt NEWvec Problem Object NEWvec not found Use traceback to see the call stack Dumped Table 8 1 lists some special characters for carriage control obtaining characters that are not represented on the keyboard or delimiting character strings Table 8 1 Special characters Character Description t Tab n New line double quotes V apostrophe backslash UHH ASCII character as an octal number that is is in the range 0 7 Any ASCII character may be represented as a three digit octal number The character can be specified in S PLUS by preceding the o
73. Z dy 100 JE About 5 PLUS Add In pil Sheet1 Sheet2 f Sheet3 Figure 10 11 The Spotfire S menu and toolbar for the Excel add in Creating a graph To create a new Spotfire S graph with the currently selected data in the current worksheet do the following 1 Select blocks of data in the current worksheet you want to graph 2 Click the Create Graph button on the Spotfire S toolbar or choose Spotfire S gt Create Graph from the Excel main menu 498 3 Using Spotfire S With Microsoft Excel Follow the instructions in the Create Spotfire S Graph wizard to create the graph Modifying a graph layout To modify the layout properties of the currently selected Spotfire S graph do the following 1 Select a Spotfire S graph in your worksheet by clicking once on it If you double click on a graph you will activate it and start editing in place Click the Modify Graph Layout button on the Spotfire S toolbar or choose Spotfire S gt Modify Graph Layout from the Excel main menu A Spotfire S Graph Sheet dialog opens in Excel allowing you to modify the layout properties of this graph Modifying a plot To modify the properties of a plot in the currently selected Spotfire S graph do the following 1 Select a Spotfire S graph in your worksheet by clicking once on it Click the Modify Plots button on the Spotfire S toolbar or choos
74. and the p value is 0 0027 which is highly significant Clearly Michelson s average value of 299 909 km sec for the speed of light is significantly different from Cornu s value of 299 990 km sec Spotfire S returns other useful information besides the p value including the statistic value the degrees of freedom the sample mean and the confidence interval The Wilcoxon signed rank test is used to test whether the median for a variable has a particular value Unlike the one sample test it does not assume that the observations come from a Gaussian normal distribution Performing a one sample Wilcoxon signed rank test From the main menu choose Statistics gt Compare Samples One Sample gt Wilcoxon Signed Rank Test The One sample Wilcoxon Test dialog opens as shown in Figure 6 10 One sample Wilcoxon Test 5 x m Data r Options Data Set fexmichel X V Use Exact Distribution Variable speed X Continuity Correction Hypotheses Results Mean Under Null Hypothesis Save As 990 MV Print Results Alternative Hypothesis j two sided hed Cancel Apply current Help Figure 6 10 The One sample Wilcoxon Test dialog Example In One Sample t Test on page 213 we performed a test on the Michelson data The test concludes that Michelson s average value for the speed of light 299 909 km sec is significantly different from Cornu s value of 299 990 km sec However we have n
75. are 6 x 10 x 2 120 observations The barley experiment was run in the 1930s The data first appeared in a 1934 report published by the experimenters Since then the data have been analyzed and re analyzed R A Fisher presented the data for five of the sites in his classic book The Design of Experiments Publication in the book made the data famous and many others subsequently analyzed them usually to illustrate a new statistical method In the early 1990s Bill Cleveland of AT amp T now Lucent Technologies analyzed the data again using Trellis graphics The result was a big surprise Through 50 years and many analyses an important feature of the data had gone undetected The basic analysis is repeated here 101 Chapter 3 Exploring Data Trellis Graphs 102 We are interested in exploring how barley yield varies based on combinations of the other variables Trellis is particularly useful for displaying the effects of covariates and their interactions We will use a color plot with conditioning to display the fourth and fifth variables 1 Use the Select Data dialog to display the barley data in a Data window Select the yield column Then press the CTRL key and select variety year and site The order of the variables selected determines how they will be used in the color plot The first variable yield will be used as x the second variety as y and the third year to determine the color of the symbols The last v
76. assessing the fit is to look at the model graphically using an assortment of plots that taken together reveal the strengths and weaknesses of the model For example a plot of the response against the fitted values gives a good idea of how well the model has captured the broad outlines of the data Examining a plot of the residuals against the fitted values often reveals unexplained structure left in the residuals which should appear as nothing but noise in a strong model The plotting options for the Linear Regression dialog provide these two plots along with the following useful plots e Square root of absolute residuals against fitted values This plot is useful in identifying outliers and visualizing structure in the residuals e Normal quantile plot of residuals This plot provides a visual test of the assumption that the model s errors are normally distributed If the ordered residuals cluster along the superimposed quantile quantile line you have strong evidence that the errors are indeed normal Regression e Residual fit spread plot or r f plot This plot compares the spread of the fitted values with the spread of the residuals Since the model is an attempt to explain the variation in the data you hope that the spread in the fitted values is much greater than that in the residuals e Cook s distance plot Cook s distance is a measure of the influence of individual observations on the regression coefficients e Pa
77. ball data 105 Small Icons button 387 Smith plots 156 circle 157 impedance 156 reflection 156 smoothers 69 355 smoothing plots 136 Sort Ascending button 48 Sort Columns dialog 48 Sort Descending button 48 sorting customized 47 48 quick 47 48 speed of light data 55 214 exploratory analysis of 216 spline smoothers 73 Split Data by Group dialog 51 S PLUS language basics of 424 data objects in 424 S PLUS syntax basics of 417 case sensitivity in 417 continuation lines in 418 formulae in 455 565 Index 566 spaces using 417 SPSS add in creating graphs 505 disabling during Setup 503 error handling 508 installing automatically 503 with Setup 503 menu for 504 modifying layout properties of graphs 505 modifying plot properties of graphs 505 overview of 503 removing automatically 504 with Setup 503 selecting data 506 for conditioning graphs 507 SPSS version required for 503 toolbar for 504 SQL Server files hints for importing and exporting 190 Stack Columns dialog 50 237 stacked bar plots 142 starting Spotfire S customizing 550 statistical modeling 454 statistical techniques analysis of variance random effects 301 cluster analysis agglomerative hierarchical 335 compute dissimilarities 329 divisive hierarchical 336 fuzzy analysis 333 k means 330 monothetic 338 partitioning around medoids 331 comparing samples one sample chi square goodness of fit test 222 Kolmogorov Smirnov goodness of fit test
78. columns Depending upon your choice of Keep selected or Drop selected specify either the columns you want to export or the columns you do not want to export respectively When Preview column names is selected this field becomes a dropdown list from which you can make your selections When Preview column names is not selected you must type a column list of names see Creating a Column List on page 32 in this field 185 Chapter 5 Importing and Exporting Data 186 Keep selected When selected Spotfire S exports only the columns you specify in the Select columns dropdown list Drop selected When selected Spotfire S exports all columns except those you specify in the Select columns dropdown list Filter page Filter rows group Preview row names When selected Spotfire S populates the Select Rows dropdown list with the row names of the data set you are exporting Select Rows Specify the rows you want to export in this field When Preview row names is selected this field becomes a dropdown list from which you can make your selections When Preview row names is not selected you must type a row list of numbers see Creating a Row List on page 36 in this field Export Rownames When selected Spotfire S includes the row names of the data set as the first column in the ODBC table Filter Expressions FILTER EXPRESSIONS Variable Expressions By including a logical expression in the Filter expression field of the import expo
79. creating and modifying 5 PLUS graphs Figure 10 10 The Add Ins dialog in Excel with the Spotfire S Add In checked Removing the If you choose to remove the Excel add in you can do so either by Excel Add In running Spotfire S Setup or by removing the add in from within Excel To remove the Excel add in using Spotfire S Setup do the following 1 Run Spotfire S Setup choosing the Modify option 2 Select Excel Add in from the list of components 3 Follow the on screen instructions Note If you install the Excel add in during the installation of Spotfire S and later remove Spotfire S the Excel add in is removed automatically To disable the Excel add in from within Excel do the following 1 Start Excel Create a new worksheet if one does not already exist 2 3 From the Tools menu choose Add Ins 4 In the Add Ins dialog clear the Spotfire S Add In check box see Figure 10 10 and click OK 497 Chapter 10 Using Spotfire S With Other Applications Using the Excel With the Excel add in installed whenever you have a worksheet in Add In focus in Excel a Spotfire S menu and toolbar appear as shown in Figure 10 11 The menu and toolbar give you the following options e Create Graph e Modify Graph Layout e Modify Plots X Microsoft Excel Ele Edit view Insert Format Tools Data 5 PLUS Window Help OSM SRY amp BOS Recreate cah arial rio Ee AE N Plots 4
80. cumulative probabilities 367 current data set 24 curve fitting plots 134 D data built in sets of 441 copying 37 deleting 44 all the data in a Data window 44 in a block of cells 44 555 Index 556 in a cell 44 in a column 45 in a row 46 editing 19 438 440 using Edit data function 440 entering from the keyboard 16 438 using Data menu options 16 using the Commands window 16 using the scan function 438 exporting to data files 168 174 to ODBC tables 183 extracting subsets of 442 importing 16 438 from data files 168 from ODBC tables 181 with importData function 438 long form stacked for box plots 121 manipulation tools for 49 moving 37 multidimensional 87 multiple y form for box plots 121 reading from a file 438 439 sorting customized 47 48 quick 47 48 types of 34 using read table function with 440 databases 14 381 attaching 408 creating 408 detaching 409 system 410 data frames engine object type of 379 Data Set field 201 data sets creating 16 17 current 24 displaying concurrent views of 25 entering data 18 names valid 17 renaming 17 restoring to initial state 20 to previous state 20 saving 23 DataTips for column description 32 for column type 35 Data window navigating keyboard shortcuts for 25 mouse shortcuts for 25 overview of 14 selecting data in cells a block of 29 all 29 a single 29 extending a selection of 29 columns a single 29 contiguous 29 in a special order 29 no
81. diets A B C and D respectively The numbers of observations are J 4 Jpg 6 Jc 6 and Jp 8 You may carry out the analysis of variance using the One way Analysis of Variance dialog 1 Open the One way Analysis of Variance dialog 2 Type blood in the Data Set field 3 Select time as the Variable and diet as the Grouping Variable 4 To generate multiple comparisons in a later section we save the results by typing anova blood in the Save As field 5 Click OK to perform the ANOVA The results are displayed in the Report window xxx One Way ANOVA for data in time by diet Calis aov formula structure Data time diet class formula data blood Terms diet Residuals Sum of Squares 228 112 Deg of Freedom 3 20 Residual standard error 2 366432 Estimated effects may be unbalanced Df Sum of Sq Mean Sq F Value Pr F diet 3 228 76 0 13 57143 0 00004658471 Residuals 20 112 5 6 The fvalue is equal to 0 000047 which is highly significant we therefore conclude that diet does affect blood coagulation times 239 Chapter 6 Statistics Kruskal Wallis Rank Sum Test 240 The Kruskal Wallis rank test is a nonparametric alternative to a one way analysis of variance The null hypothesis is that the true location parameter for y is the same in each of the groups The alternative hypothesis is that y is different in at least one of the groups Unlike one way ANOVA this test does not require norma
82. factorial designs constructed such that inferences may be made regarding main first order effects Level combinations necessary for estimating second and higher order effects are excluded in the interest of requiring as few measurements as possible Generating an orthogonal array design From the main menu choose Statistics Design gt Orthogonal Array The Orthogonal Array Design dialog opens as shown in Figure 6 30 Orthogonal Array Design loli Design Structure r Randomization Levels fa 2 I Randomize Row Order Minimal Residual DF Restricted Factors r Results Names Save In Factor Names ee a Canc ae Mi current Figure 6 30 The Orthogonal Array Design dialog Example We create a design with 3 levels of the first variable and two levels of the second 1 Open the Orthogonal Array Design dialog 2 Specify 3 2 as the Levels 265 Chapter 6 Statistics Design Plot 266 3 Click OK A data set containing the design is created and displayed in a Data window In this simple example the orthogonal array design is equivalent to the factorial design created in the previous section A design plot displays a function of a variable for each level of one or more corresponding factors The default function is the mean Creating a design plot From the main menu choose Statistics gt Design gt Design Plot The Design Plot dialog opens as shown in Figure 6 31 Design Plot M
83. for each set of data and the resulting set of estimates is used as an empirical distribution for the statistic Performing bootstrap inference From the main menu choose Statistics gt Resample gt Bootstrap The Bootstrap Inference dialog opens as shown in Figure 6 77 351 Chapter 6 Statistics 352 Bootstrap Inference 1O x Model Options Results Plot Jack After Boot Data r Save Model Object Data Set fiuel frame X Save As I Save Resampling Indices Statistic to Estimate Expression Jmean Mileage Cancel Appt current Figure 6 77 The Bootstrap Inference dialog Example The data set fuel frame is taken from the April 1990 issue of Consumer Reports It contains 60 observations rows and 5 variables columns Observations of weight engine displacement mileage type and fuel were taken for each of sixty cars We obtain bootstrap estimates of mean and variation for the mean of the Mileage variable 1 2 3 4 6 Open the Bootstrap Inference dialog Type fuel frame in the Data Set field Type mean Mileage in the Expression field On the Options page type 250 in the Number of Resamples field to perform fewer than the default number of resamples This speeds up the computations required for this example Click on the Plot page and notice that the Distribution of Replicates plot is selected by default Click OK A bootstrap summary appears in th
84. formula Formulas can be saved as separate S PLUS objects and supplied as arguments to the modeling functions A partial listing of S PLUS modeling functions is given in Table 8 8 Table 8 8 S PLUS modeling functions Function Description aov manova Analysis of variance models Im Linear model regression gim Generalized linear model including logistic and Poisson regression gam Generalized additive model loess Local regression model tree Classification and regression tree models nis ms Nonlinear models Ime nime Mixed effects models factanal Factor analysis princomp Principal components analysis pama n a l Cluster analysis daisy clara Statistics In a formula you specify the response variable first followed by a tilde and the terms to be included in the model Variables in formulas can be any expression that evaluates to a numeric vector a factor or ordered factor or a matrix Table 8 9 gives a summary of the formula syntax Table 8 9 Summary of the S PLUS formula syntax Expression Meaning A B A is modeled as B B C Include both B and C in the model B C Include all of B except what is in C in the model B C The interaction between B and C B C Include B C and their interaction in the model C in B C is nested within B B C Include B and C in B in the model The following sample Spotfire S session ill
85. function is the traditional Spotfire S initialization tool The S init file has the advantage of being a text file that can easily be edited outside of Spotfire S The S_FIRST variable is a convenient way to override First for a specific Spotfire S session Here is a sample First function that starts the default graphics device gt First lt function graphsheet After creating a First function you should always test it immediately to make sure it works Otherwise Spotfire S will not execute it in subsequent sessions Here is a sample S init file that sets the output width for the session as well as the default displayed precision options width 55 digits 4 You can create an S init file in any folder in which you want to start Spotfire S Spotfire S checks the current folder to see whether this initialization file exists and evaluates it if it finds it To store a sequence of commands in the S_FIRST variable use the following syntax on the Spotfire S command line SPLUS EXE S_FIRST Spotfire S expression For example the following command tells Spotfire S to start the default graphics device SPLUS EXE S_FIRST graphsheet To avoid misinterpretation by the command line parser it is safest to surround complex S PLUS expressions with a single or double quote whichever you do not use in your S PLUS expression For example the following command starts Spotfire S and modifies several options 551 C
86. group set Order Type to Median of X In the Layout group type 1 for of Columns Click the Position Size tab of the dialog Type 0 5 for Aspect Ratio and then click OK 103 Chapter 3 Exploring Data Adding a legend Now add some final touches to your plot Add a legend and vary the symbol styles as well as the symbol colors for the two years Click the Auto Legend button on the Graph toolbar A legend is automatically created and placed on your graph You can position the legend by selecting it click just inside the border of the legend and dragging it to the desired location Varying the symbols 1 104 Right click any symbol on the plot to display its shortcut menu and select Vary Symbols On the Vary Symbols page set Vary Style By to z Column so that both the color and symbol styles vary by year You can change the defaults for symbol styles and colors see Chapter 11 Customizing Your Spotfire S Session for more information Click OK Notice that the legend has been updated to reflect the new symbol styles Your final Trellis plot should look similar to the one shown in Figure 3 42 Trebi Glabron No 475 Svansota 1932 a 1931 Trebi Glabron o 475 Svansota Trebi Glabron N 5 o 47 8 Svansota Svansota yield Figure 3 42 Formatted Trellis plot of barley yields for 1931 and 1932 Three Dimensional Plots 3D Scatter Plots Visualizing Multidimensional Data Now ex
87. if H Ho but we make no distributional assumptions about our populations Two forms of the Wilcoxon test are supported the signed rank test and the rank sum test e Kolmogorov Smirnov goodness of fit test a test to determine whether two samples come from the same distribution The two sample t test is used to test whether two samples come from distributions with the same means This test handles both paired and independent samples The samples are assumed to come from Gaussian normal distributions If this is not the case then a nonparametric test such as the Wilcoxon rank sum test may be a more appropriate test of location Compare Samples Performing a two sample t test From the main menu choose Statistics gt Compare Samples gt Two Samples gt t Test The Two sample t Test dialog opens as shown in Figure 6 13 Two sample t Test o x m Data m Hypotheses y Data Set weight gain lad Mean Under Null Hypothesis Variable 1 gain high E 0 Variable 2 fosniow z Alternative Hypothesis two sided b I Variable 2 is a Grouping Variable Confidence Interval Test 3 Confidence Level 10 95 Type of t Test C Pairedt Two sample t SEL Save As IV Assume Equal Variances I Print Results Cancel Apply current Figure 6 13 The Two sample t Test dialog Example Suppose you are a nutritionist interested in the relative merits of two diets one featuring high protein and the other f
88. in the Import From ODBC dialog For more information on adding or modifying data sources see the online help Table Name Once you select a data source the dropdown list in this field is populated with table names Select the table you want to import By default the first table in the data source is selected SQL Query Specify any legal Structured Query Language SQL statement in this field When you select a table to import a default SQL query is generated for importing all the data in the table ODBC page To group Data frame By default Spotfire S creates a new data set with the same name as the table being imported To override the default name type a different name in this field To import the data into an existing data set type or select its name Start col By default Spotfire S appends the imported data to the end of the data set specified in the Data frame field To specify a different starting column type or select its name in this field Insert at start col When selected Spotfire S inserts the imported data starting at the column specified in the Start col field Overwrite target When selected Spotfire S overwrites any existing data when importing Note After you make your selections on the ODBC page of the Import From ODBC dialog Spotfire S has the basic information necessary to import an ODBC table For greater control over your importing parameters use the Filter page discussed below
89. in the case of equivalent data the column next selected determines the ranking and so on e Specify a different data set or column s in which to store the sort results if you want to avoid overwriting your original data To perform a customized sort do the following 1 From the main menu choose Data Restructure gt Sort The Sort Columns dialog opens as shown in Figure 2 31 Sort Columns ol x From m To Data Set ethanol X Data Set ethanol X Columns ar E Columns lt ALL gt kd Sort By Columns N Ox C X I Descending Cancel Appt qel current Help Figure 2 31 The Sort Columns dialog 2 In the From group select the columns you want to sort from the Columns dropdown list To sort all the columns in the data set select the special key word lt ALL gt 48 Manipulating Data 3 Select one or more columns to sort by from the Sort By Columns dropdown list To sort by more than one column CTRL click to select the columns in the desired ranking order 4 In the To group specify a target destination for the sort results To sort in place select the same data set and columns from the Data Set and Columns dropdown lists respectively as you selected in the corresponding From group fields Caution Mismatched columns may result when sorting in place with fewer than lt ALL gt columns selected in the Columns fields To send the sort results to a different data s
90. in the first row and column of the worksheet then all you need to specify is the file name and file type If a row contains column names specify the number of that row in the Col names row field it does not have to be the first row You can select a rectangular subset of the worksheet by specifying starting and ending columns and rows Lotus style column names for example A AB can be used to specify the starting and ending columns The row specified as the starting row is always read first to determine the data types of the columns Therefore there cannot be any blank cells in this row In other rows blank cells are filled with missing values All imports from and exports to Access files are done using ODBC so the various ODBC components must be properly installed If your Excel worksheet contains numeric data only in a rectangular block starting in the first row and column of the worksheet then all you need to specify is the file name and file type If a row contains column names specify the number of that row in the Col names row field it does not have to be the first row You can select a rectangular subset of the worksheet by specifying starting and ending columns and rows Excel style column names for example A AB can be used to specify the starting and ending columns Note that when importing from or exporting to Excel Spotfire S truncates column names to 256 characters All imports from and exports to Oracle file
91. in the middle The median is represented by a horizontal line segment within the rectangle and the top and bottom areas of the rectangle portray the upper and lower quartiles There are two types of box plots single and grouped Grouped box plots are discussed later in this chapter A single box plot consists of a box plot describing one column of data To create a vertical box plot for a single set of data ij box Mi 1 Select the y column 2 Click the ES button on the Plots 2D palette To create a horizontal box plot click the E button instead Vertical box plot C Horizontal box plot 26 217 122 QQ Plots Plotting One Dimensional Data A quantile quantile plot or QQ plot for short is useful for comparing your data with another set of data or with the quantiles of a distribution function that you conjecture is a good model for your data In the latter case the plot shows the ordered data values along the vertical axis versus the corresponding quantiles of the distribution function you specify along the horizontal axis You interpret the plot in a very simple way e If the points fall close to a straight line your conjectured distribution is a reasonably good model for your data e If the points do not fall close to a straight line your conjectured distribution is not a good model and you need to look for an alternative distribution that is a better model Th
92. included in the selection only the first column in the selection is sent to Spotfire S to make the graph For example using the above data you select the blocks A1 B6 and C1 D6 z 14l4 gt bi Sheet1 Sheet2 Sheets f 4 501 Chapter 10 Using Spotfire S With Other Applications 502 The Create Spotfire S Graph wizard sends A1 A6 as the x data and C1 D6 as the y data to create two line plots Selecting data for conditioning graphs When using the Create Spotfire S Graph wizard in Step 2 you can specify an Excel worksheet and data range to use for conditioning the graph you are creating A conditioned graph allows you to view your data in a series of panels where each panel contains a subset of the original data The subset in each panel is determined by the levels of the conditioning data range you select You can skip conditioning by leaving the Conditioning Range field in this dialog blank When specifying a data range for conditioning you may specify any valid data range in normal Excel range syntax For example say you specify the data range A1 B6 from the Sheet1 worksheet for the data to plot in a Spotfire S graph You can also specify the data range C1 C6 from Sheet1 for the conditioning data If a 2D line plot is created the plot is conditioned on the data in C1 C6 Handling errors during graph creation If Spotfire S encounters problems during the creation of a graph in Excel any error messages will ap
93. it easy to jump to a specific cell location in a Data window 1 Press F5 or choose View gt Go To Cell from the main menu The Go To Cell dialog opens as shown in Figure 2 11 Column CURRENT x Row CURRENT x Cancel Help Figure 2 11 The Go To Cell dialog 2 Select the column name and enter the row number of the cell you want to jump to To go to the last column last row position type or select the special key word END in the Column and Row fields 3 Click OK The Go To Cell dialog is also useful for extending a cell selection To extend a selection from the active cell to the location specified in the dialog simply hold down the SHIFT key while clicking OK For example if column 1 row 5 is the active cell and you specify column 5 row 5 in the Go To Cell dialog and press SHIFT OK the selection is extended from column 1 row 5 to column 5 row 5 Customizing a You can customize a Data window to fit your formatting preferences Data Window by using the Data Frame dialog as shown in Figure 2 12 To open the dialog do one of the following e Double click the top shaded cell in the upper left hand corner of the Data window 27 Chapter 2 Working With Data e With the Data window in focus choose Format gt Sheet from the main menu Data Frame 1 5 x Name SDF1 Color E Default Col Type double T Bold Font Tahoma T Italics Size fio Cancel AEE current Help Figure 2 12
94. least squares by adding the LTS robust line to the graph you just made 3 With the columns to plot selected select the graph region press the SHIFT key and click the Robust LTS button on the Plots 2D palette The resulting graph shown in Figure 3 11 reveals that the LS fit with outliers removed and the LTS fit with outliers included are indeed rather close to one another Notice that the scatter plot now displays the original axis ranges and that the two outliers removed from the LS fit are displayed with the robust LTS line T T T T 0 85 0 60 0 35 0 10 0 15 0 40 0 65 diff hstart Figure 3 11 Scatter plot with least squares fit no outliers and robust LTS lines 4 Now that you have the LS fit for the data with the two outliers excluded you can easily add the points back in and see how the line fits change Just choose Format gt Include All Points 68 Scatter Plots With Nonparametric Curve Fits Visualizing Two Dimensional Data from the main menu and you get the graph shown in Figure 3 12 All existing plots on the graph will be recalculated to include all points 0 85 0 60 0 35 0 10 0 15 0 40 0 65 diff hstart Figure 3 12 Scatter plot with least squares and robust LTS lines outlier points included In the previous section we fit linear parametric functions to the scatter plot data Frequently you do not have enough prior information to determine what kind of parametric
95. main menu select the desired file and click OK 481 Chapter 9 Using the Script and Report Windows PRINTING A SCRIPT OR REPORT 482 To print a Script or Report in Spotfire S you can use the Windows standard Print button amp or the Print Script or Print Report option in the File menu To print using the Print button click the Print button on the Standard toolbar A dialog appears asking you to confirm your print choice To print using the Print dialog do the following 1 From the main menu choose File gt Print Script or File gt Print Report The Print dialog appears 2 In the Print dialog choose the options that you want See the online help for a description of the options 3 Click OK to start printing USING SPOTFIRE S WITH OTHER APPLICATIONS Using Spotfire S With Microsoft Excel Linking Data Between Excel and Spotfire S Using the Excel Add In Application Using Spotfire S With SPSS Using Spotfire S With MathSoft Mathcad Using Spotfire S With Microsoft PowerPoint 484 484 495 503 509 516 483 Chapter 10 Using Spotfire S With Other Applications USING SPOTFIRE S WITH MICROSOFT EXCEL Linking Data Between Excel and Spotfire S Creating or Opening an Excel Worksheet 484 There are two different ways in which you can use Spotfire S with Microsoft Excel e The Excel Link Wizards give you the ability to plot or analyze data stored in an Excel worksheet from within Spot
96. melinda v Browse If you check this box this dialog will no longer appear at startup To get it back check the option in the Options General Settings Startup dialog box I Always start in this project e J ee Figure 7 16 The Open Spotfire S Project dialog If you set this behavior as the default Spotfire S will ask you to specify the project folder you want to use for the upcoming session each time you start the program A Spotfire S project folder is the central Windows folder for storing the data and documents you create and modify during a session When you specify a Windows folder as a Spotfire S project folder Spotfire S automatically creates two important subfolders within it e A Data folder which corresponds to the working data for that particular project e A Prefs folder in which Spotfire S saves your preferences for example the selections you make in the General Settings dialog and customizations for example when you save a new default Object Explorer In addition the project folder becomes the default folder for saving the document objects such as Graph Sheets reports and scripts that you must manually save in files in order to permanently store Because each project folder has its own Data and Prefs folders and Organizing Your Work is the default folder for saving your document objects project folders provide a handy way to organize the work you do in Spotfire S By making
97. miles The argument car miles is a built in S PLUS vector data object Since there is no other argument to plot the data are plotted against their natural index or observation numbers through 120 Since you may be interested in gas mileage you may want to plot car miles against car gals This is also easy to do with plot gt plot car gals car miles The result is shown in Figure 8 3 Graphics in Spotfire S car miles 200 250 300 350 fi fi fi fi 150 fi 100 fi o car gals Figure 8 3 A Spotfire S plot You can use many S PLUS functions besides plot to display graphical results in a Spotfire S graphics window Many of these functions are listed in Table 8 4 and Table 8 5 which display respectively high level and low level plotting functions High level plotting functions create a new plot complete with axes while low level plotting functions typically add to an existing plot Table 8 4 Common high level plotting functions barplot hist Bar graph histogram boxplot Boxplot brush Brush pair wise scatter plots spin 3D axes contour image 3D plots persp symbols P coplot Conditioning plot dotchart Dotchart faces stars Display multivariate data 447 Chapter 8 Using the Commands Window Table 8 4 Common high level plotting functions Continued map Plot all or part of the U S part of the maps library pairs Plot all p
98. modeled using log linear regression In log linear regression the response is assumed to be generated from a Poisson distribution with a centrality parameter that depends upon the values of the covariates Fitting a log linear Poisson regression From the main menu choose Statistics gt Regression gt Log linear Poisson The Log linear Poisson Regression dialog opens as shown in Figure 6 45 Log linear Poisson Regression BEI Model Options Results Plot Predict m Data Data Set solder ha eee Weights hd Link log bd Subset Rows with 3 Save Model Object M Omit Rows with Missing Values Save As Variables Dependent Independent Formula skips X skips Create Formula Cancel Apply f current The Log linear Poisson Regression dialog Example Help In this example we fit a Poisson regression to the solder data 1 Open the Log linear Poisson Regression dialog 2 Type solder in the Data Set field Logistic Regression Regression 3 Select skips as the Dependent variable and lt ALL gt in the Independent variable list This generates skips in the Formula field 4 Click OK A summary of the log linear regression appears in the Report window The values in the resulting table of coefficients are all fairly large indicating that all of the process variables have a significant influence upon the number of skips gener
99. obtaining help for 421 Michaelis Menten relationship 290 Microsoft Access files hints for importing and exporting 190 193 Microsoft Excel files hints for importing and exporting 193 Microsoft PowerPoint 516 Microsoft SQL Server files hints for importing and exporting 190 missing value or NA 38 44 modeling statistical 454 model objects 206 monothetic analysis 338 Move Block dialog 39 Move Columns dialog 40 Move Rows dialog 40 moving cells by dragging 37 using the clipboard 38 using the Data menu 39 columns by dragging 40 using the clipboard 40 using the Data menu 40 rows by dragging 40 using the clipboard 40 using the Data menu 40 multidimensional data 87 multipanel plots 109 multiple plot layout 449 multiple plots on a graph 93 multivariate analysis of variance MANOVA 344 N NA or missing value 38 44 New Data Set button 16 New Folder button 389 New Working Chapter dialog 410 NLS plots 135 nonlinear curve fitting plots 135 Nonlinear Least Squares Regression dialog 287 288 290 291 nonlinear regression 288 nonparametric curve fits 69 nonparametric density estimates 125 normal distributions 371 normal power and sample size 259 Normal Power and Sample Size dialog 259 O object defaults 523 Object Explorer 14 378 automatically opening at startup 395 closing 385 customizing 390 default 394 document object type of 381 drag and drop in 404 elements of 385 Explorer Pages in deletin
100. of its values using an integer sequence to ffi bar label the bars or select both x and y to create a bar plot of y using the x data to label the bars Click the Wii button on the Plots 2D palette If any of the of values in the column is negative click the Es button instead Plotting One Dimensional Data To create a horizontal bar plot select the columns in reverse order and click the E button Vertical bar with base at Y min plot aln Horizontal bar plot x p OOMOO T MEERE AEE i T f T T T T 0 50 0 55 0 60 0 65 0 70 0 75 y x S pS o o a a Bar with base at zero plot is If your data set contains a z column of error values you can create a bar plot of y using the z data for the error bars To create a bar with error plot 1 Select the x y and z columns 2 Click the lili button on the Extra Plots palette 129 Chapter 4 Creating Plots Pareto Plots 130 For data arranged in multiple y columns Spotfire S automatically calculates and displays error bars See the online help for details Bar with error plot la 104 I 8 I I Lia ji _ 8 I I Pa 1 T i 0 T T A
101. of numbers and another list Functions A function is an object containing Spotfire S interpreted code that performs an analytical task using data and other function objects In addition to accessing the functions built into S PLUS you can also write your own functions in the S PLUS language Other engine objects There are a number of more esoteric objects primarily used in programming such as expressions names and formulas For more information on these types of objects consult the Programmer s Guide Interface objects reside in memory during the execution of the Spotfire S application They are loaded at startup and archived when the application is closed These archived files can be found in your preferences Prefs folder Some types of interface objects such as menu items and toolbars can be used to customize the user interface For more information see Chapter 8 Extending the User Interface in the Application Developer s Guide Document objects are displayed as child windows of the Spotfire S application Unlike engine objects document objects are not saved in databases To permanently store these types of objects you must save them in files Note that the file format of a document object is unique to its particular type Databases The Search Path Understanding Object Types and Databases Graph Sheets A Graph Sheet sgr file is a document object containing a graph Because Spotfire S is o
102. of the chapter folder containing the desired Data folder or click Browse and navigate to it 410 Organizing Your Work To create and attach a new working database that is a new Data folder within a new chapter folder type the pathname of a new chapter folder At the confirmation prompt click OK 2 In the Label text box specify a label to use to identify the database This is the name that will appear in the Object Explorer 3 Click OK Note If another chapter is currently attached in the default position one the old chapter is moved to position two and detached The new working chapter is then attached in position one 411 Chapter 7 Working With Objects and Databases 412 USING THE COMMANDS WINDOW Introduction 415 Commands Window Basics 416 Entering Expressions 416 Basic Syntax 417 Quitting Spotfire S 420 Command Line Editing 420 Getting Help in Spotfire S 421 S PLUS Language Basics 424 Data Objects 424 Managing Data Objects 429 Functions 431 Operators 432 Expressions 434 Optional Arguments to Functions 436 Importing and Editing Data 438 Reading a Data File 438 Entering Data From the Keyboard 438 Reading an ASCII File 439 Editing Data 440 Built In Data Sets 441 Extracting Subsets of Data 442 Subsetting From Vectors 442 Subsetting From Matrices 443 Graphics in Spotfire S 446 Making Plots 446 Multiple Plot Layout 449 Statistics 451 Summary Statistics 451 413 Chapter 8 Using t
103. of the dialog Undoing Actions Entering Editing and Saving Data 3 In the Data Options group select character from the Default Text Col dropdown list and click OK General Settings aioli xt General Data Startup Computations Data Options ASCII Import Export Options M Show Auto Created Data in View MV Autoload Data Sets Import Delimiter Export Delimiter IV Show Imported Data in View M Enable DataTips M Enable Smart Cursor IV Prompt on import overwrite M Buffer Data Entry Date Time Input Default Text Col factor gili Bete forme Masy Excel Link a Lime format Jf mmess tt z IV Save link information r Date Time Output IV Prompt on overwrite Date format Md yyyy z Time format h mm ss tt z M Remove data on doc close pi I Remove data on link close T Selection includes col names I Selection includes row names Cancel Figure 2 6 Changing the default column type for character data After entering some values in a Data window you may need to edit them To edit a value in a cell do the following 1 Click in the cell containing the value you want to edit 2 Either press ENTER to go into edit mode or just start typing to overwrite the current data To abandon your changes while typing press ESC There are two levels of undo for the edits you make in a Data window You can either und
104. of the five variables displayed in Figure 3 30 1 Right click any data point to display the plot s shortcut menu and select Line Histogram 90 Visualizing Multidimensional Data 2 On the Line Histogram page of the Scatter Plot Matrix dialog select the Draw Histograms check box by clicking it Data to Plot Line Histogram Symbol Vary Symbols Smooth r Line r Fill Sie zj Fill Color HBL Blue z Eolor mm Fill Pattern Emy xl Weiaht Ir Pattern Color Yew z Break line SS Border F atHissina Style T ats Jal Color Me z l Weight fi Cancel Aneb current Figure 3 31 The Line Histogram page of the Scatter Plot Matrix dialog 3 Click OK to redraw the scatterplot matrix with histograms The histograms provide you with additional useful information For example the variables Weight Disp and Fuel seem to have normal or close to normal distributions In contrast the variable Mileage is perhaps not normally distributed These conjectures would need to be investigated further before we could draw any conclusions Adding least squares lines Spotfire S can fit a variety of lines through our scatter plots We can fit least squares robust kernel loess and smoothing splines through the matrix plots For example to add least squares lines to Figure 3 30 do the following 1 Right click any data point to display the plot s shortcut menu and selec
105. one For instance to remove two objects named a and b use the following expression gt Tmt gD To look at the contents of a stored data object just type its name a ij 4 32 1 gt y 1 123456 789 10 Functions S PLUS Language Basics A function is an S PLUS expression that returns a value usually after performing some operation on one or more arguments For example the c function returns a vector formed by combining the arguments to c You calla function by typing an expression consisting of the name of the function followed by a pair of parentheses which may enclose some arguments separated by commas For example runif is a function that produces random numbers uniformly distributed between 0 and 1 To get Spotfire S to compute 10 such numbers type runif 10 gt runif 10 1 0 6033770 0 4216952 0 7445955 0 9896273 0 6072029 6 0 1293078 0 2624331 0 3428861 0 2866012 0 6368730 Spotfire S displays the results computed by the function followed by a new prompt In this case the result is a vector object consisting of 10 random numbers generated by a uniform random number generator The square bracketed numbers here 1 and 6 help you keep track of how many numbers are displayed on each line and help you locate particular numbers One of the functions in S PLUS that you will use frequently is the function c which allows you to combine data values into a vector For example gt 13 7100 103 1 3 7 100
106. options and dialogs to create or modify To drag a graph object into a Script window do the following 1 Click the New button D on the Standard toolbar and select Graph Sheet from the list Open the Annotation palette and drag a filled rectangle onto your Graph Sheet Create a new Script window by clicking on the New button C on the Standard toolbar this time selecting Script File from the list You may want to vertically tile the Script window and the Graph Sheet this makes it easier to select and drag objects between the two To tile the windows choose Window gt Tile Vertical from the main menu Select the rectangle from the graph and drag it into the upper pane the program pane of the Script window Note 6 7 8 478 As you drag the mouse the cursor changes into a drop cursor When the mouse is inside the program pane you will see a gray vertical marker line at the left most edge of the line you are over This indicates where the commands will be inserted in the script when you release the mouse When you release the mouse the commands will be written into the Script window starting at the line where you dropped the objects Delete the rectangle on your Graph Sheet In your script change the Fil1Color from Red to Blue Press the Run button on the Script toolbar A rectangle appears in your Graph Sheet this time blue Time Saving Tips for Using Scripts For more inf
107. or Excel Link Wizard analyze in Spotfire S you can use the Spotfire S to Excel Link Wizard to establish a link from the data frame in Spotfire S to an Excel worksheet To create a link from Spotfire S to Excel do the following 1 Create a new Excel worksheet from within Spotfire S 2 With the Data window displaying your S PLUS data frame in focus click the Spotfire S to Excel Link Wizard button G amp on the Data window toolbar or choose Link Wizard from the Edit or shortcut menu The Spotfire S to Excel Link Wizard opens as shown in Figure 10 3 S PLUS to Excel Link Wizard x Welcome to the S PLUS to Excel Link Wizard This wizard walks you through the steps necessary to link an S PLUS data frame to an Excel spreadsheet To continue click next lext gt Cancel Help Figure 10 7 The Spotfire S to Excel Link Wizard 491 Chapter 10 Using Spotfire S With Other Applications 3 Click Next 4 As you can see in Figure 10 8 the Source data frame field is automatically filled in with the name of the active data frame Select your newly created Excel workbook from the Target Excel Workbook dropdown list notice that the Target Excel worksheet and Target Excel range fields are automatically filled in S PLUS to Excel Link Wizard x Choose source and destination for link Source data frame my splus data fe Target Excel workbook SISOS Target Excel worksheet Sheet1 X
108. page of the Object Explorer dialog is shown in Figure 7 9 Object Explorer 1 BBE Explorer Right Pane Advanced Data Classes character data frame factor matrix named numeric integer x Model Classes fact aov aoviist anova arima bootstrap censorR eg clara co x Ea r EES Figure 7 9 The Advanced page of the Object Explorer dialog Data Classes Specify which classes of engine objects you want Spotfire S to define as data objects Model Classes Specify which classes of engine objects you want Spotfire S to define as model objects Default definitions are provided for both data classes and model classes but you can modify these definitions to suit your needs When filtering on database objects Spotfire S uses these definitions to determine what objects to display in a folder For a complete discussion of the important filtering mechanism of folders see Filtering on Objects on page 397 You can use the Explorer Page dialog to format your Explorer Pages The dialog is displayed automatically when you insert a new Explorer Page see page 388 To open the dialog for an existing Explorer Page first click its tab and then do one of the following e From the main menu choose Format gt Explorer Page Double click in the white space of the left pane of the Object Explorer e Right click in the white space of the left pane of the Object Explorer and select Properties from the shortcut menu 393 Chap
109. paralytic polio 115 contracted paralytic polio and the remaining 201 087 did not contract any kind of polio Table 6 8 A contingency table summarizing the results of the Salk vaccine trials None Nonparalytic Paralytic Vaccinated 200 688 24 33 Placebo 201 087 27 115 When working with contingency table data the primary interest is most often determining whether there is any association in the form of statistical dependence between the two categorical variables whose counts are displayed in the table The null hypothesis is that the two variables are statistically independent Setting up the data We create a vaccine data set containing the information in Table 6 8 1 Open an empty data set by clicking the New Data Set button E on the Standard toolbar 2 Enter the three columns of observations listed in Table 6 8 3 Change the column names by double clicking on V1 and typing in None double clicking on V2 and typing in Nonparalytic and then double clicking on V3 and typing in Paralytic 4 Change the row names by double clicking in the gray box next to the first entry in each row Type Vaccinated for the first row name and Placebo for the second row name 5 Rename the data set by double clicking in the upper left corner of the Data window In the dialog that appears type vaccine in the Name field and click OK Statistical inference We perform a chi square test of independency for the vaccine data
110. plot 1 10 1 10 main Straight Line gt hist rnorm 50 main Histogram of Normal gt qqnorm rt 100 5 main Samples from t 5 gt plot density rnorm 50 main Normal Density type 1 449 Chapter 8 Using the Commands Window The result is shown in Figure 8 4 Straight Line Histogram of Normal o wo co o z o o o o aad o o v o 5 i m o oe SS SS 2 4 6 8 10 3 2 1 0 1 2 3 1 10 rnorm 50 Samples from t 5 Normal Density a3 E 3 BS z 5 gq 5 5 D 2 S 2 1 0 1 2 3 2 1 0 1 a 3 Quantiles of Standard Normal density rnorm 50 x Figure 8 4 A multiple plot layout 450 STATISTICS Summary Statistics Statistics S PLUS includes functions for doing all kinds of statistical analysis including hypothesis testing linear regression analysis of variance contingency tables factor analysis survival analysis and time series analysis This section gives overviews of the functions that produce summary statistics perform hypothesis tests and fit statistical models S PLUS includes functions for calculating all the standard summary statistics for a data set together with a variety of robust and or resistant estimators of location and scale Table 8 6 gives a list of the most common functions for summary statistics Table 8 6 Common functions for summary statistics EOF Correlation coefficient cummax cummin Cumulative maximum minimum product cumprod
111. printed in the Report window If Spotfire S recognizes the data set as a groupedData structure the Formula and Random Formula fields are filled in automatically by the formula extractor for groupedData objects For more details see Chapter 14 Linear and Nonlinear Mixed Effects Models in the Guide to Statistics Volume 7 The Nonlinear Mixed Effects Models dialog fits a nonlinear mixed effects model in the formulation described in Lindstrom and Bates 1990 but allows for nested random effects Fitting a nonlinear mixed effects model From the main menu choose Statistics gt Mixed Effects Nonlinear The Nonlinear Mixed Effects Models dialog opens as shown in Figure 6 53 307 Chapter 6 Statistics 308 Nonlinear Mixed Effects Models BEI Model Options Results Plot Predict r Data Data Set Soybean X ape Subset Rows with Save Model Object M Omit Rows with Missing Values Save As Effects Fixed asym xmid scal 1 Random fasym xmid scal 11Plt lh Eormula weight SSlogis Time Asym xmid scal Parameters name value fired c 18 52 7 5 Cancel Apply 4 current Figure 6 53 The Nonlinear Mixed Effects Models dialog Example The Soybean data comes from an experiment that compares growth patterns of two genotypes of soybeans Variables include a factor giving a unique identifier for each plot Plot a factor indicating which variety
112. required on your part This database called the working data is the database in which all the data objects you create and modify as well as all the functions you write in the S PLUS language are automatically and transparently saved 20 Entering Editing and Saving Data You can easily view all the objects stored in your working data by using the Object Explorer For a complete discussion of the working data and how to use the Object Explorer see Chapter 7 Working With Objects and Databases If you prefer more control over which new and modified data objects you want Spotfire S to save you can instruct Spotfire S to prompt you with a dialog that gives you the opportunity to specify which changes to keep and which to discard This dialog appears when you end your Spotfire S session To set this preference do the following 1 From the main menu choose Options gt General Settings to open the General Settings dialog 2 Inthe Prompts Closing Documents group on the General page of the dialog select the Show Commit Dialog on Exit check box and click OK General Settings maix General Data Startup Computations m Prompts Closing Documents DDE Server Support IV Prompt to Save Graph Sheets IV Respond to DDE Requests T Prompt to Save Data Files I Old Format for DDE Request Remove Data from Database J Enable Toollins Never Remove I Color Toolbar I Large Buttons Automation Iv Teehe
113. s chi square test for 2D contingency table var test F test to compare two variances kruskal test Kruskal Wallis rank sum test fisher test Fisher s exact test for 2D contingency table Statistics Table 8 7 S PLUS functions for hypothesis testing Continued Test Description binom test Exact binomial test friedman test Friedman rank sum test mcnemar test McNemar s chi square test prop test Proportions test cor test Test for zero correlation mantelhaen test Mantel Haenszel chi square test The following example illustrates how to use t test to perform a two sample t test to detect a difference in means This example uses two random samples generated from N 0 1 and N 1 1 distributions We set the random number seed with the function set seed so this example is reproducible gt set seed 19 gt x lt rnorm 10 gt y lt rnorm 5 mean 1 gt t test x y Standard Two Sample t Test data x and y p 1 9312 df 13 p value 07175 alternative hypothesis true difference in means is not equal to 0 95 percent confidence interval 1 7254080 0 3502894 sample estimates mean of x mean of y 0 4269014 0 2606579 453 Chapter 8 Using the Commands Window Statistical Models 454 Most of the statistical modeling functions in S PLUS follow a unified modeling paradigm in which the input data are represented as a data frame and the model to be fit is represented as a
114. scatterplot matrices 87 151 scatter plots 62 87 132 3D 144 least squares straight line fits for 65 nonparametric curve fits for 69 robust line fits for 65 selecting and highlighting points in 63 scripts creating 464 document object type of 381 editing 465 moving and copying text in 468 opening 465 printing 467 running 466 errors and warnings in 468 portions of 466 with Run button 466 saving 466 stopping 467 undoing edits in 469 using find and replace in 469 Script window 461 464 automatic delimiter matching in 472 automatic indention in 472 automatic insertion of rights braces in 472 changing defaults settings of 473 dragging function objects into 479 dragging graph objects into 478 editing text in 468 output pane of 462 464 overview of 462 program pane of 462 464 savings settings as defaults for 474 selecting text in 468 using the Find button in 469 viewing the History Log in 475 476 search path 14 381 382 383 384 396 401 displaying in the right pane 381 masked objects 383 384 selecting cells a block of 29 all in a Data window 29 a single 29 extending a selection of 29 Index using Go To Cell dialog 27 columns a single 29 contiguous 29 in a special order 29 noncontiguous 29 rows a single 29 contiguous 29 in a special order 29 noncontiguous 29 with CTRL key 29 30 with SHIFT key 29 30 sensors data 92 shortcuts object 385 SigmaPlot files hints for importing and exporting 190 sliced
115. structures objects of class 1ist and structure respectively Note You can use the Advanced page of the Object Explorer dialog to explicitly specify what you want Spotfire S to define as data and model objects See page 393 for details Functions If selected the folder filters on function objects By selecting your working data as the database to filter you can display functions you have written in the S PLUS language or those you have created by modifying the built in functions Folder page Documents group The Documents group is for filtering on Graphs Scripts and Reports currently open in your session document objects are not stored in databases Select any or all of these check boxes as desired Objects page The Objects page of the Folder dialog shown in Figure 7 13 lists the objects that are exceptions to the folder s filtering properties Folder 1 aioli xt Folder Objects Advanced Objects Copied To Folder Object Paths Remove Objects Excluded From Filter Object Paths Cancel Apply current Figure 7 13 The Objects page of the Folder dialog 399 Chapter 7 Working With Objects and Databases Objects page Objects Copied To Folder group Object Paths This text box displays references to objects not matching the folder s filtering properties but whose shortcuts you placed in the folder To remove one of these object shortcuts from the folder se
116. target cell locations To move or copy a cell or block of cells using Cut Copy and Paste do the following 1 Select the cell or block of cells you want to move or copy 2 Do one of the following To move the cell or block press CTRL X or click the Cut button amp on the Standard toolbar or choose Cut from the Edit or shortcut menu 38 Manipulating Data To copy the cell or block press CTRL C or click the Copy button on the Standard toolbar or choose Copy from the Edit or shortcut menu 3 Click the mouse in the new location in the Data window 4 Press CTRL V or click the Paste button f on the Standard toolbar or choose Paste from the Edit or shortcut menu To move or copy a cell or block of cells using the Data menu do the following 1 From the main menu choose Data gt Move gt Block to move the cell or block or Data Copy gt Block to copy the cell or block Depending upon your selection either the Move Block or Copy Block dialog opens as shown in Figure 2 19 Move Block ioi xi Copy Block 5 x From To 7 From q r To Data Set ethanol z Data Set ethanol X Data Set ethanol x Data Set ethanol Columns NOx X Columns lt END gt X Columns NOx fs Columns lt END gt X Rows E 4 Rows 1 3 Rows 2 4 Rows 1 3 Cancel Apply current Help Cancel Apply ie ico Figure 2 19 The Move Block and Copy Block dialogs 2 In the Columns and Ro
117. the Kaplan Meier estimate The Nonparametric Survival dialog fits a variety of nonparametric survival curves and allows the inclusion of grouping variables Fitting a nonparametric survival curve From the main menu choose Statistics P Survival Nonparametric Survival The Nonparametric Survival dialog opens as shown in Figure 6 56 Nonparametric Survival BEI Model Options Results Plot m Data r Model Data Set leukemia x Curve Type kaplan meier x Weights v Subset Rows with gt Save Model Object Save As M Omit Rows with Missing Values a OD Foma Formula Surv time status 4 Create Formula Cancel Apply 4 current Figure 6 56 The Nonparametric Survival dialog Cox Proportional Hazards Survival Example The leukemia data set contains data from a trial to evaluate efficacy of maintenance chemotherapy for acute myelogenous leukemia We fit a Kaplan Meier survival curve to the full set of data 1 Open the Nonparametric Survival dialog 2 Type leukemia in the Data Set field 3 Enter the Formula Surv time status 1 or click on the Create Formula button to construct the formula The Surv function creates a survival object which is the appropriate response variable for a survival formula To use the Formula builder click the Create Formula button In the dialog that appears highlight the time variable and click the Time 1 button Highlight the statu
118. the General Settings dialog 3 In the Open at Startup group select the Object Explorer check box and click OK 395 Chapter 7 Working With Objects and Databases WORKING WITH OBJECTS Finding Objects 396 As we stated in the introduction to this chapter everything in S PLUS is an object The Object Explorer as its name implies gives you a way to explore the structure of your S PLUS objects The Object Explorer is also a handy tool for finding objects stored in databases and for organizing your objects by using the filtering mechanism of your Explorer Page folders In addition you can use the Object Explorer to create select view edit copy move and delete objects and object shortcuts The Find Objects dialog is a powerful searching tool for finding objects stored in any database in the current search path To find an object and place it in the current folder do one of the following e Click the folder s icon to select it and then click the Find Objects button amp on the Object Explorer toolbar or choose Edit gt Find from the main menu Right click the folder s icon and select Find from the shortcut menu To find an object and place it in a new folder named Found Objects do one of the following e With no objects selected in the left pane click the Find Objects button PA on the Object Explorer toolbar or choose Edit gt Find from the main menu e Right click in the white space of the left pan
119. the Independent variables As a third way of generating a formula click the Create Formula button select conc as the Response variable and CTRL click to select vel and state as the Main Effects You can use the Create Formula button to create complicated linear models and learn the notation for model specifications The online help discusses formula creation in detail 4 On the Plot page of the dialog select Cond Plots of Fitted vs Predictors This type of plot displays a separate plot in one variable for different subsets of another variable In our case it plots a separate curve for each level of state 5 Click OK A summary of the loess model is presented in the Report window and a Graph Sheet displays the conditional plot Nonlinear regression uses a specific nonlinear relationship to predict a continuous variable from one or more predictor variables The form of the nonlinear relationship is usually derived from an application specific theoretical model The Nonlinear Regression dialog fits a nonlinear regression model To use nonlinear regression specify the form of the model in S PLUS syntax and provide starting values for the parameter estimates Regression Fitting a nonlinear least squares regression From the main menu choose Statistics P Regression gt Nonlinear The Nonlinear Regression dialog opens as shown in Figure 6 42 Nonlinear Regression BEI Model Options Results Predict Data r Save Mode
120. the New button L on the Standard toolbar or choose File gt New from the main menu In the New dialog select Data Set and click OK As shown in Figure 2 3 a new empty Data window opens named by default SDFx where x is a sequential number SDF1 Oo x a 2 3 4 5 6 i 8 9 Figure 2 3 A new empty Data window Entering Editing and Saving Data To give your new data set a more appropriate name do the following 1 Double click the top shaded cell in the upper left hand corner of the Data window The Data Frame dialog opens as shown in Figure 2 4 Data Frame 1 5 x Name pon ooo Color e r Default Col Type doube zf T Bold Font Tahoma T Italics Size fo Cancel Apply curent Figure 2 4 The Data Frame dialog Help 2 Type anew name in the Name text box and click OK Note Valid data set names may include letters numbers and periods but must not start with a number Extended ASCII characters are not permitted You can also create a new data set and rename it at the same time by using the Data menu 1 From the main menu choose Data gt Select Data The Select Data dialog opens as shown in Figure 2 5 Select Data Mm E Source Existing Data Existing Data Name x C New Data New Dat Import File aiti I Show Dialog on Startup Cancel Appt current Figure 2 5 The Select Data
121. the original files are copied there before updating Date Time Formats group Use Regional Options for input When selected date time formats used when inputting data are reset to the current selections in your Windows Regional Options Clear this check box to preserve from session to session the selections you make in the Date Time Input group on the Data page of the dialog Use Regional Options for output When selected date time formats used when outputting data are reset to the current selections in your Windows Regional Options Clear this check box to preserve from session to session the selections you make in the Date Time Output group on the Data page of the dialog Computations Changing Defaults and Settings The Computations page of the General Settings dialog is shown in Figure 11 6 General Settings aioli xt General Data Startup Computations Eror Handing p Miscellaneous I System Debug Mode Print Digits Poo Error Action Jexpression dum TimeSeresEps 00001 Warning Action fat End Of Expre Tools Max Recursion 256 Editor notepad Output Page Size Pager notepad Width auto gt Length auto hd Cancel Figure 11 6 The Computations page of the General Settings dialog Error Handling group System Debug Mode When you select System Debug Mode Spotfire S performs various internal checks during evaluation This option gives you more information about wa
122. the shape of the distribution of the data Compare Samples The first three questions were probably in Michelson s mind when he gathered his data The last two must be answered to determine which techniques can be used to obtain valid statistical inferences from the data For example if the shape of the distribution indicates a nearly normal distribution without outliers we can use the Student s t tests to assist in answering question 2 If the data contain outliers or are far from normal we should use a robust method or a nonparametric method such as the Wilcoxon signed rank test In this section we use Spotfire S to carefully analyze the Michelson data Identical techniques can be used to explore and analyze any set of one sample data Exploratory data analysis To obtain a useful exploratory view of the Michelson data create the following four plots a boxplot a histogram a density plot and a QQ normal plot In Chapter 3 we created plots similar to the ones shown in Figure 6 9 to graphically analyze the Michelson data 1100 7 1000 7 900 7 speed 800 7 700 4 600 700 800 900 1000 1100 600 speed Figure 6 9 Exploratory data analysis plots for the Michelson data 215 Chapter 6 Statistics 0 005 7 0 004 4 0 003 7 0 002 7 0 001 7 0 000 11004 10004 900 4 speed 800 4 700 4 T 600 T 700 T 800 T 900 speed T T
123. the smoothness of the curve The issue of good bandwidth selection is complicated and has been treated in many statistical research papers You can however gain a good feeling for the practical consequences of varying the bandwidth by experimenting with smoothers on real data This section describes how to use four different types of smoothers e Kernel Smoother a generalization of running averages in which different weight functions or kernels may be used The weight functions provide transitions between points that are smoother than those in the simple running average approach Loess Smoother a noise reduction approach that is based on local linear or quadratic fits to the data 355 Chapter 6 Statistics Kernel Smoother Local Regression Loess 356 e Spline Smoother a technique in which a sequence of polynomials is pieced together to obtain a smooth curve e Supersmoother a highly automated variable span smoother It obtains fitted values by taking weighted combinations of smoothers with varying bandwidths A kernel smoother is a generalization of running averages in which different weight functions or kernels may be used The weight functions provide transitions between points that are smoother than those in the simple running average approach By default the bandwidth for the Spotfire S kernel smoother is 0 5 which includes roughly half of the data points in each smoothing window The default kernel is
124. this warning message see Chapter 11 Robust Regression in the Guide to Statistics Volume 1 The robust LTS regression method performs least trimmed squares regression It has less detailed plots and summaries than standard linear regression and robust MM regression 281 Chapter 6 Statistics 282 Performing robust LTS regression From the main menu choose Statistics Regression gt Robust LTS The Robust LTS Linear Regression dialog opens as shown in Figure 6 38 Robust LTS Linear Regression Dixi Model Options Results Plot m Data Data Set fuel frame X Weights v pubes Hose Way r Save Model Object M Omit Rows with Missing Values Save As Variables Dependent Mileage M Independent Formula Mileage wi eight Disp Create Formula Cancel Apply current Help Figure 6 38 The Robust LTS Linear Regression dialog Example In the fuel frame data we predict Mileage by Weight and Disp using robust LTS regression 1 Open the Robust LTS Linear Regression dialog 2 Type fuel frame in the Data Set field 3 Type Mileage Weight Disp in the Formula field Alternatively select Mileage as the Dependent variable and CTRL click to select Weight and Disp as the Independent variables As a third way of generating a formula click the Create Formula button select Mileage as the Response variable and CTRL click to select Weight and Disp as the Main Effects You ca
125. to select fill patterns and pattern colors in the order in which you want them to cycle 545 Chapter 11 Customizing Your Spotfire S Session Fill Color The Fill Color page of the Color Style dialog is shown in Figure 11 19 Options Lines Symbols Pattem Color Fill Color Fill Color 1 Plot Defaut Strip Color 1 Userl2 Fill Color 2 EL Masen Stip Cor Cusen Fill Color 3 J Wretow Stip Colors Cus Fill Color 4 Eeue z Stip Color Cusen Fill Color 5 EMI Macenta Stip Cols Cusen E Fill Color amp Cyan Highlight Coord uses gt Fill Color Z Wiee Highlight Color2 Muses Fill Color 8 EtG m Highlight Color3 Muse gt Fill Color 3 Lt Cyan m Highlight Colors Uses gt FillColor 10 ML Magen Highlight Color Use gt Hep Figure 11 19 The Fill Color page of the Color Style dialog The Fill Color page has ten fill color fields five strip color fields for strip labels in multipanel plots and five highlight color fields Use these fields to select fill colors strip colors and highlight colors in the order in which you want them to cycle Color Schemes There are eight available color schemes for user and image colors that are used when defining graph styles To specify color schemes choose Options gt Color Schemes from the main menu The Color Schemes dialog consists of two tabbed pages of options discussed below 546 User Colors Changing De
126. two rows of the data set The time series plots reveal that the values of tel gain during these first two years were the smallest during the entire 14 year history At the same time the diff hstart values during the first two years were near their overall average for the 14 year interval Furthermore notice that except for the first four years there is a striking correlation pattern between the two series whenever one increases so does the other It appears that the relative behavior of the two variables is different during the first 2 4 years from that in the remaining years This should make you feel more justified in using either the LS fit with the first two years deleted or the robust LTS fit as descriptors of the behavior to be expected beyond year 14 Visualizing Two Dimensional Data Time Series Plots Our line plot of the exmain data would be much nicer if the actual dates were printed on the x axis the time axis instead of the rather anonymous labeling using the integers 1 2 14 You can do this by creating a third variable Year specifying the year in which each observation was made Creating a dates variable To create an integer variable named Year 1 With the exmain Data window in focus select the third and currently empty column by clicking at the top of the column TT diff hstart 0 0600 0 1300 0 1400 0 0700 0 0500 ANN 2 From the main menu choose Insert gt Column
127. v Save As anova blood Variable ftime X IV Print Results Grouping Variable diet X Cancel Apply current Help Figure 6 17 The One way Analysis of Variance dialog Example The simplest kind of experiments are those in which a single continuous response variable is measured a number of times for each of several levels of some experimental factor For example consider the data in Table 6 2 from Box Hunter and Hunter 1978 The data consist of numerical values of blood coagulation times for each of four diets Coagulation time is the continuous response variable and diet is a qualitative variable or factor having four levels A B C and D The diets corresponding to the levels A B C and D were determined by the experimenter Table 6 2 Blood coagulation times for four diets Diet A B C D 62 63 68 56 60 67 66 62 63 71 71 60 59 64 67 61 65 68 63 235 Chapter 6 Statistics 236 Table 6 2 Blood coagulation times for four diets Continued Diet A B C D 66 68 64 63 59 Your main interest is to see whether or not the factor diet has any effect on the mean value of blood coagulation time Experimental factors such as diet are often called the treatments Formal statistical testing for whether the factor levels affect the mean coagulation time is carried out using analysis of variance ANOVA This method needs to be com
128. values Meaningful high low plots can thus include from three to five columns of data The first column selected containing the x data is used to label the x axis The final two columns represent the high and low data values Average data or open and close data should be selected as the y or y and z columns respectively To create a high low close or high low average plot F high low ioj x 1 Select the x close or average high and low columns 146 Candlestick Plots Plotting Multidimensional Data 2 Click the Ht button on the Plots 2D palette High low plot Ht 2620 7 2580 2540 2500 T T T T T T T 1 1 00 1 2 00 1 3 00 1 4 00 1 5 00 1 6 00 1 7 00 To create a high low open close plot close high 1 Select the x open 2619 37 2699 64 j ST Oy es close high and low 2615 77 2628 83 data in that order 2 Click the Ht button on the Plots 2D palette High low open close plot Ht 2620 4 2580 4 2540 7 2500 T i T T T i 1 1 1 00 1 2 00 1 3 00 1 4 00 1 5 00 1 6 00 1 7 00 A candlestick plot a variation on the high low open close plot displays the difference between the open value and the close value as a filled rectangle The color of the rectangle shows whether the difference is positive or negative 147 Chapter 4 Creating Plots To create a candlestick plot H candlestick joy x 1 2 3 4
129. variety of soybean is in the plot Variety the year the plot was planted Year the time each sample was taken time and the average leaf weight per plant weight We are interested in modeling weight as a function of Time in a logistic model with parameters Asym xmid and scal We expect that the variation increases with time and hence use generalized least squares with a Power variance structure instead of standard nonlinear regression In a Power variance structure the variance increases with a power of the absolute fitted values 1 Open the Generalized Nonlinear Least Squares dialog 2 Type Soybean in the Data Set field 3 Enter the following Formula weight SSlogis Time Asym xmid scal The SSlogis function is a se f starting function used to specify the nonlinear model as well as provide initial estimates to the solver Generalized Least Squares 4 On the Options page of the dialog select Power as the Variance Structure Type 5 Click OK A summary of the fitted model appears in the Report window 313 Chapter 6 Statistics SURVIVAL Nonparametric Survival 314 Survival analysis is used for data in which censoring is present Nonparametric survival curves are estimates of the probability of survival over time They are used in situations such as medical trials where the response is time to failure usually with some times lost to censoring The most commonly used nonparametric survival curve is
130. ydata2 variables as the y data In this case two line plots are created the first with x data as the xdata variable and y data as the ydatal variable and the second with x data as the xdata variable and y data as the ydata2 variable Steps 1 and 2 of the Create Spotfire S Graph wizard allow you to add to remove from and reorder the list of selected variables used to create the Spotfire S graph Create S PLUS Graph Step 1 of 4 Data to Graph x Select variables in order For a 2D plot select x and y columns then depending on the plot type either additional y s or columns specifying symbol properties such as color or size For a 3D plot select x y and z Variables Selected variables Use the Move button to move the variables to the Selected variables list Use the Up and Dn buttons to set the order of the selected variables Use the Del button to remove selected variables Cancel Bach LNew gt gt Finish Figure 10 13 The Create Spotfire S Graph Data to Graph dialog The Variables list is a list of all available variables in the data editor The Selected variables list is a list of variables from the available variables you have chosen to include in the Spotfire S graph When a variable is selected in either list you can use the Move buttons to move it between the lists When a variable is selected in the Selected variables list you can use the Up and Dn buttons to change the order of the var
131. 0 gt Cll TRUE helle Data Object Names S PLUS Language Basics fiig 17 TRUE hallo Object names must begin with a letter and may include any combinations of uppercase and lowercase letters numbers and periods For example the following are all valid object names mydata data ozone RandomNumbers lottery ohio 1 28 90 The use of periods often enhances the readability of similar data set names as in the following data 1 data 2 data 3 Objects and methods created with S PLUS 6 and later often follow a naming scheme that omits periods but adds capital letters to enhance readability setMethod signalSeries Warning You should not choose names that coincide with the names of S PLUS functions If you store a function with the same name as a built in S PLUS function access to the S PLUS function is temporarily prevented until you remove or rename the object you created Spotfire S warns you when you have masked access to a function with a newly created function To obtain a list of objects that mask other objects use the masked function At least seven S PLUS functions have single character names C D c I q s and t You should be especially careful not to name one of your own functions c or t as these are functions used frequently in Spotfire S Vector Data Objects By now you are familiar with the most basic object in S PLUS the vector which is a set of numbers character val
132. 0 10 level 231 Chapter 6 Statistics Kolmogorov Smirnov Goodness of Fit 232 but not at the 0 05 level Since normality holds a two sample f test is probably most appropriate for these data However for illustrative purposes we conduct a two sample Wilcoxon test to see if the two diets differ in mean weight gain We conduct a two sided test where the null hypothesis is that the difference in diets is 0 that is we test if the mean weight gain is the same for each diet 1 If you have not done so already create the weight gain data set with the instructions given on page 226 2 Open the Two sample Wilcoxon Test dialog Specify weight gain as the Data Set 4 Select gain high as Variable 1 and gain 1low as Variable 2 By default the Variable 2 is a Grouping Variable check box should not be selected and the Type of Rank Test should be set to Rank Sum Click OK The Report window shows the following output Wilcoxon rank sum test data x gain high in weight gain and y gain low in weight gain rank sum normal statistic with correction Z 1 6911 p value 0 0908 alternative hypothesis true mu is not equal to 0 You may also see a warning in the Message window because the value 107 appears twice in the data set The warning can be ignored for now The p value of 0 0908 is based on the normal approximation which is used because of ties in the data It is close to the t statistic p value of 0 0757 It therefore support
133. 1 Click an empty space within the graph region to select the 3D workbox Four green circles and a green triangle appear If only a solitary green knob appears you have clicked on the plot rather than the workbox try again Now rotate the point cloud by dragging any of the circles for rotation about the vertical axis or the triangle for rotation about the horizontal axis parallel to the screen Examine the Visualizing Multidimensional Data cloud as you rotate it After a few rotations you will start to notice something As shown in Figure 3 51 there is a slice of data missing in this seemingly random point cloud o 0 Oo o 5 o o 62 20558 8 goo o a o oo o o oo oo Do ge oo 8 8a Se 8 s Bob ye aS O a So o 2 Q e 2 PQ S GTS 2 PADD Sore oS E D O oo o o o o o3 oe o Q of a o Soag a PSQ 50 Oo oo 2 lt o o 2 2 m o 2 o o P 60 8 B 00OV Saxo o oq 3 o P8840 ge o o n OB 9A y 6 o fo Q 2 o P 2 o o o GS y wog 4 o Figure 3 51 The sliced ba11 data set rotated to reveal missing points Viewing the points in three dimensions allows you to discover structure not apparent in any of the two variable plots 115 Chapter 3 Exploring Data 116 CREATING PLOTS Introduction 119 The Plot Palettes 119 The Insert Graph Dialog 120 Plot Properties Dialogs 120 Structuring Your Data to Plot 120 Plotting One Dimensional Data 122 Box Plots 122 QQ Plots 123 Probability Plots 125 Histogram D
134. 1 Chapter 8 Using the Commands Window EXTRACTING SUBSETS OF DATA Subsetting From Vectors 442 Another powerful feature of the S PLUS language is the capability to extract subsets of data for viewing or for further manipulation The examples in this section illustrate subset extraction for vectors and matrices similar techniques can be used to extract subsets of data from other S PLUS data objects Suppose you create a vector of length 5 consisting of the integers 5 14 8 9 5 as follows gt x lt e 5 14 6 9 35 Ax HI 514 8 9 5 To display a single element of this vector just type the vector s name followed by the element s index within characters For example type x 1 to display the first element and x 4 to display the fourth element gt x 1 1h S gt x 4 1 9 To display more than one element at a time use the c function within the characters The following displays the second and fifth elements of x gt Met 253 i te 5 Use negation to display all elements except a specified element or list of elements For instance x 4 displays all elements except the fourth gt x 4 fl amp 14 8 amp Similarly x c 1 3 displays all elements except the first and third gt x c 1 3 Ci La 9 5 Subsetting From Matrices Extracting Subsets of Data A more advanced use of subsetting uses a logical expression within the characters Logical expressions divide a vecto
135. 1 Open the Pearson s Chi Square Test dialog 257 Chapter 6 Statistics 2 Type vaccine in the Data Set field 3 Select the Data Set is a Contingency Table check box 4 Click OK A summary of the test appears in the Report window The p value of 0 indicates that we reject the null hypothesis of independence Vaccination and polio status are related 258 Power and Sample Size POWER AND SAMPLE SIZE Normal Mean When designing a study one of the first questions to arise is how large a sample size is necessary The sample size depends upon the minimum detectable difference of interest the acceptable probability of rejecting a true null hypothesis alpha the desired probability of correctly rejecting a false null hypothesis power and the variability within the population s under study Spotfire S provides power and sample size calculations for one and two sample tests of normal means or binomial proportions e Normal power and sample size computes sample sizes for statistics that are asymptotically normally distributed such as a sample mean Alternatively it may be used to calculate power or minimum detectable difference for a sample of a specified size Binomial power and sample size computes sample sizes for statistics that are asymptotically binomially distributed such as a proportion Alternatively it may be used to calculate power or minimum detectable difference for a sample of a specified size The
136. 10 23 Completing the presentation PowerPoint starts and the graphs you chose are inserted as slides in the order you specified in a new PowerPoint presentation As the graphs are inserted status information appears in a box in the wizard 7 When the presentation is complete click Exit If you saved your presentation list in the wizard PowerPoint will automatically save the new presentation using the same name If you did not save your presentation list in the wizard and the Presentation Name is Untitled you will need to explicitly save it in PowerPoint CUSTOMIZING YOUR SPOTFIRE SESSION Introduction Changing Defaults and Settings Object Defaults General Settings Command Line Undo and History Text Output Routing Document Background Colors Graph Options Graph Styles Color Schemes Auto Plot Redraw Customizing Your Session at Startup and Closing Setting Your Startup Options Setting Your Closing Options 522 523 523 524 535 536 537 537 537 541 546 549 550 550 552 521 Chapter 11 Customizing Your Spotfire S Session INTRODUCTION 522 Spotfire S gives you many options for customizing your working environment In this chapter we show you how to set your general session preferences as well as how to do the following Define your own defaults for S PLUS objects Choose the font to be used in the Commands window Specify undo and history log preferences Control text output display C
137. 20 15 130 15 10 0 025 0 9 56 56 5 120 15 130 15 10 0 050 0 9 48 48 6 1209 15 130 15 10 0 100 0 9 39 39 The Binomial Power and Sample Size dialog assists in computing sample sizes for statistics that are asymptotically binomially distributed Alternatively it may be used to calculate power or minimum detectable difference for a sample of a specified size Computing power and sample size for a proportion From the main menu choose Statistics Power and Sample Size gt Binomial Proportion The Binomial Power and Sample Size dialog opens as shown in Figure 6 28 261 Chapter 6 Statistics 262 Binomial Power and Sample Size eimai xt Model Options Printout m Select Null Hypothesis Compute Sample Size Proportion 04 C Power Cram Prannrhn Min Difference 207P Ufopotior r Alternative Hypothesis Sample Type One Sample x z Probabilities o Alphafs fo 05 Test Type two sided g Power s os x r Results Sample Sizes Save As NI 100 500 1000 50 M Print Results NZ NENA Cancel Appt current Figure 6 28 The Binomial Power and Sample Size dialog Example Historically 40 of the voters in a certain congressional district vote for the Democratic congressional candidate A pollster is interested in determining the proportion of Democratic voters in an upcoming election The pollster wants to know how sizable a difference co
138. 219 t test 213 Wilcoxon signed rank test 218 two sample Kolmogorov Smirnov goodness of fit test 232 t test 224 Wilcoxon rank sum test 231 counts and proportions chi square test 255 exact binomial test 244 Fisher s exact test 248 Mantel Haenszel test 253 McNemar s test 250 proportions parameters test 246 data summaries crosstabulations 207 summary statistics 204 factor analysis 341 generalized linear models 292 k samples Friedman rank test 241 Kruskal Wallis rank sum test 240 one way analysis of variance 234 multivariate analysis of variance 344 power and sample size binomial 259 261 normal 259 principal components 343 regression linear 271 local loess 286 resampling 351 bootstrap 351 jackknife 353 smoothing supersmoother 357 survival analysis Cox proportional hazards 315 time series autocovariance correlation 359 autoregressive integrated moving average 361 tree models 322 statistical tests analysis of variance ANOVA 234 300 one sample 213 two sample 224 statistics dialogs for 200 Correlations and Covariances 209 Crosstabulations 207 209 Data Set field in 201 Density Cumulative Probability or Quantile 368 371 formulas in 201 Nonlinear Least Squares Regression 287 288 290 291 plotting from 202 Random Number Generation 372 Random Sample Generation 366 Save As field in 201 Save In field in 201 Summary Statistics 204 216 Variables field in 201 distribution functions 368 371 intr
139. 3 0 you can then click the tab that corresponds to the type of DSN Data Source Name you wish to create The type of DSN controls access to the data source you are creating as follows e User DSNs are specific to the login account that is in effect when they are created They are local to a computer and dedicated to the current user e System DSNs are local to a computer but not dedicated to a particular user Any user having login privileges can use a data source set up with a System DSN e File DSNs are file based data sources that may be shared among all users that have the same drivers installed These data sources are neither dedicated to a user nor local to a computer Importing From an ODBC Table Importing From and Exporting to ODBC Tables Click the appropriate tab and then click the Add button If you are running Administrator 2 0 you can create a User DSN by clicking the Add button from the initial dialog Or to create a System DSN click that button and then the Add button in the subsequent dialog File DSNs are only available with Administrator 3 0 The Create New Data Source dialog appears Select the ODBC driver for the database you want to connect to and click Finish If the list of drivers in the Create New Data Source dialog is empty or does not contain a driver for your database or application you need to install the database or its ODBC driver At this point a driver specific dialog should appear asking dat
140. 35 0 10 0 15 0 40 0 65 diff hstart Figure 3 5 Scatter plot of tel gain versus diff hstart The plot immediately reveals two important features in the data With the exception of two of the data points there is a positive and roughly linear relationship between new housing starts and the increase in residential telephone extensions The two exceptional data points are well detached from the remainder of the data such data points are called outliers Identifying Outliers Selecting and Highlighting Points Visualizing Two Dimensional Data Move the mouse pointer over one of the outlying points A DataTip appears showing the values of the two variables at that point as shown in Figure 3 6 a diff hstart 0 06 tel gain 1 135 0 35 0 10 0 15 0 40 0 65 diff hstart Figure 3 6 DataTip showing variable values at pointer location Notice the number that appears on the first line of the DataTip This number identifies the row number of the data set corresponding to the point In Figure 3 6 the DataTip identifies this point as Row 1 Now move the mouse pointer over the second outlying point The DataTip for this point identifies it as Row 2 Thus the first two observations in the data set are the outliers You can highlight data points in a scatter plot with a color that distinguishes them from the remainder of the data Let s highlight the two outliers in the scatter plot for the exmain data 1 Open the Graph T
141. 5 A scatter plot of ozone versus temperature From the scatter plot we hypothesize a linear relationship between temperature and ozone concentration We choose ozone as the response and temperature as the single predictor The choice of response and predictor variables is driven by the subject matter in which the data arise rather than by statistical considerations 1 Open the Linear Regression dialog 2 Type air in the Data Set field 3 Type ozone temperature in the Formula field Alternatively select ozone as the Dependent variable and temperature as the Independent variable As a third way of generating a formula click the Create Formula button and select ozone as the Response variable and temperature as a Main Effect You can use the Create Formula button to create complicated linear models and learn the notation for model specifications The online help discusses formula creation in detail 4 Go to the Plot page on the Linear Regression dialog and check the seven main diagnostic plots 5 Click OK to do the linear regression 273 Chapter 6 Statistics 274 Spotfire S generates a Graph Sheet with seven diagnostic plots You can access these plots by clicking the seven page tabs at the bottom of the Graph Sheet The plots appear similar to those shown in Figure 6 36 Spotfire S prints the results of the linear regression in the Report window wee Linear Medel eee Call Im formula ozone temperature data a
142. 5 The range of E used for each panel is determined using the default equal count method with a 25 overlap The algorithm picks interval end points that are values of the data the left end point of the lowest interval is the minimum of the data and the right end point of the highest interval is the maximum of the data The end points are chosen to make the counts of points in the intervals as nearly equal as possible and the fractions of points shared The Barley Data Visualizing Multidimensional Data by successive intervals as close to the target fraction as possible Overlapping ranges typically provide greater sensitivity in detecting non homogeneity than non overlapping ranges The conditioned plot shows a clear positive relationship between the oxides of nitrogen and the compression ratio for low values of E For high values of E the slope is close to zero In each panel the pattern appears linear Changing the plot type Since the patterns appear to be linear let s redraw the Trellis graph using a least squares fit 1 Click any point in the plot to select the plot 2 Click the Linear Fit button on the Plots 2D palette The graph is redrawn using a linear fit The sample data set barley contains data from an agricultural field trial to study the crop barley At six sites in Minnesota ten varieties of barley were grown in each of two years The data are the yields for all combinations of site variety and year so there
143. 6 with a Parzen kernel smoother line using bandwidth 0 15 Cubic smoothing splines are computed by piecing together a sequence of local cubic polynomials Smoothness is assured by having the value slope and curvature of neighboring polynomials match where they meet The smoothing parameter controls the amount of curvature within the polynomials by governing the trade off between the degree of smoothness of the curve fit and fidelity to the data values The more accurately the cubic smoothing spline fits the data values the rougher the curve is and conversely Spotfire S automatically chooses the smoothing parameter using a theoretically justified technique based on the data values Alternatively you can specify a smoothing parameter value to control the smoothness of your spline smoother Fitting the spline smooth e Make a scatter plot of sensor 5 versus sensor 6 with the cubic smoothing spline based on automatic bandwidth selection superimposed on the plot To do so convert your kernel 73 Chapter 3 Exploring Data smooth plot to a spline plot by clicking a point in the plot and then clicking the Spline button EZ on the Plots 2D palette The resulting plot is shown in Figure 3 17 Figure 3 17 Sensor 5 versus sensor 6 with a spline smoother line Choosing your own bandwidth From the shortcut menu for the plot select Smooth Sort as before to open the Line Scatter Plot dialog at the Smooth Sort p
144. 72 Informix files hints for importing and exporting 190 193 initialization of Spotfire S 550 Insert Block dialog 41 Insert Column button 42 Insert Columns dialog 42 Insert Graph dialog 120 Index inserting cells 41 columns 42 rows 43 Insert Page button 388 Insert Rows dialog 43 interface objects 380 J jackknife 353 joint distribution 153 K kernel smoothers 70 boxcar 70 parzen 72 k means method 330 Kolmogorov Smirnov goodness of fit test 219 232 Kruskal Wallis rank sum test 240 Kruskal Wallis Rank Sum Test dialog 240 L Large Icons button 387 levels experimental factor 235 levels plots 152 linear models diagnostic plots for 274 279 F statistic for 274 multiple R squared for 274 standard error for 274 line fits with selected points deleted 66 line plots 76 77 132 3D 144 List button 387 list function 428 lists components 428 engine object type of 380 loess local regression 286 559 Index 560 loess smoothers 75 356 Lotus files hints for importing and exporting 193 M MANOVA 344 Mantel Haenszel test 253 manuals online 8 masked objects 383 384 matching delimiter 472 Mathcad component for creating graphs 510 creating scripts 513 installing automatically 509 Mathcad version required for 509 modifying components 515 overview of 509 matrices engine object type of 379 subsetting from 443 matrix function 426 McNemar s test 250 Merge Two Data Sets dialog 51 methods
145. 89 A histogram of normal data 1 Open an empty data set by clicking the New Data Set button B on the Standard toolbar Open the Random Numbers dialog Verify that the name of the data set appears in the Data Set field Type Sample in the field for Target Column Enter a Sample Size of 100 and select normal from the Distribution pull down menu Note the default values of 0 and 1 for the Mean and Std Deviation Click Apply Spotfire S generates the sample of random numbers and places it in a Sample column of the data set Highlight the Sample column in the Data window Open the Plots 2D palette and click the Histogram button Fy Spotfire S plots a histogram of the 100 observations One aspect that might strike you about the histogram is how much it does not look like Figure 6 87 or Figure 6 89 Since the Random Numbers dialog is still open you can generate a new sample of data by simply clicking Apply again A new Random Numbers and Distributions data set is generated and the histogram is automatically redrawn for the new data Click Apply several times and watch the histogram change 10 Vary the Sample Size and notice how the histogram changes The larger the sample size the more likely the histogram is to look like Figure 6 89 However even for larger data sets the histogram rarely approaches the almost perfect look of Figure 6 89 For small sample sizes the shapes you see may not even resemble the normal shape
146. A TIBCO Spotfire S 8 2 for Windows User s Guide November 2010 TIBCO Software Inc IMPORTANT INFORMATION SOME TIBCO SOFTWARE EMBEDS OR BUNDLES OTHER TIBCO SOFTWARE USE OF SUCH EMBEDDED OR BUNDLED TIBCO SOFTWARE IS SOLELY TO ENABLE THE FUNCTIONALITY OR PROVIDE LIMITED ADD ON FUNCTIONALITY OF THE LICENSED TIBCO SOFTWARE THE EMBEDDED OR BUNDLED SOFTWARE IS NOT LICENSED TO BE USED OR ACCESSED BY ANY OTHER TIBCO SOFTWARE OR FOR ANY OTHER PURPOSE USE OF TIBCO SOFTWARE AND THIS DOCUMENT IS SUBJECT TO THE TERMS AND CONDITIONS OF A LICENSE AGREEMENT FOUND IN EITHER A SEPARATELY EXECUTED SOFTWARE LICENSE AGREEMENT OR IF THERE IS NO SUCH SEPARATE AGREEMENT THE CLICKWRAP END USER LICENSE AGREEMENT WHICH IS DISPLAYED DURING DOWNLOAD OR INSTALLATION OF THE SOFTWARE AND WHICH IS DUPLICATED IN TIBCO SPOTFIRE S LICENSES USE OF THIS DOCUMENT IS SUBJECT TO THOSE TERMS AND CONDITIONS AND YOUR USE HEREOF SHALL CONSTITUTE ACCEPTANCE OF AND AN AGREEMENT TO BE BOUND BY THE SAME This document contains confidential information that is subject to U S and international copyright laws and treaties No part of this document may be reproduced in any form without the written authorization of TIBCO Software Inc TIBCO Software Inc TIBCO Spotfire TIBCO Spotfire S Insightful the Insightful logo the tagline the Knowledge to Act Insightful Miner S S PLUS TIBCO Spotfire Axum S ArrayAnalyzer S EnvironmentalStats S FinMet
147. As field of a dialog Once the execution of a dialog function completes the object shows up in the Object Explorer Double clicking on the object either displays results in a Data window or prints a summary for the object For model objects such as the results from a linear regression right click context menus are available To display the related menus right click the model object in the Object Explorer Most menu choices correspond to the tabbed pages from the dialog This allows you to do plotting and prediction for a model without relaunching an entire dialog An example for a linear model object is shown in Figure 6 3 Analysis a Object Explorer iof x Contents of Data Data Object Pos Data Class Dimensions Graphs I my data 1 data frame E3 Reports Xi 1 Im Scripts Delete Short Cut GA SearchPath Cut Copy Delete Properties Plot Summary Predict Coefficients ey H Figure 6 3 The right click context menu shown for a linear model object 203 Chapter 6 Statistics SUMMARY STATISTICS Summary Statistics 204 One of the first steps in analyzing data is to create summaries This can be done numerically through the Summary Statistics Crosstabulations and Correlations and Covariances dialogs e Summary Statistics calculates summary statistics such as the mean median variance total sum quartiles etc e Crosstabulations tabulates the number of cases for eac
148. BE Cc D FG H l J K L A Pareto plot is a bar plot sorted on the dependent variable combined with a line plot displaying cumulative percentages of the categories bars A histogram of descending percentages of each category is plotted with a line plot displaying cumulative percentages A Pareto plot essentially combines the properties of a bar plot and a line plot To create a vertical Pareto plot 1 Select an x column of categorical data and a y i column of values ae i w 1 alla Small 2345 00 3 Small 1845 00 anen nal son 2 Click the i button on the Plots 2D palette To create a horizontal Pareto plot select the columns in reverse order and click the E button on the Extra Plots palette Plotting One Dimensional Data Im 3 Vertical Pareto plot a Horizontal Pareto plot 1007 Large 7 804 a Van 7 aa 607 Sporty x x Small 40 ie Medium 7 ee 207 Compact gt 0 T T T T T T T T T T T Compact Medium Small Sporty Van Large 0 20 40 60 80 100 xX y 131 Chapter 4 Creating Plots PLOTTING TWO DIMENSIONAL DATA Scatter and Line Plots 132 Scatter and line plots are the most basic kinds of plots for displaying data You can use them to plot a single column of data or to plot one data column against another To create any of the scatter line plots 1 Select either the x or y
149. Commit Dialog on Exit When selected Spotfire S displays the Save Database Changes dialog as shown in Figure 11 2 when you exit Spotfire S Save Database Changes x The following database objects have been created or modified OK during this session Current versions of selected objects will be saved in the database Cancel Jcreated modified Deselect All Select All IV Display Dialog On Exit Figure 11 2 The Save Database Changes dialog 525 Chapter 11 Customizing Your Spotfire S Session The Save Database Changes dialog gives you an opportunity to save or discard any new or changed data objects before closing the program Note that you can also turn off this feature by clearing the Display Dialog On Exit check box at the bottom of the dialog Hint To display this dialog at any time click the Restore Data Objects button on the Standard toolbar Automation group Echo ExecuteString When selected command strings executed with the automation method ExecuteStrings are displayed on the command line in the Commands window if open Selecting this option automatically enables the Show ExecuteString output option Show ExecuteString output When selected output from the result of executing the string passed to ExecuteString is displayed in the output window specified in Text Output Routing see page 537 Send Missings as VT_ERROR When selected automation client support send
150. D Drag sliders to bottom and right arrows and click the cell 25 Chapter 2 Working With Data Table 2 1 Keyboard and mouse shortcuts for navigating a Data window Continued Action Keyboard Mouse Moves to first column same row HOME Drag horizontal slider to left arrow and click the cell Moves to last column same row END Drag horizontal slider to right arrow and click the cell Moves to first row current column CTRL PAGE UP Drag vertical slider to top arrow and click the cell Moves to last row current column CTRL PAGE DOWN Drag vertical slider to bottom arrow and click the cell Selects a column CTRL SPACEBAR Click the column header Selects a row SHIFT SPACEBAR Click the row header Selects the entire Data window CTRL SHIFT SPACEBAR or CTRL A Click the top cell in the upper left hand corner of the Data window Puts cursor in selection mode and moves cursor to make block selection SHIFT ARROW KEYS Click and drag the mouse across cells Displays online help Fl Click the Help button on the Standard toolbar and then click in the Data window Displays the Go To Cell dialog F5 From the main menu choose View gt Go To Cell Puts cursor in edit mode to edit the column name 26 F9 Double click the name box of the column header Viewing and Formatting Data The Go To Cell dialog makes
151. Down T Match case 2 Type your search string in the text field labeled Find what 3 Click Find Next Help in the When working in the Commands window you can get help for any Commands and command by using the or help function For example to open the Script Windows help file for anova simply type gt help anova or gt anova To get help for a command when working in a Script window simply highlight the command and press F1 Online Manuals For a list of available manuals see the section TIBCO Spotfire S Books on page iv To view a manual online choose Help gt Online Manuals from the main menu and select the desired title Note Online versions of the documentation The online manuals are viewed using Adobe Acrobat Reader which can be installed as an option during the installation of Spotfire S It is generally useful to turn on bookmarks under the View entry of the menu bar while using Acrobat Reader rather than rely on the contents at the start of the manuals Bookmarks are always visible and can be expanded and collapsed to show just chapter titles or to include section headings Tip of the Day Spotfire S on the Web Training Courses Help Support and Learning Resources To help speed your progress in Spotfire S a handy Tip of the Day appears by default each time you start the program See Figure 1 2 Tip of the Day x Did you know You can open an Excel worksh
152. Draft Mode When selected graphs are displayed onscreen in draft mode which speeds up redraw time dramatically Note that draft mode only affects screen resolution printed output is always publication quality To toggle this option on and off click the Set Draft Mode button ie on the Standard toolbar or choose View gt Draft from the main menu Default 2D Axes Select the type of 2D axes to be used as the default Note that the selection you make here becomes the default type in the Standard toolbar Unless you specify otherwise the default type is Linear Conditioning Options group Condition Mode On Conditioning mode affects how selected data are used in creating a plot with a plot button When conditioning mode is on the last column s selected are used as conditioning variables for a multipanel graph The number of columns used for conditioning is specified in the Condition Vars field below For more information on multipanel graphs see Chapter 3 Traditional Changing Defaults and Settings Trellis Graphics in the Guide to Graphics To toggle this option on and off click the Set Conditioning Mode button on the Standard toolbar Condition Vars Select the number of columns to use as conditioning variables when conditioning mode is on Note that the selection you make here becomes the default value in the Standard toolbar However you can change this number at any time by selecting a different value from dropdown list
153. E S init This is a text file containing S PLUS expressions The default initialization file performs the remaining steps in this list If your system administrator has performed any site customization in the file SHOME local S init the actions in that file are evaluated next You can edit this to set actions to be performed at startup for every project Spotfire S then determines your working data in the current folder The S init file is a text file containing S PLUS expressions that are executed at the start of your session Note that this file is different from SHOME S init which affects all users sessions Spotfire S evaluates the function First Sys which includes evaluating the local system initialization function First 1ocal if it exists Spotfire S evaluates the environment variable S_FIRST if set or the first First function found in the search paths set by steps 3 5 In most cases the initialization process includes only one of steps 7 and 9 above Thus you will probably use only one of the following mechanisms to set your startup options Create an S PLUS function named First containing the desired options Creating the First Function Creating an S init File Setting S_FIRST Customizing Your Session at Startup and Closing e Create a text file of Spotfire S tasks named S init in your current folder e Set the Spotfire S environment variable S_FIRST as described below The First
154. Excel Sheet toolbar Note When you click the Update Excel to Spotfire S Link button the Excel data are copied to the S PLUS data frame you specified using the same data range column headings and data types as you specified in the wizard Note that if you change the dimensions of the Excel region for example by adding extra columns and or rows you must update the link Updating an Excel worksheet linked to an S PLUS data frame If the data in Spotfire S change the corresponding Excel worksheet is not automatically updated to reflect those changes You can force the data to be recopied from Spotfire S to Excel by doing the following 1 Select the name of the Excel link from the Active Link dropdown list on the Data window toolbar 494 Saving Data Removing Links Using the Excel Add in Application Using Spotfire S With Microsoft Excel 2 Click the Update Current Link button on the Data window toolbar When you close an Excel worksheet Spotfire S prompts you to save your Excel data in a file In the case of Excel data linked to an S PLUS data frame the data frame will be deleted automatically by default although it will be recreated when you next open the Excel worksheet in Spotfire S If you prefer you can change this default behavior through the Data tab of the Options gt General Settings dialog For details see Chapter 11 Customizing Your Spotfire S Session Removing an Excel to S
155. INTERACTION A description of graphical interactions expected of the user CLASSES A description of the classes the function is applicable to if it is a default method WARNING Anything the user should be warned about when using the function DETAILS Descriptions of algorithmic details and implementation issues BACKGROUND Background information on the function or method NOTE Any information that does not fit into the above categories REFERENCES Available texts and papers the user can refer to for additional information BUGS Descriptions of known bugs in the function SEE ALSO Links to related S PLUS functions EXAMPLES Coded Spotfire S examples Keywords A list of keywords that place the help file in the Contents topics of the help system 423 Chapter 8 Using the Commands Window S PLUS LANGUAGE BASICS Data Objects 424 This section introduces the most basic concepts you need in using the S PLUS language expressions operators assignments data objects and function calls When using Spotfire S you should think of your data sets as data objects belonging to a certain class Each class has a particular representation often defined as a named list of slots Each slot in turn contains an object of some other class Among the most common classes are numeric character factor list and data frame This chapter introduces the most fundamental data objects for more information see the Programmer
156. In K Means Clustering on page 330 we clustered the information in the state df data set using the k means algorithm In this example we use the partitioning around medoids algorithm 1 If you have not done so already choose File gt Load Library to load the examp1e5 library This library contains a few example data sets that are not in the main Spotfire S databases including state df 2 Open the Partitioning Around Medoids dialog 3 Type state df in the Data Set field 4 CTRL click to select the Variables Population through Area Fuzzy Partitioning Cluster Analysis 5 Click OK A summary of the clustering appears in the Report window Example 2 In Compute Dissimilarities on page 329 we calculated dissimilarities for the fuel frame data set In this example we cluster the fuel frame dissimilarities using the partitioning around medoids algorithm 1 If you have not already done so create the object fuel diss from the instructions on page 330 2 Open the Partitioning Around Medoids dialog 3 Select the Use Dissimilarity Object check box 4 Select fuel diss as the Saved Object 5 Click OK A summary of the clustering appears in the Report window Most clustering algorithms are crisp clustering methods This means that each object of the data set is assigned to exactly one cluster For instance an object lying between two clusters must be assigned to one of them In fuzzy clustering each observation is gi
157. In a Power variance structure the variance increases with a power of the absolute fitted values 1 Open the Generalized Least Squares dialog 2 Type Ovary in the Data Set field 3 Enter the following Formula follicles sin 2 pixTime cos 2 pi Time 4 On the Options page of the dialog select Power as the Variance Structure Type 5 Click OK A summary of the fitted model appears in the Report window The Generalized Nonlinear Least Squares dialog fits a nonlinear model using generalized least squares The errors are allowed to be correlated and or have unequal variances Performing generalized nonlinear least squares regression From the main menu choose Statistics Generalized Least Squares P Nonlinear The Generalized Nonlinear Least Squares dialog opens as shown in Figure 6 55 311 Chapter 6 Statistics 312 Generalized Nonlinear Least Squares BEI Model Options Results Plot Predict m Data Data Set Soybean x Subset Hows with r Save Model Object M Omit Rows with Missing Values Save As Model Formula weight Slogis Time Asym xmid scal Parameters name value Cancel Appt current Figure 6 55 The Generalized Nonlinear Least Squares dialog Example The Soybean data comes from an experiment to compare growth patterns of two genotypes of soybeans Variables include a factor giving a unique identifier for each plot Plot a factor indicating which
158. Links Statistics Graph Options Window Help ps No Active Link J SID SE zu 1 bei Linear Jel afi z JOSESAY SBA o o Ber ANH Me Ble oa 10 lt z ule was x 8 58 EELA Al bd Trial a my Excel_data C T NN 2 Figure 10 1 A sample Excel worksheet opened in Spotfire S When an Excel worksheet is in focus the Spotfire S Standard toolbar is replaced by the Excel Sheet toolbar which contains most of the buttons available on the Standard toolbar plus a new section dedicated to Excel as shown in Figure 10 2 The familiar Excel toolbar appears beneath the new Excel Sheet toolbar Note also that the View Insert Format Tools and Data menus are provided by Excel Excel to Spotfire S Link Wizard Remove Excel to Spotfire S link owt A No Active Link 7 3 0 SE ni fai Linear Je Active Link Update Excel to Spotfire S link tafi Beh Figure 10 2 The Excel Sheet toolbar 485 Chapter 10 Using Spotfire S With Other Applications Using the Excel to Spotfire S Link Wizard 486 Before you can use Spotfire S to plot or analyze data stored in an Excel worksheet you must first get the data into an S PLUS data frame This is done by using the Excel to Spotfire S Link Wizard to establish a link from a region in an Excel worksheet to a data frame in Spotfire S To create a link from Excel to Spotfire S do the following 1 Select the region in your Excel worksheet that y
159. MENT AT ANY TIME Copyright 1996 2010 TIBCO Software Inc ALL RIGHTS RESERVED THE CONTENTS OF THIS DOCUMENT MAY BE MODIFIED AND OR QUALIFIED DIRECTLY OR INDIRECTLY BY OTHER DOCUMENTATION WHICH ACCOMPANIES THIS SOFTWARE INCLUDING BUT NOT LIMITED TO ANY RELEASE NOTES AND READ ME FILES TIBCO Software Inc Confidential Information The correct bibliographic reference for this document is as follows TIBCO Spotfire S 8 2 for Windows User s Guide TIBCO Software Inc For technical support please visit http spotfire tibco com support and register for a support account iii TIBCO SPOTFIRE S BOOKS Note about Naming S PLUS and the product Spotfire S expressions and so forth language behavior Throughout the documentation we have attempted to distinguish between the language S PLUS refers to the engine the language and its constituents that is objects functions e Spotfire S refers to all and any parts of the product beyond the language including the product user interfaces libraries and documentation as well as general product and The TIBCO Spotfire S documentation includes books to address your focus and knowledge level Review the following table to help you choose the Spotfire S book that meets your needs These books are available in PDF format in the following locations e In your Spotfire S installation directory SHOME help on Windows SHOME doc on
160. Md os Manas The list object is the most general and most flexible object for holding data in Spotfire S A list is an ordered collection of components Each list component can be any data object and different components can be of different modes For example a list might have three components consisting of a vector of character strings a matrix of numbers and another list Hence lists are more general than vectors or matrices because they can have components of different types or modes and they are more general than data frames because they are not restricted to having a rectangular row by column nature You can create lists with the 1ist function To create a list with two components one a vector of mode numeric and one a vector of character strings type the following gt list 101 119 c char string 1 char string 2 ELIG 1 101 102 103 104 105 106 107 108 109 110 111 112 113 14 114 115 116 117 118 119 eae L shar siring 1 ehar siring 2 The components of the list are labeled by double square bracketed numbers here 1 and 2 This notation distinguishes the numbering of list components from vector and matrix numbering After each component label Spotfire S displays the contents of that component Managing Data Objects Assigning Data Objects S PLUS Language Basics For greater ease in referring to list components it is often useful to name the components You do this by giving each argumen
161. Model Call glm formula Kyphosis Age Number Start family binomial link probit data kyphosis na action na exclude control list epsilon 0 0001 maxit 50 trace F Deviance Residuals Min 10 Median 30 Max 2 217301 0 5440968 0 3535132 0 124005 2 149486 Regression Coefficients Value Std Error t value Intercept 1 063353291 0 809886949 1 312965 Age 0 005984768 0 003507093 1 706475 Number 0 215179016 0 121687912 1 768286 Start 0 120214682 0 038512786 3 121423 Dispersion Parameter for Binomial family taken to be 1 Null Deviance 83 23447 on 80 degrees of freedom Residual Deviance 61 0795 on 77 degrees of freedom Number of Fisher Scoring Iterations 5 299 Chapter 6 Statistics ANALYSIS OF VARIANCE Fixed Effects ANOVA 300 Analysis of variance ANOVA is generally used to explore the influence of one or more categorical variables upon a continuous response The ANOVA dialog performs classical fixed effects analysis of variance Fitting a fixed effects an ANOVA model From the main menu choose Statistics gt ANOVA P Fixed Effects The ANOVA dialog opens as shown in Figure 6 49 ANOVA JET x Model Options Results Plot Compare Data Data Set blood X Weights hd Subset Rows with Save Model Object Omit Rows with Missing Values Save As Variables Dependent fime v Independent diet Formula ftime diet Create Formula
162. Normal Power and Sample Size dialog assists in computing sample sizes for statistics that are asymptotically normally distributed Alternatively it may be used to calculate power or minimum detectable difference for a sample of a specified size Computing power and sample size for a mean From the main menu choose Statistics Power and Sample Size gt Normal Mean The Normal Power and Sample Size dialog opens as shown in Figure 6 27 259 Chapter 6 Statistics 260 Normal Power and Sample Size maizi Model Options Printout m Select r Standard Deviations Compute Sample Size Sigmat 15 Power Gees fs C Min Difference tia Sample Type Two Sample Probabilities p Null Hypothesis Alphafs 0 025 0 05 0 1 x Mear Power s 0 8 0 9 X Meant 120 5 ames Sizes _ Alternative Hypothesis fil UM Ear N2 Mean2 130 N2 N1 fi Test Type two sided a Results Save As VV Print Results Cancel Appt current The Normal Power and Sample Size dialog Help Figure 6 27 Example A scientist is exploring the efficacy of a new treatment The plan is to apply the treatment to half of a study group and then compare the levels of a diagnostic enzyme in the treatment subjects with the untreated control subjects The scientist needs to determine how many subjects are needed in order to determine whether the treatment significantly changes the concentration of the diagnostic
163. Script Window You can type commands directly into a Script window program pane using commands and expressions in the S PLUS language The Script window by default sends commands to the Spotfire S interpreter When you click the mouse in the upper pane of the Script window the caption or title of the window changes to the name of the script followed by program As you type in this pane the line and column number indicators change to reflect where you are editing The lower pane is used for script output When you run your script all output such as that from the print function calls commands in your script and any warnings or errors normally appears in this output pane this can be changed through the Options gt Text Output Routing dialog When you click the mouse in this pane the caption of the Script window changes to the name of the script followed by output You can copy text from the output pane into the clipboard but you cannot enter text To open an existing script file do one of the following e Click the Open button on the Standard toolbar From the main menu choose File gt Open In the Open dialog select the Files of type to be Spotfire S Script Files ssc q Navigate to the folder of your choice select your script file and click Open to open your script in a new Script window Look in Si dialogs ba Sl al cl dlacomm sse Files of type Script Files ssc q z Cancel Help
164. Spans 152 i Subset Rows with j Pad pooo M Omit Rows with Missing Values Taper for M Detrend I Demean Number ch Frequencies Cancel Apply 4 current Figure 6 84 The Spectrum Plot dialog Example In Autocorrelations on page 359 we computed autocorrelations for the lynx df time series In this example we plot a smoothed periodogram of the lynx df data to examine the periodicities in the series 1 If you have not done so already choose File gt Load Library to load the example5 library The example5 library contains the lynx df data Open the Spectrum Plot dialog Type lynx df in the Data Set field Oe be Select lynx as the Variable 5 Click OK A spectrum plot of the lynx df data appears in a Graph Sheet 365 Chapter 6 Statistics RANDOM NUMBERS AND DISTRIBUTIONS Random Sample of Rows 366 The Data menu provides tools for generating random numbers and calculating values related to theoretical distributions These techniques include e Random sample of rows sampling from a data set e Distribution functions performing calculations regarding a specific distribution e Random numbers creating data sets of random numbers from specified distributions Additional data operations that are less statistical in nature are described in Chapter 2 Working With Data Sometimes we have a data set and we want to randomly select a certain number of rows from
165. Target Excel range jaon Range a E e Figure 10 8 Specifying an Excel target in the wizard e If you want to change your selection click the Range button to the right of the Target Excel range field select a different range and click OK in the dialog prompt 5 Click Next 6 Figure 10 9 shows the last page of the wizard Spotfire S automatically generates a default name of the form SplusBook1 Sheet1 A1 D111 for the link If you prefer you can type a different name for the new link in the text box 7 Click Finish to close the wizard and create the link from Spotfire S to Excel 492 Using Spotfire S With Microsoft Excel S PLUS to Excel Link Wizard x Congratulations You have successfully configured the following link SplusB ook1 Sheet 41 0111 I Allow S PLUS to store link information in data frame lt Back Cancel Help Figure 10 9 The final page of the wizard Hint If you are working with the same S PLUS data frame regularly and do not want to go through the process of creating a link using the wizard each time you can allow Spotfire S to store information in the data frame that will allow the link to be recreated automatically each time you open it This works by adding an attribute to the data frame and will not harm your data If you do not want Spotfire S to modify the data frame in any way clear the Allow Spotfire S to store link information in data frame che
166. The Data Frame dialog You can use this dialog to rename your data set to change the default type for new columns or to specify the font font size and other formatting characteristics of the Data window Setting Your When you open a new empty Data window its formatting is based Preferred on a set of defaults For example the default type for new columns is Defaults double a type of numeric data By using the Data Frame dialog you can change these default settings so that any new Data windows you open will reflect your particular formatting preferences To set new defaults first make any desired changes in the Data Frame dialog for an open Data window and click OK to accept the changes Then do one of the following From the main menu choose Options gt Save Window Size Properties as Default e Right click the top shaded cell in the upper left hand corner of the Data window and select Save Data Frame as default Selecting Data In order to format or manipulate data you must first select the data on which to operate You can select a single cell a block of cells or one or more columns or rows By first selecting your data in a Data window you can also limit the scope of some menu options 28 Selecting Cells and Blocks Viewing and Formatting Data To select a single cell click in the cell you want to select To select a block of cells do one of the following Press and hold down the mouse button
167. ULL REQUIRED ARGUMENTS formula a formula object with the response on the left of a operator and the terms separated by operators on the right The response may be a single numeric variable or a matrix OPTIONAL ARGUMENTS data data frame in which to interpret the variables named in the formula subset and weights arguments This may also be a single number to handle some special cases see below for details If data is missing the variables in the model formula should be in the search path xl Figure 8 2 Help file for the 1m function The command is particularly useful for obtaining information on classes and methods If you use with a function call Spotfire S offers documentation on the function name itself and on all methods that might be used with the function if evaluated In particular if the function call is methods name where name is a function name Spotfire S offers documentation on all methods for name available in the current search list For example gt methods summary 421 Chapter 8 Using the Commands Window Reading Spotfire S Help Files 422 The following are possible methods for summary Select any for which you want to see documentation summary summary lt Default gt summary object groupVecVirtual summary object numericSequence summary object seriesVirtual summary object timeDate summary object timeEvent summary object timeRelative 9 summary o
168. UNIX Linux e In the Spotfire S Workbench from the Help gt Spotfire S Manuals menu item In Microsoft Windows in the Spotfire S GUI from the Help gt Online Manuals menu item Spotfire S documentation Information you need if you See the Must install or configure your current installation of Spotfire S review system requirements Installtion and Administration Guide Want to review the third party products included in Spotfire S along with their legal notices and licenses Licenses TIBCO Spotfire S Books Spotfire S documentation Continued Information you need if you See the Are new to the S language and the Spotfire S Getting Started GUI and you want an introduction to importing Guide data producing simple graphs applying statistical models and viewing data in Microsoft Excel Are a new Spotfire S user and need how to use Spotfire S primarily through the GUI User s Guide Are familiar with the S language and Spotfire S and you want to use the Spotfire S plug in or customization of the Eclipse Integrated Development Environment IDE Spotfire S Workbench User s Guide Have used the S language and Spotfire S and you want to know how to write debug and program functions from the Commands window Programmer s Guide Are familiar with the S language and Spotfire S and you want to extend its
169. When the input and output variables have been specified these variables are available for use in Mathcad Click out of the component and type or use the variables you have defined For example type fitted to see the results of running the S PLUS script Modifying a component You can modify any Spotfire S component by right clicking on the component in the Mathcad worksheet and choosing Properties from the shortcut menu For the script component right click on the script text and then choose Properties Clicking outside the script text pops up a different shortcut menu A properties dialog appears showing all the options of the original script or graph wizard plus for script components the option to change the input and output variable names By default these names are in0 inl in2 in3 and out0 out1 out2 out3 Modifying the input and output names is useful if you have an existing Spotfire S script that uses meaningful variable names and you don t want to edit the script before including it as a component 515 Chapter 10 Using Spotfire S With Other Applications USING SPOTFIRE S WITH MICROSOFT POWERPOINT If you have Microsoft PowerPoint 7 0 or higher installed on your computer you can automatically create a PowerPoint presentation using your Spotfire S Graph Sheets Installing the During a Typical installation of Spotfire S Setup examines your PowerPoint system for the necessary version of PowerPoint and if d
170. a run analyses and create modify or print graphs etc P Script program O x 36 10 WA n cond0 lt sum freq c t order 3 n cond lt maxi n cond n cond0 if length x lt n cond stop x is too short for the ARIMA model specified if any miss p lin cond0 stop Missing values in beginning of series miss lt 1 44444e 020 if any miss p if is null xreg xreg miss p lt miss x miss p lt miss list x x xreg xreg miss p miss p miss miss n cond n cond objects gt objects 1 Last value arima clean x tree Figure 9 1 A Script window showing the program pane above and output pane below You can execute scripts from within Spotfire S or from another application via DDE or by calling Spotfire S and passing the script name on the command line When using the S PLUS language the Script window is an alternative to the Commands window The Commands window is interactive commands typed in the Commands window are evaluated immediately through the interpreter with the output shown below each command The Script window on the other hand lets Introduction you type a set of commands and functions and evaluates them only on demand The script can be executed by clicking the Run button gt on the Script window toolbar If a section of the script is selected highlighted only that selection will be executed The output is shown in the ou
171. a binomial sample has a specified proportion parameter p In addition it can assess whether two or more samples have the same proportion parameter As the proportions parameters test uses a normal approximation to the binomial distribution it is less powerful than the exact binomial test Hence the exact binomial test is usually preferred The advantages of the proportions parameters test are that it provides a confidence interval for the proportions parameter and that it may be used with multiple samples Performing a proportions parameters test From the main menu choose Statistics gt Compare Samples gt Counts and Proportions gt Proportions Parameters The Proportions Test dialog opens as shown in Figure 6 22 Proportions Test 5 x Data M Options Data Set cancer fd Confidence Level 0 95 Success Yariable smokers Z M Apply Yates Continuity Correction Trials Variable patients Foun Hypotheses Save As Proportions Variable I Print Results X Alternative Hypothesis two sided Cancel Apply current Help Figure 6 22 The Proportions Test dialog Example Sometimes you may have multiple samples of subjects with each subject characterized by the presence or absence of some characteristic An alternative but equivalent terminology is that you have three or more sets of trials with each trial resulting in a success or failure For example the data set shown in Table
172. a box or boxcar smoother which weighs each point within the smoothing window equally Other choices include a triangle a Parzen kernel and the Gaussian kernel With a triangle kernel the weights decrease linearly as the distance from the point of interest increases so that the points on the edge of the smoothing window have a weight near zero A Parzen kernel is a box convolved with a triangle With a normal or Gaussian kernel the weights decrease with a Gaussian distribution away from the point of interest Local regression or loess was developed by W S Cleveland and others at Bell Laboratories It is a clever approach to smoothing that is essentially a noise reduction algorithm Loess smoothing is based on local linear or quadratic fits to the data at each point a line or parabola is fit to the points within the smoothing window and the predicted value is taken as the y value for the point of interest Weighted least squares is used to compute the line or parabola in each window Connecting the computed y values results in a smooth curve For loess smoothers the bandwidth is referred to as the span of the smoother The span is a number between 0 and 1 representing the percentage of points that should be included in the fit for a particular smoothing window Smaller values result in less smoothing and very small values close to 0 are not recommended If the span is not specified an appropriate value is computed using cross validation
173. a menu 40 data types of 34 changing 34 DataTips for 35 descriptions for adding or changing 32 DataTips for 32 tips for 32 inserting 42 lists of 32 moving by dragging 40 using the clipboard 40 using the Data menu 40 names of changing in place 31 using properties dialog 31 default 30 tips for 30 numbers of 30 numeric display precision of 35 changing 35 format types for 35 changing 35 removing 46 selecting 29 contiguous 29 in a special order 29 noncontiguous 29 setting defaults for 36 sorting customized 47 48 quick 47 48 command line editing 420 command line options 535 Commands window 413 editing command lines in 420 entering expressions in 416 opening 416 quitting Spotfire S from 420 comment plots 155 Index compute dissimilarities 329 concurrent views of a data set 25 continuous response variable 235 contour plots 111 152 contour plots 3D 152 conventions typographic 12 Convert to Data Frame button 403 Convert to Objects 202 Copy Block dialog 39 Copy Columns dialog 40 copying cells by dragging 37 using the clipboard 38 using the Data menu 39 columns by dragging 40 using the clipboard 40 using the Data menu 40 TOWS by dragging 40 using the clipboard 40 using the Data menu 40 Copy Rows dialog 40 Correlations and Covariances dialog 209 counts and proportions 244 Cox proportional hazards 315 Create Categories dialog 50 Create Object dialog 402 crosstabulations 207 Crosstabulations dialog 207 209
174. a set specified in the Data set field Start col By default when you select Add to existing data set Spotfire S appends the imported data to the end of the data set To specify a different starting column type or select its name in this field Note After you make your selections on the Data Specs page of the Import From File dialog Spotfire S has the basic information necessary to import a data file For greater control over your importing parameters use the Options and Filter pages discussed below The Options page of the Import From File dialog is shown in Figure 5 2 170 Importing From and Exporting to Data Files Import From File 5 x Data Specs Options Filter r General gt Additional Col names row auto wi Worksheet number Row name col auto z Auto E Started ooo IV Strings as factors End col En z I Sort factor levels Start row fi T7 Labels as numbers End row kEND gt Century cutoff 1930 ASCII Format string FO O Delimiter T Separate Delimiters Date format Ma x Time format fr mm ss tt X Cancel Appt current Figure 5 2 The Options page of the Import From File dialog Options page General group Col names row If the file you are importing contains column names type the number corresponding to the row in the file that contains the column names By default Spotfire S attempts to formulate sensible column names from the firs
175. abase and driver specific information required to connect to the database Fill in the required fields and click OK The new data source should be visible the next time the Import From ODBC or Export to ODBC dialogs are launched from Spotfire S The Import From ODBC dialog shown in Figure 5 7 below consists of two tabbed pages labeled ODBC and Filter To open the dialog do the following e From the main menu choose File gt Import Data gt From ODBC Connection Import From ODBC oi x ODBC Fiter m From Data Source lt SELECTSOURCE gt m Add Sources Modify Source Table Name X SQL Query f To Data frame sDF1 X Start col kEND gt x Insert at start col C Overwrite target Cancel Apply current Help Figure 5 7 The ODBC page of the Import From ODBC dialog 181 Chapter 5 Importing and Exporting Data ODBC page From group Data Source Select the desired data source from the dropdown list in this field Note A data source consists of the data you want to access the application that has the data and the computer and network connections necessary to reach the data If the desired data source does not appear in the Data Source dropdown list or if the list is blank you may need to configure one or more data sources To do so either use the ODBC applet available in the Control Panel or click the Add Sources or Modify Source button
176. ables The data may then be analyzed using ANOVA or other techniques The Factorial Design and Orthogonal Array Design dialogs create experimental designs The Design Plot Factor Plot and Interaction Plot dialogs produce exploratory plots for designs The Factorial Design dialog creates a factorial or fractional factorial design The basic factorial design contains all possible combinations of the variable levels possibly replicated and randomized A fractional factorial design excludes some combinations based upon which model effects are of interest Creating a factorial design From the main menu choose Statistics gt Design Factorial The Factorial Design dialog opens as shown in Figure 6 29 Factorial Design 5 x Design Structure Names Levels fa 2 Factor Names Number of Replications Row Names fi Randomization Fraction I Randomize Row Order Restricted Factors Results Save In Cancel Apply current Help Figure 6 29 The Factorial Design dialog Orthogonal Array Experimental Design Example We create a design with 3 levels of the first variable and two levels of the second 1 Open the Factorial Design dialog 2 Specify 3 2 as the Levels 3 Click OK A data set containing the design is created and displayed in a Data window The Orthogonal Array Design dialog creates an orthogonal array design Orthogonal array designs are essentially very sparse fractional
177. abulations crosstabs formula number car age type data claims na action na fail drop unused levels T 8942 cases in table fescesseses N N RowTotal N ColTotal N Total Psasnesn ses car age type A B C D RowTotl sRSSeS a ole a eee 0 3 391 1538 1517 688 4134 0 0946 0 3720 0 3670 0 1664 0 462 0 3081 0 3956 0 5598 0 6400 l 0 0437 0 1720 0 1696 0 0769 l Atneose eoercsofsesesesfiestssapecoeseateetasse 4 7 538 1746 941 324 3549 Correlations Summary Statistics 0 1516 0 4920 0 2651 0 0913 0 397 0 4240 0 4491 0 3472 0 3014 0 0602 0 1953 0 1052 0 0362 8 9 187 400 191 44 822 0 2275 0 4866 0 2324 0 0535 0 092 0 1474 0 1029 0 0705 0 0409 0 0209 0 0447 0 0214 0 0049 10 153 204 61 19 437 0 3501 0 4668 0 1396 0 0435 0 049 0 1206 0 0525 0 0225 0 0177 0 0171 0 0228 0 0068 0 0021 ColTot1 1269 3888 2710 1075 8942 l 0 14 0 43 0 30 0 12 l Test for independence of all factors Cho 2 S00 2952 defes 9 p Yates correction not used Each cell in the table contains the number of claims for that car age and type combination along with the row percentage column percentage and total percentage of observations falling in that cell The results of the test for independence indicate that the percentage of observations in each cell is
178. age In the Smoothing Spline Specs group experiment with the values available in the Deg of Freedom 74 Visualizing Two Dimensional Data degrees of freedom pull down list to control the smoothness of the spline fit The spline smoother result with 6 degrees of freedom is shown in Figure 3 18 V5 Figure 3 18 Sensor 5 versus sensor 6 with a spline smoother line using 6 degrees of freedom Loess Smoothers The loess smoother developed by W S Cleveland and co workers at Bell Laboratories is a clever approach based on local linear or local quadratic fits to the data 75 Chapter 3 Exploring Data Line Plots and Time Series Plots 76 Fitting the loess smooth Click a point in the plot to select the plot and then click the Loess button amp on the Plots 2D palette to make a loess curve fit to the sensors data and plot the curve fit with the data The result is shown in Figure 3 19 Figure 3 19 Sensor 5 versus sensor 6 with a loess smoother line using the default bandwidth Varying the bandwidth of the loess smooth Open the Line Scatter Plot dialog to the Smooth Sort page In the Loess Friedman Specs group notice the three boxes labeled Span Degree and Family Experiment with Span values above and below 0 5 the default value This number determines the bandwidth as a fraction of the range of the x axis values of the data in this case the range of the sensor 5 values Experimen
179. ain column names or contains names that you want to replace type a comma delimited list of names that you want to use in this field The Export To File dialog shown in Figure 5 4 below consists of three tabbed pages labeled Data Specs Options and Filter To open the dialog do the following e From the main menu choose File gt Export Data gt To File Export To File S x Data Specs Options Filter m From Data frame kd To File Name Browse Files of Type fasci file whitespace delimited asc dat txt prn z Cancel Apply current Help Figure 5 4 The Data Specs page of the Export To File dialog Data Specs page From group Data frame Type or select the name of the data set you want to export Importing From and Exporting to Data Files Data Specs page To group File Name By default Spotfire S creates a new data file with the same name as the data set being exported and saves it in your current project folder To save the file in a different location click Browse and navigate to the desired folder You can also replace the default file name with a different name Note If the name you specify in the File Name field is the name of an existing file Spotfire S prompts you for an overwrite confirmation Files of Type Select the desired format for the exported file from the dropdown list in this field The file extension is automatically reflecte
180. air wise scatter plots pie Pie chart pay Generic plotting qqnorm qqplot Normal and general QQ plots scatter smooth Scatter plot with a smooth curve tsplot Plot a time series usa Plot the boundary of the U S Table 8 5 Common low level plotting functions abline Add line in intercept slope form axis Add axis box Add a box around plot contour image Add 3D information to plot persp symbols identify Use mouse to identify points on a graph legend Add a legend to the plot lines points Add lines or points to a plot mtext text Add text in the margin or in the plot 448 Multiple Plot Layout Graphics in Spotfire S Table 8 5 Common low level plotting functions Continued stamp Add date and time information to the plot title Add title x axis labels y axis labels and or subtitle to plot It is often desirable to display more than one plot in a window or ona single page of hard copy To do so you use the S PLUS function par to control the layout of the plots The following example shows you how to use par for this purpose The par command is used to control and customize many aspects of Spotfire S plots In this example you use par to set up a window or a page to have four plots in two rows of two each Following the par command we issue four plot commands Each creates a simple plot with a main title gt par mfrow c 2 2 gt
181. al inference We are interested in examining whether the treatment affected the probability of survival 1 Open the Fisher s Exact Test dialog 249 Chapter 6 Statistics McNemar s Test 250 2 Type fisher trial in the Data Set field 3 Select the Data Set is a Contingency Table check box 4 Click OK A summary of the test appears in the Report window The p value of 0 0314 indicates that we reject the null hypothesis of independence Hence we conclude that the treatment affects the probability of survival In some experiments with two categorical variables one of the variables specifies two or more groups of individuals that receive different treatments In such situations matching of individuals is often carried out in order to increase the precision of statistical inference However when matching is carried out the observations usually are not independent In such cases the inference obtained from the chi square test Fisher s exact test and Mantel Haenszel test is not valid because these tests all assume independent observations McNemar s test allows you to obtain a valid inference for experiments where matching is carried out McNemar s statistic is used to test the null hypothesis of symmetry namely that the probability of an observation being classified into cell ij is the same as the probability of being classified into cell j i The returned p value should be interpreted carefully Its vali
182. allow the link to be recreated automatically each time you open the Excel worksheet in Spotfire S This works by adding a comment to the top left cell of your region shown as a little red triangle in the upper right corner of the cell and will not harm your data If you do not want Spotfire S to modify the Excel file in any way clear the Allow Spotfire S to store link information in Excel check box on this page To turn off this default behavior choose Options gt General Settings from the main menu click the Data tab and clear the Save link information check box in the Excel Link group The following two things have just occurred e The data region you selected in Excel has been copied to an S PLUS data frame 490 Using Spotfire S With Microsoft Excel e A link has been created enabling you to easily update the data frame in Spotfire S when the data in Excel change Hint You can create a quick link without explicitly establishing a link using the Excel to Spotfire S Link Wizard To do so simply click a button on a plot palette or choose an option from the Statistics menu while your Excel worksheet is the active document and a range is selected Spotfire S automatically creates the link for you with a default name and default column and row labels Using the If you have data stored in an S PLUS data frame that you would Spotfire to prefer to store in an Excel worksheet but still be able to plot
183. alog 2 Type air in the Data Set field 3 Select the variables you want summary statistics for in the Variables field For this example we choose lt ALL gt the default 4 We want to print the results in a Report window and store the results in a model object Enter summary air in the Save As field to create an object of this name in which to store the results Spotfire S overwrites any existing variable with this name without warning Make sure that the Print Results check box is selected to ensure that the results are printed in a Report window 205 Chapter 6 Statistics 5 Click on the Statistics tab to see the statistics available For this example select the Variance and Total Sum check boxes 6 Click OK A Report window is created with the following output eax Summary Statistics for data in air ozone radiation temperature wind Min 1 00 7 00 57 00 2 30 ISt 0i 2 62 113 50 71 00 7 40 Mean o 25 184 80 TL79 9 94 Median 3 14 207 00 79 00 9 70 ard Qu 396 255 50 84 50 11 50 Max 5 52 334 00 97 00 20 70 Total N 111 00 111 00 111 00 111 00 NA s 0 00 0 00 0 00 0 00 Variance 0 79 8308 74 90 82 12 67 Std Dev 0 89 91 15 9 53 3 56 Sum 360 50 20513 00 8635 00 1103 20 7 If the above output is not displayed in a Report window check the Message window for error messages and correct any problems that are reported 8 To access the model object containing the results use the Object Explorer To open th
184. alysis of variance a simple one factor analysis of variance No interactions are assumed among the main effects That is the k samples are considered independent and the data must be normally distributed e Kruskal Wallis rank sum test a nonparametric alternative to a one way analysis of variance No distributional assumptions are made e Friedman rank sum test a nonparametric analysis of means of a one factor designed experiment with an unreplicated blocking variable The ANOVA dialog provides analysis of variance models involving more than one factor see Analysis of Variance on page 300 The One Way Analysis of Variance dialog generates a simple analysis of variance ANOVA table when there is a grouping variable available that defines separate samples of the data No interactions are assumed among the main effects that is the samples are considered to be independent The ANOVA tables include F statistics which test whether the mean values for all of the groups are equal These statistics assume that the observations are normally Gaussian distributed For more complex models or ANOVA with multiple predictors use the Analysis of Variance dialog Compare Samples Perform a one way ANOVA From the main menu choose Statistics gt Compare Samples gt k Samples gt One way ANOVA The One way Analysis of Variance dialog opens as shown in Figure 6 17 One way Analysis of Variance Mm E Data r Results Data Set blood
185. amine your graph to find the long undetected discrepancy It appears in the Morris panel For all other sites 1931 produced a significantly higher overall yield than 1932 The reverse is true at Morris But most importantly the amount by which 1932 exceeds 1931 at Morris is similar to the amounts by which 1931 exceeds 1932 at the other sites Either an extraordinary natural event such as disease or a local weather anomaly produced a strange coincidence or the years for Morris were inadvertently reversed More Trellis graphics a statistical modeling of the data and some background checks on the experiment led to the conclusion that the data are in error But it was a Trellis graphic such as that created in Figure 3 42 that provided the Aha that led to the conclusion The scatter plots scatterplot matrices and Trellis plots discussed in the preceding sections may all be used to display three dimensional data In addition 3D scatter plots surface plots and contour plots are available These plots are particularly suited for plotting points in three dimensional space including plotting one variable as a function of the other two variables In Spotfire S we consider some plots to be 2D plots and others to be 3D plots This distinction is based upon the type of axes used This section discusses e 3D scatter plots e Surface plots Contour plots The first two plots are considered to be 3D plots because they use 3D axes to repr
186. ample Wilcoxon Test dialog 232 typographic conventions 12 U undo and history options 536 Undo button 19 undoing actions 469 in a Data window 19 most recent 19 restoring to initial state 20 to previous state 20 Unstack Columns dialog 50 Update Current Link button 495 Update Excel to Spotfire S Link button 494 V variable continuous response 235 Variables field 201 vector arithmetic 434 vector plots 149 vectors arithmetic for 434 creating with c function 431 engine object type of 379 subsetting from 442 viewing in a Script window 476 Ww Web sites TIBCO 9 weight gain data 225 Width to Fit Data button 33 Wilcoxon rank sum test 231 Wilcoxon signed rank test 215 218 working chapter selecting 410 working data 20 381 382 383 384 406 X XY pairs line plots 139 XY pairs plot 138 Y Y series plots 138
187. and instead pretend that the 200 observations are taken at sequential time points We create an exponentially weighted moving average Shewhart chart to monitor whether the process is staying within control limits 1 3 4 5 If you have not done so already create the qcc process data set with the instructions given on page 220 Open the Quality Control Ungrouped dialog Charts Continuous Type qcc process in the Data Set field Select X as the Variable Click OK A Shewhart chart appears in a Graph Sheet The Quality Control Charts Counts and Proportions dialog creates quality control charts for counts number of defective samples and proportions proportion of defective samples Quality Control Charts Creating quality control charts counts and proportions From the main menu choose Statistics Quality Control Charts gt Counts and Proportions The Quality Control Charts Counts and Proportions dialog opens as shown in Figure 6 76 Quality Control Charts Counts and Proportions _ x Model Results Plot r Data r Calibration Data Set foatch qcc Type Variable NumBad F Graup Batch Size Type Unequal Saved Ube Size Column NumSample a Calibration Object Batch Size ae ES Chart Type Type Number np Cancel Appt current Figure 6 76 The Quality Control Charts Counts and Proportions dialog Example We create a Spotfire S data set bat
188. andard toolbar or select Create PowerPoint Presentation from the File menu 2 The Welcome screen of the PowerPoint Presentation Wizard is displayed E PowerPoint Presentation Wizard L lx ty ig Y Welcome to the PowerPoint Presentation Wizard This wizard will guide you through the process of presenting your graphs in a Microsoft PowerPoint presentation When you are finished you will have a complete PowerPoint presentation saved to disk containing all the graphs you chose to include Cancel Back Next gt Figure 10 20 The PowerPoint Presentation Wizard 517 Chapter 10 Using Spotfire S With Other Applications 3 Click Next C PowerPoint Presentation Wizard x ig Please choose the graph sheet files to add to your PowerPoint presentation You can use the Up and Dn buttons ER to arrange the graphs in your presentation a Use Add Graph to add graphs Presentation Name Untitled w A my graph 1 Pagel Load List my graph 2 Pagel my graph 3 Pagel Seke Clear List Add Graph Remove Graph Cancel Figure 10 21 Selecting your Graph Sheets j Next gt 4 As shown in Figure 10 21 the first page of the wizard gives you several options By default the Graph Sheets currently open in your session are selected for the presentation However you can change the selections by doing any of the following e Click the Add
189. apter type the pathname of the chapter folder containing the desired Data folder or click Browse and navigate to it To create and attach a new chapter that is a new Data folder within a new chapter folder type the pathname of a new chapter folder At the confirmation prompt click OK 2 To attach the chapter as read only select the Attach read only check box 408 Organizing Your Work 3 In the Label text box specify a label to use to identify the chapter This is the name that will appear in the left pane of the Object Explorer under SearchPath 4 Click the buttons in the Position field to select the search path position in which to attach the chapter To use the chapter as your working data set Position to 1 Important Note The chapter attached in position one of the search path must be a read write chapter Typically this database is the working data associated with the current project folder Note that as presently implemented Spotfire S does not remember the databases you attached in an earlier session when starting the program at a later time Only the working data associated with a particular project folder and the Spotfire S system databases are reinstated in the search path To establish a search path for a particular project create a First function For details see Customizing Your Session at Startup and Closing on page 550 5 Click OK Detaching a When you are finis
190. aracter in the imported file should be treated For example you must use FASCII or fixed width columns not separated by delimiters if the rows in the file are not separated by line feeds or if the file splits each row of data into two or more lines Column names cannot be read from a FASCII data file If you want to name the columns type a list of names separated by any of the delimiters specified in the Delimiter field in the Alt col names field For each imported column specify one column name for example Apples Oranges Pears You can use an asterisk to denote a missing name for example Apples Pears If each row ends with a new line Spotfire S treats it as a single character wide variable that is to be skipped If you want to import only some of the rows specify a starting and ending row Format strings are used when importing data from or exporting data to fixed format ASCII FASCII text files A format string specifies how each character in the imported file should be treated You must use a format string together with the FASCII file type if the columns in the data file are not separated by delimiters Format strings for importing data When importing data from a FASCII file a valid format string consists of a percent sign followed by the data type for each column in the file Available data types are e s which denotes a character string e f which denotes a numeric value and e the asterisk
191. ard regression techniques parametric functions are fit to scatter plot data Frequently you do not have enough prior information to determine what kind of parametric function to use In such cases you can fit a nonparametric curve which does not assume a particular type of relationship Nonparametric curve fits are also called smoothers since they attempt to create a smooth curve showing the general trend in the data The simplest smoothers use a running average where the fit at a particular x value is calculated as a weighted average of the y values for nearby points The weight given to each point decreases as the distance between its x value and the x value of interest increases In the simplest kind of running average smoother all points within a certain distance or window from the point of interest are weighted equally in the average for that point The window width is called the bandwidth of the smoother and is usually given as a percentage of the total number of data points Increasing the bandwidth results in a smoother curve fit but may miss rapidly changing features Decreasing the bandwidth allows the smoother to track rapidly changing features more accurately but results in a rougher curve fit More sophisticated smoothers add variations to the running average approach For example smoothly decreasing weights or local linear fits may be used However all smoothers have some type of smoothness parameter bandwidth controlling
192. ariable site will be used as the conditioning variable Now open the Plots 2D palette Turn conditioning mode on by clicking the Set Conditioning Mode button W on the Standard toolbar The small yellow bars at the top of each of the icons on the Plots 2D palette indicate that conditioning mode is on 4 Ensure that of Conditioning Columns is set to 1 slah l e Visualizing Multidimensional Data Click the Color button L on the Plots 2D palette A Trellis graph appears showing barley yield for each variety conditioned on the site The yields for 1931 and 1932 appear in different colors The resulting graph is shown in Figure 3 41 U P z5 ogas MANANENAA variety lt LO aee LIIIN lt 50 DoT NiS30 CaS TA A DA N A E yyy o o ie 6 yield Figure 3 41 Unformatted Trellis plot of barley yields for 1931 and 1932 6 Turn conditioning mode off click the Set Conditioning Mode button EH again maximize the new Graph Sheet and close the Plots 2D palette Formatting the panels To make it easier to compare yields across sites we will make three changes to the layout of the panels Stack them in one column reorder them according to the median of the yield data shown in each panel and set the aspect ratio of each panel to 0 5 1 Right click an empty space within the graph to display the shortcut menu for the graph and select Multipanel In the Conditioning Columns
193. ariate data and the individual one dimensional components of the data are often referred to as variables Two dimensional plots help you quickly grasp the nature of the relationship between the two variables that constitute bivariate data For example is the relationship linear or nonlinear Are the variables highly correlated Are there any outliers Are there any distinct clusters When you couple 2D plot visualization of your bivariate data with one dimensional visualizations of the distribution of each of the two variables for example using box plots or histograms you gain a thorough understanding of your data The main gain data in Table 3 1 present the relationship between the number of housing starts and the number of new main telephone extensions The first column New Housing Starts is the change in new housing starts from one year to the next in a geographic area around New York City in sanitized units for confidentiality The second column Gain in Main Residential Telephone Extensions is the increase in main residential telephone extensions in the same geographic area again in sanitized units In this section we explore the relationship between these two variables Table 3 1 Main gain data Gain in Main Residential New Housing Starts Telephone Extensions 0 06 1 135 0 13 1 075 Table 3 1 Main gain data Continued Visualizing Two Dimensional Data
194. art 127 polar 143 probability 125 projection 158 quantile quantile QQ 123 rotating 3D 108 scatter 62 87 105 132 least squares straight line fits for 65 nonparametric curve fits for 69 robust line fits for 65 selecting and highlighting points in 63 scatterplot matrix 151 Smith 156 circle 157 impedance 156 reflection 156 smoothing 136 stacked bar 142 surface 110 text as symbols 138 time series 76 85 Trellis 159 using statistics dialogs 202 vector 149 XY pairs 138 XY pairs line 139 Y series 138 Plots 2D palette 119 Plots 3D palette 119 polar plots 143 Index Powerpoint 516 PowerPoint presentation creating 517 PowerPoint Presentation Wizard creating a presentation 517 disabling during Setup 516 installing automatically 516 with Setup 516 removing automatically 517 with Setup 516 precedence of operators 435 principal components technique 343 probabilities cumulative 367 probability distributions skewed 216 probability plots 125 project folders 406 407 projection plots 158 Properties button 397 proportions parameters test 246 p values 368 Q QQ plots 123 quantile quantile QQ plots 123 quantiles 367 R random effects analysis of variance 301 Random Number Generation dialog 221 372 random numbers generating 221 372 random numbers and distributions 366 Random Numbers dialog 50 221 random sample generation 366 Random Sample Generation dialog 366 563 Index 564 Random Sa
195. ata 56 Visualizing One Dimensional Data The box plot indicates that the median has a value of about 950 and that the distribution is probably a bit skewed toward the smaller values It also indicates a possible outlier with a value of about 650 0 005 7 0 004 7 0 003 7 0 002 7 0 001 7 0 000 600 700 800 900 1000 1100 speed Figure 3 2 Density estimate with histogram of the Michelson data The data points in the QO normal plot shown in Figure 3 3 do not fall particularly close to the straight line provided in the plot which suggests that the data may not be normally distributed 1100 7 1000 7 o o speed 800 7 700 4 600 T T T T T 2 1 0 1 2 Normal Distribution Figure 3 3 QQ normal plot with reference line for the Michelson data 57 Chapter 3 Exploring Data Exploring QQ plots for other distributions 1 Try making QQ plots for other distributions Right click any data point to display the shortcut menu From the shortcut menu select Distribution to display the QQ Plot dialog opened at the Distribution page Data to Plot Line Symbol 700 o Vary Symbols Smooth Sott 600 T T 2 4 Save OQ Plot as default Normal Help 2 Select t in the Function combo box type 5 in the df 1 degrees of freedom box and click OK QQ Plot 1 BEI Data to Plot Line Symbol Distribution Vary Symbols Smooth Sort
196. ata Precision Data Column Row Ascending Width Frame Type Column Width to Update Type Fit Data current link Selector Figure 2 2 The Data window toolbar Note For a complete discussion of the Excel section of the Data window toolbar see Using the Spotfire S to Excel Link Wizard on page 491 In the following sections we introduce the main features of the Data window and provide step by step procedures for performing the most common editing tasks 15 Chapter 2 Working With Data ENTERING EDITING AND SAVING DATA Creating a Data Set 16 There are a number of methods you can use to get data into Spotfire S The easiest way is to import the data from another source such as Excel Lotus or SAS The Data menu also provides a number of options for generating data For example the Transform option allows you to perform a series of operations on one column in a data set and place the results in another column The Commands window is another powerful tool for generating data By writing an expression in the S PLUS programming language you can for example add two columns together and place the results in a third column The most fundamental way to get data into Spotfire S of course is to simply type them in from the keyboard the focus of this section To create a new data set first open a new Data window by doing one of the following Click the New Data Set button B on the Standard toolbar Click
197. ata Range A1 B40 Range T First Row Contains Column Names T First Column Contains Row Names Column Names Range Range Row Names Range Range Finish Cancel Help Figure 10 4 Specifying a data range in the wizard Hint When your selection includes data only Spotfire S tries to find suitable column and row labels near the data region you select in your worksheet you can modify or delete these if you wish Alternatively if you use the CTRL key to select two or three noncontiguous regions in your worksheet prior to starting the wizard Spotfire S uses these additional regions for column and or row names Finally you can tell Spotfire S that all your selections will include column and or row names by specifying this as the default behavior For details see Chapter 11 Customizing Your Spotfire S Session e If you want to change your selection click the Range button to the right of the Data Range field select a different range and click OK in the dialog prompt e If the range you selected includes column and or row names select the First Row Contains Column Names check box and or the First Column Contains Row Names check box as appropriate 487 Chapter 10 Using Spotfire S With Other Applications Ifthe range you selected includes only data but you would now like to specify column or row names click the Range button to the right of the Column Names Range or Row Names
198. ata set by double clicking in the upper left corner of the Data window In the dialog that appears type mcnemar trial in the Name field and click OK Statistical inference We use McNemar s test to examine whether the treatments are equally effective 1 Open the McNemar s Square Test dialog 2 Type mcnemar trial in the Data Set field 3 Select the Data Set is a Contingency Table check box 4 Click OK A summary of the test appears in the Report window The p value of 0 0291 indicates that we reject the null hypothesis of symmetry in the table This suggests that the two treatments differ in their efficacy Mantel Haenszel Test Compare Samples The Mantel Haenszel test performs a chi square test of independence on a three dimensional contingency table It is used for a contingency table constructed from three factors As with McNemar s test the returned value should be interpreted carefully Its validity depends on the assumption that certain sums of expected cell counts are at least moderately large Even when cell counts are adequate the chi square is only a large sample approximation to the true distribution of the Mantel Haenszel statistic under the null hypothesis Performing a Mantel Haenszel test From the main menu choose Statistics gt Compare Samples gt Counts and Proportions gt Mantel Haenszel Test The Mantel Haenszel s Chi Square Test dialog opens as shown in Figure 6 25 Mantel Haenszel s Ch
199. ated Logistic regression models the relationship between a dichotomous response variable and one or more predictor variables A linear combination of the predictor variables is found using maximum likelihood estimation where the response variable is assumed to be generated by a binomial process whose probability parameter depends upon the values of the predictor variables Fitting a logistic regression From the main menu choose Statistics Regression Logistic The Logistic Regression dialog opens as shown in Figure 6 46 Logistic Regression Ioi x Model Options Results Plot Predict r Data Data Set kyphosis hd eral Weights hd Link logit wi Subset Rows with Save Model Object IV Omit Rows with Missing Values Save As Variables Dependent kyphosis X Independent Formula Kyphosis Age N umber Start Create Formula Cancel Apply f current Help Figure 6 46 The Logistic Regression dialog 295 Chapter 6 Statistics 296 Example The data set kyphosis has 81 rows representing data on 81 children who have had corrective spinal surgery The outcome Kyphosis is a binary variable and the other three variables Age Number and Start are numeric Figure 6 47 displays box plots of Age Number and Start for each level of Kyphosis g E Eag D lt absent present absent present Kyphosis Kyphosis Number
200. ated using an ordinary least squares analysis to calculate y values for given values of x using a transformed model where appropriate To create any of the curve fitting plots 1 Select the x and y columns 2 Click the Plots 2D palette button corresponding to the desired plot Linear least squares curve fitting plot Z Polynomial curve fitting plot le 134 Plotting Two Dimensional Data Exponential curve fitting plot I Power curve fitting plot 2 Log base 10 curve fitting plot Ia Log base e curve fitting plot v Nonlinear Nonlinear curve fitting fits a user defined model to a set of data Curve Fitting points Because you must specify a model and initial values for every PI parameter in the model simply selecting your data and clicking the ots plot button does not automatically generate the plot Instead a new Graph Sheet is opened with a plot icon in the upper left hand corner To generate the plot double click the plot icon to open the Nonlinear Curve Fitting dialog and specify the required information in the appropriate fields For detailed information on producing this type of plot see the online help 135 Chapter 4 Creating Plots Smoothing Plots 136 To create an NLS plot 1 Hi nonlinear MEE Select the x column as the independent variable and the y column as the dependent variable Click the lus
201. aved in the object fuel diss We use this object in later examples of clustering dialogs K Means One of the most well known partitioning methods is k means In the Clustering k means algorithm observations are classified as belonging to one of k groups Group membership is determined by calculating the centroid for each group the multidimensional version of the mean and assigning each observation to the group with the closest centroid 330 Partitioning Around Medoids Cluster Analysis Performing k means clustering From the main menu choose Statistics Cluster Analysis gt K Means The K Means Clustering dialog opens as shown in Figure 6 64 l x K Means Clustering Model Results m Data i Options Data Set state ct NM Num of Clusters fe Variables 4 Max Iterations 10 Save Model Object F ave As Subset Rows with M Omit Rows with Missing Values Cancel Apply K current Help Figure 6 64 The K Means Clustering dialog Example We cluster the information in the state df data set These data describe various characteristics of the 50 states including population income illiteracy life expectancy and education 1 Choose File Load Library to load the examp1e5 library Highlight example5 in the Library Name list and click OK This library contains a few example data sets that are not in the main Spotfire S databases including state df Open
202. bar plot ai A comment plot plots character data on a graph and can be used with all axes types For a 2D comment plot the x and y values specify the x y position of each comment and the z values are the comment text If no z values are specified the x y coordinates are displayed on the plot You can use comment plots to plot any character or combination of characters as a symbol to produce labeled scatter plots to automatically plot character data and to create tables To create a comment plot 1 Select the x y and z columns 2 Click the button on the palette Extra Plots Comment plot 0 48 7 0 46 7 0 44 7 0 42 4 0 40 7 FEB MAR JUN APR NOV JUL SEP oct AUG DEC 155 Chapter 4 Creating Plots Smith Plots 156 Smith plots which are drawn in polar coordinates are often used in microwave engineering to show impedance characteristics There are three types of Smith plots reflection impedance and circle Only reflection plots can be produced automatically by clicking a palette button In the Smith reflection plot the x values are magnitudes which must range between 0 and 1 The y values are angles measured clockwise from the horizontal To create a Smith reflection plot ij smith iol x
203. be specific to a particular machine select this option if you want to be able to share your Object Explorer files If this option is not selected the paths to the databases that the folder is filtering are archived In this case when the database paths are being read from file and a path exists but is not currently in the search path the user is prompted to attach the database 401 Chapter 7 Working With Objects and Databases Setting Your Preferred Defaults Manipulating Objects Creating Objects 402 After customizing a folder s filtering properties you can save your changes as the new folder default by doing one of the following e Click the folder s icon to select it and choose Options gt Save Folder as Default from the main menu e Right click the folder s icon and select Save Folder as default from the shortcut menu The Object Explorer often provides the most convenient way to manipulate some types of objects You can use the Object Explorer to create select view edit copy move and delete objects and object shortcuts The shortcut menu for each folder in an Explorer Page provides options for creating both objects and other folders If you specify a default class for object creation in a folder as discussed on page 401 an additional menu selection appears for creating this type of object When you create an object a shortcut to the object is placed in the folder while the object itself is always st
204. behave in the following manner e Browse select a node on the tree plot Summary information on the node appears in the Report window Right click to leave the selection mode Specify a name in the Save As field to save a list of the node information 323 Chapter 6 Statistics 324 Burl select a split on the tree plot Plots appear under the tree that display the change in deviance for all candidate splits The actual split has the largest change in deviance These plots are useful for examining whether other splits would produce an improvement in fit similar to the improvement from the actual split Right click to leave the selection mode Specify a name in the Save As field to save a list with information on the candidate splits Histogram specify variables for which to draw histograms in the Hist Variables field Select a split on the tree plot Plots appear under the tree that display histograms of the specified variables with separate histograms for the values in the two nodes resulting from the split Right click to leave the selection mode Specify a name in the Save As field to save a list of the variable values corresponding to the histograms Identify select a node on the tree plot The row names or numbers for the observations in that node appear in the Report window Right click to leave the selection mode Specify a name in the Save As field to save a list of the observations in each node Rug specify the variable t
205. bject oriented each element of a Graph Sheet is itself an editable object In the Object Explorer a Graph Sheet is displayed in a hierarchical fashion with the top most object being the Graph Sheet itself Scripts A script sse file is a document object containing S PLUS scripting code Reports A report rtf or srp file is a document object containing output Object Explorer As we will see in the next section the Object Explorer is the handy interface that makes it easy for you to manipulate and visually organize your S PLUS objects The Object Explorer sbf file is itself a document object that you can save in a file As we noted in the previous section engine objects are stored in internal databases The system databases contain thousands of built in engine objects including functions and sample data sets In addition as you create your own data sets and functions Spotfire S automatically saves these objects in a special database called the working data The search path displays all the databases that are currently attached in the order in which Spotfire S searches them when you request an object To see the databases in the search path do the following 1 Open the Object Explorer by clicking the Object Explorer button amp on the Standard toolbar The Object Explorer is discussed in detail in the next section 2 Click the SearchPath object s icon in the left pane of the Object Explorer 381
206. bject timeSequence 10 summary object timeSpan 11 summary object timeZoneC 12 summary object timeZoneS Selection oo N Ooan AUNG You enter the number of the desired method and Spotfire S displays the associated help file if it exists in the Windows help system the command does not check for the existence of the help files before constructing the menu After each menu selection Spotfire S presents an updated menu showing the remaining choices To get back to the Spotfire S prompt from within a menu enter 0 You call help with the name of an S PLUS function operator or data set as an argument For instance the following command displays the help file for the c function gt help c The quote marks are optional for most functions but are required for functions and operators containing special characters such as lt To get the most information from the Spotfire S help system you should become familiar with the general arrangement of the help files which are organized as follows not all files contain all sections e DESCRIPTION A short description of the function USAGE The function call with all of its arguments e REQUIRED ARGUMENTS Descriptions of arguments that are required by the function e OPTIONAL ARGUMENTS Descriptions of arguments that are optional Commands Window Basics VALUE The return value from the function SIDE EFFECTS Side effects from the function GRAPHICAL
207. ble for floating point double precision real numbers and factor for categorical data For a detailed discussion of the S PLUS data types see the Programmer s Guide To change the data type of a column do the following 1 Click in the column and then click the Change Data Type button on the Data window toolbar or choose Data gt Change Data Type from the main menu The Change Data Type dialog opens as shown in Figure 2 15 Change Data Type o x From m Type Data Set SDF1 X Current Type double Columns v1 X New Type ial Cancel Appt K current Help Figure 2 15 The Change Data Type dialog 2 In the Type group select a new data type from the New Type dropdown list and click OK Changing the Format Type Changing the Display Precision Setting Your Preferred Defaults Viewing and Formatting Data If you pause your mouse cursor over the number box in the column header a DataTip displays the column type as shown in Figure 2 16 P fuel frame PE 2 3 4 5 Weight Disp Mileage Fuel Type Small 33 00 3 03 33 00 3 03 37 00 2 70 annan 97 00 114 00 81 00 nan Eagle Summit 4 Ford Escort 4 Ford Festiva 4 liana Chim A 2345 00 1845 00 anen nN T Small Small aan Mamani Figure 2 16 A DataTip displays the column type Spotfire S uses the standard Windows format types for columns containing numeric data Mixed Number Decimal Scientific Curre
208. box Type over the text to find different text C Replacewith e eee T Match whole word only ees Cancel I Match case Figure 9 7 The Replace dialog has the same text length limits as the Find dialog 4 Inthe Replace with box type the replacement text As with the Find what box if you used Replace with in your current work session the replacement characters you last specified are selected in the Replace with box Type over the text to specify different replacement characters 470 Context Sensitive Help The Script Window e Choose Find Next to move the cursor to the next occurrence of the word in Find what e Choose Replace to replace the current occurrence of the word in Find what with the word in Replace with e Choose Replace All to replace all occurrences of the word in Find what with the word in Replace with with no confirmation dialog You can also delete text with the Replace option Follow the steps above but leave the Replace with box blank If the cursor is at the beginning in the middle or at the end of a word in a Script window pressing the F1 key will pop up help for this word Specifically if the word is the name of an S PLUS function help will be shown for this function 471 Chapter 9 Using the Script and Report Windows SCRIPT WINDOW FEATURES Automatic Matching of Delimiters Automatic Insertion of Right Braces Automatic Indentation 472 The Script window provid
209. box plots 122 grouped 140 long form stacked data 121 multiple y form data 121 braces right automatic insertion of 472 brush and spin 541 brushing 114 spinning 114 bubble color plots 92 146 bubble plots 145 C candlestick plots 147 cells copying by dragging 37 using the clipboard 38 using the Data menu 39 inserting 41 moving by dragging 37 using the clipboard 38 using the Data menu 39 selecting a block of 29 all in a Data window 29 a single 29 extending a selection of 29 using Go To Cell dialog 27 c function 431 Change Data Type button 34 Change Data Type dialog 34 chapter folders 407 chapters 378 407 selecting working 410 character strings delimiting 431 chi square goodness of fit test 222 chi square test 207 255 class 424 Clear Block dialog 44 Clear Column button 45 Clear Columns dialog 45 clearing columns 45 rows 46 Clear Row button 46 Clear Rows dialog 47 closing Spotfire S customizing 552 cluster analysis agglomerative hierarchical 335 compute dissimilarities 329 divisive hierarchical 336 fuzzy analysis 333 k means 330 monothetic 338 partitioning around medoids 331 coagulation data 235 Collapse Item button 386 color plots 88 145 colors document background 537 Color Scale Legend button 93 color schemes 546 column lists 32 columns changing the width of adjusting to widest cell 33 by dragging 33 using the toolbar 33 clearing 45 copying by dragging 40 using the clipboard 40 using the Dat
210. button on the Plots 2D palette See the online help Nonlinear least squares curve fitting plot us Scatterplot smoothers are useful for fitting arbitrary smooth functions to a scatter plot of data points 1 1 j x Y Select the x and y columns Click the Plots 2D palette button corresponding to the desired plot Plotting Two Dimensional Data Loess smoothing plot Wa Spline smoothing plot let 0 48 4 0 48 7 0 46 7 0 46 0 444 044 7 0 42 4 0 42 1 0 40 4 0 40 7 0 38 0 38 T 10 20 30 40 7 y y 4 x 10 20 30 40 X RA Robust LTS smoothing plot ag 0 48 7 0 48 7 0 46 4 0 46 5044 7 0 44 4 0 42 4 0 42 0 40 7 0 40 7 0 38 7 0 38 7 10 20 30 40 Pa 56 7 i i x MA EZ Kernel smoothing plot ee Friedman super smoothing plot E 0 48 7 0 48 pag 0 46 4 0 44 sgaid 0 425 0 427 0 40 7 0 404 0 38 T y T T 0 38 7 7 7 10 20 30 40 10 20 30 40 137 Chapter 4 Creating Plots Text as Symbols Plots Y Series Plots 138 A text as symbols plot is just a special kind of line scatter plot with text strings used as plotting symbols To create the text as symbols plot ij text as symbols iol x 1 Select the x y and z columns with the z column text used as the plotting symbols 2 Click the LE button on the Plots 2D palette B Text as symbols pl
211. by visual inspection you Column Width can either drag the cursor or use a toolbar button To change the column width by dragging do the following 1 Position the cursor on the vertical line to the right of the column heading The mouse pointer becomes a resize tool 1 2 Weight Disp 2 Drag the resize tool to the right to increase the width of the column or to the left to decrease the width To change the column width using a toolbar button do the following 1 Click in the column 2 Click the Increase Width button E or the Decrease Width button E on the Data window toolbar Each click increases or decreases the column width by one character To adjust the column width to fit the widest cell in the column do the following 1 Click in the column 2 Click the Width to Fit Data button Ea on the Data window toolbar 33 Chapter 2 Working With Data Changing the Data Type 34 If you need to set an exact column width open the column properties dialog and specify the width you want in terms of the number of characters in the default font and point size A column s data type determines the type of data you can enter in that column For example a column of type character accepts only character data while a column of type integer accepts only integer data The S PLUS data types are character complex double factor integer logical single and timeDate The two most commonly used data types are dou
212. cad Mathcad Note If you have an earlier version of Mathcad Setup warns you that Mathcad 8 02 is required and tells you that it cannot install the Spotfire S Mathcad component Using the Spotfire S Component for Mathcad With the Spotfire S component for Mathcad installed you can use it from any Mathcad worksheet by choosing Insert gt Component from the Mathcad main menu The Component Wizard appears as shown in Figure 10 15 The Component Wizard gives you two options e Create a Spotfire S graph e Create and run an S PLUS language script Often you will want to use both options in a single worksheet You might first use a script to create or modify some data in Spotfire S and then graph the data Your Mathcad worksheet can display both the data and the graph and an explanation of what you are hoping to accomplish 509 Chapter 10 Using Spotfire S With Other Applications Component Wizard x Select a component to insert File Read or Write Input Table Scriptable Object SmartSketch S PLUS Graph S PLUS Script es coc tee Figure 10 15 The Component Wizard Creating a graph To create a graph with the Spotfire S component for Mathcad do the following 1 Click in a blank space in your Mathcad worksheet A red crosshair indicates the insertion point 2 From the Mathcad main menu choose Insert gt Component to open the Component Wizard dialog 3 Select Spotfire S Grap
213. ch qcc that contains simulated data representing the number of defective items in daily batches over 40 days For the first 10 days the batches were of size 20 but for the remaining 30 days batches of 35 were taken 1 Open an empty data set by clicking the New Data Set button B on the Standard toolbar 2 Enter the following forty numbers in the first column 3 2 7 4 5 4 4 8 3 4 6 6 6 918 9 71111 9 10 10 14 5 15 11 14 15 11 10 14 8 11 13 16 14 19 13 15 23 3 The first column represents the number of defective items in the daily samples Change the column name by double clicking on V1 and typing in NumBad Press ENTER or click elsewhere in the Data window to accept the change 349 Chapter 6 Statistics 350 4 Select Data gt Transform from the main menu Verify that the name of the data set appears in the Data Set field and type NumSample in the Target Column field Type the command c rep 20 10 rep 35 30 in the Expression field and click OK This step creates a column named NumSample containing 10 copies of the integer 20 followed by 30 copies of the integer 35 The NumSamp1e column represents the batch size of the simulated observations Rename the data set by double clicking in the upper left corner of the Data window In the dialog that appears type batch qcc in the Name field and click OK We create a Number np Shewhart chart for these data 1 A Oe IS 6 Open the Quality Control Charts Counts and
214. ck box on this page To turn off this default behavior choose Options gt General Settings from the main menu click the Data tab and clear the Save link information check box in the Excel Link group The following two things have just occurred e The active S PLUS data frame has been copied to the newly created Excel worksheet e A link has been created enabling you to easily update the Excel worksheet when the data in Spotfire S change 493 Chapter 10 Using Spotfire S With Other Applications Analyzing Excel To analyze a region of Excel data that has been linked to a data frame Data in Spotfire in Spotfire S do the following S 1 Select the Excel region you want to work with by selecting the name of the S PLUS data frame from the Active Link dropdown list on the Excel Sheet toolbar ExcelShee Active Link 2 Create a graph of the data by clicking a button on a plot palette or perform a statistical analysis by choosing an option from the Statistics menu Updating Linked Updating an S PLUS data frame linked to an Excel worksheet Data If the data in Excel change the corresponding S PLUS data frame is not automatically updated to reflect those changes You can force the data to be recopied from Excel to Spotfire S by doing the following 1 Select the name of the S PLUS data frame from the Active Link dropdown list on the Excel Sheet toolbar 2 Click the Update Excel to Spotfire S Link button on the
215. click the Auto Legend button on the Graph toolbar A legend is automatically created and placed on your graph You can position the legend by selecting it click just inside the border of the legend and dragging it to the desired location The resulting graph is shown in Figure 3 25 Main Gain 24 diff hstart tel gain Year Figure 3 25 Formatted plots of tel gain and diff hstart Plots in separate panels Since the two time series are scaled somewhat differently it is useful to look at them in separate panels 1 Click inside the graph region but not on the plot to select the graph Eight green boxes appear along the border of the graph to indicate that the graph is selected 2 Click the Graph Tools button ET on the Graph toolbar 83 Chapter 3 Exploring Data 84 3 Click the Separate Panels with Varying Y Axes button on the Graph Tools palette See Figure 3 26 Main Gain diff hstart tel gain T T T T T T T T 0 2 4 6 8 10 12 14 Year Figure 3 26 Separate panels with varying Y axes plot Interpreting the main gain plots Making line plots time series plots of tel gain and diff hstart versus time is a simple yet powerful complement to making only a scatter plot of these variables Using both plot types gives you a more complete understanding of your data Earlier in this chapter we determined that the outliers were in the first
216. column a default name Vx where x is a sequential number which is displayed in the header beneath the column number You can use the default names to refer to your columns but it is usually better to replace them with names that are more descriptive Tips for naming your columns e Column names must be unique within a data set e Column names must start with a letter and may contain any combination of letters numbers and periods However column names may not include extended ASCII characters such as E Viewing and Formatting Data e S PLUS function names and other reserved words cannot be used as column names While you can refer to columns by either their names or their numbers referring to them by name is often easier since some operations cause columns to be renumbered For example if you insert a column between columns 5 and 6 all columns to the right of column 5 are renumbered If you use numbers to refer to your columns you must remember to use the new numbers in subsequent operations To change a column name in place do the following 1 Double click in the name box of the column header or with any cell in the column active press F9 2 Type anew column name or edit the existing name 3 Press ENTER or click elsewhere in the Data window to accept the changes To change a column name by using its properties dialog do the following 1 Double click in the number box of the column header or click in the col
217. creative use of multiple project folders you can structure your work into distinct projects keeping the data and documents for each project separate Specifying a If you choose see Chapter 11 Customizing Your Spotfire S Project Folder Session the Open Spotfire S Project dialog will appear each time you start the program giving you the opportunity to specify which project folder you want to use for the upcoming session Note If you decide to use the same project folder each time you start Spotfire S you can turn off the dialog prompt by selecting the Always start in this project check box in the dialog To turn off the dialog prompt from within Spotfire S choose Options gt General Settings from the main menu click the Startup tab and clear the Prompt for project folder check box To specify your project folder do one of the following and then click OK in the dialog e Accept the default project folder The very first time you start Spotfire S this is the system default located in the users folder of the Spotfire S program folder Thereafter the default is the project folder you used for the previous session e Specify an existing project folder by typing its pathname in the text box or clicking Browse and navigating to it e Create a new project folder by typing a pathname in the text box Note For a folder to be used as a Spotfire S project folder it must contain a Data folder and a Prefs
218. ct the folder is filtering on a reference to it is placed in the Objects Copied To Folder box on the Objects page of the dialog Documents Select the classes of document objects you want to include in the folder Interface Objects Select the classes of interface objects you want to include in the folder Advanced page Database Filter group Search Working Chapter Only If selected Spotfire S filters only the working data and no other databases in the search path To filter other databases first clear this check box and then select the databases to filter in the Databases field Databases Select the databases you want Spotfire S to search from among the databases currently in the search path Only the objects that are found in these databases are displayed in the folder Classes Select the classes of objects to display in the folder In this field your selections appear as a comma delimited list The selections you make in the Data Objects group of the Folder page will modify the contents of this field To include all objects classes select the special key word All Include Derived Classes If selected objects that are derived from the classes you specify in the Classes field are included For example a design object is derived from a data frame object If you select this option and the folder is set to filter on data frame objects design objects are also displayed Archive Database Position Only Because database paths can
219. ctal representation with a backslash So for example if you didn t have the vertical bar on your keyboard you could specify it using 174 The ASCII character set with the octal representation can be found in standard programming texts When you press the RETURN key and it is clear to Spotfire S that an expression is incomplete for example the last character is an operator or there is a missing parenthesis Spotfire S provides a continuation prompt to remind you to complete the expression The default continuation prompt is Interrupting Evaluation of an Expression Error Messages Commands Window Basics Here are two examples of incomplete expressions that cause Spotfire S to respond with a continuation prompt gt 3 aa 1 63 034 156 Poj 1 3416 In the first example Spotfire S determined that the expression was not complete because the multiplication operator must be followed by a data object In the second example Spotfire S determined that c 3 4 1 6 was not complete because a right parenthesis is needed In each of the above cases the user completed the expression after the continuation prompt and then Spotfire S responded with the result of the evaluation of the complete expression Sometimes you may want to stop the evaluation of an S PLUS expression For example you may suddenly realize you want to use a different command or the output display of data on the screen is extremely l
220. ctory This option works in conjunction with the Shortcut page of the Spotfire S Properties dialog to determine the location of the project folder To open the Shortcut page right click the Spotfire S startup icon on the desktop select Properties and click the Shortcut tab Note By default this option is not selected and the Start in field on the Shortcut page is ignored The project folder is then determined by one of the following e The default behavior The value of S_PROJ in the Target field of the Shortcut page The value of S_DATA in the Target field of the Shortcut page If Set S_PROJ to Working Directory is selected the project folder is determined by the contents of the Start in field on the Shortcut page To avoid confusion it is recommended that you do not mix methods that is do one of the following e Do not select this option and use S_PROJ or S_DATA in the Target field to specify the location of the project and or data folder e Select this option and use only the Start in field to specify the location of the project folder without specifying S_PROJ or S_DATA in the Target field Register all OLE objects When selected all the objects you have linked or embeded OLE objects are registered Prompt for project folder When selected the dialog shown in Figure 11 5 opens each time you start Spotfire S allowing you to specify the project folder you want to use for the session To turn off this
221. cumsum and sum diff Create sequential differences max min Maximum and minimum pmax pmin Maxima and minima of several vectors mean Arithmetic mean median 50th percentile prod Product of elements of a vector quantile Compute empirical quantiles range Returns minimum and maximum of a vector sample Random sample or permutation of a vector 451 Chapter 8 Using the Commands Window Hypothesis Testing 452 Table 8 6 Common functions for summary statistics Continued sum Sum elements of a vector summary Summarize an object var Variance and covariance The summary function is a generic function providing appropriate summaries for different types of data For example for an object of class 1m created by fitting a linear model the returned summary includes the table of estimated coefficients their standard errors and t values along with other information The summary for a standard vector is a six number summary of the minimum maximum mean median and first and third quartiles gt summary stack loss Min 1st Qu Median Mean 3rd Qu Max 7 11 15 17 52 19 42 S PLUS contains functions for doing classical hypothesis testing as shown in Table 8 7 Table 8 7 S PLUS functions for hypothesis testing Test Description t test Student s one or two sample t test wilcox test Wilcoxon rank sum and signed rank sum tests chisq test Pearson
222. d Wilks A R 1988 The New S Language Wadsworth amp Brooks Cole Pacific Grove CA Burns Patrick 1998 Poetry Download for free from http www seanet com pburns Spoetry Chambers John 1998 Programming with Data Springer Verlag Krause A and Olson M 1997 The Basics of S and S PLUS Springer Verlag New York Lam Longhow 1999 An Introduction to S PLUS for Windows CANdiensten Amsterdam Spector P 1994 An Introduction to S and S PLUS Duxbury Press Belmont CA Data analysis Bowman Adrian and Azzalini Adelchi 1997 Smoothing Methods Oxford University Press Bruce A and Gao H Y 1996 Applied Wavelet Analysis with S PLUS Springer Verlag New York Chambers J M and Hastie TJ 1992 Statistical Models in S Wadsworth amp Brooks Cole Pacific Grove CA Efron Bradley and Tibshirani Robert J 1994 An Introduction to the Bootstrap Chapman amp Hall Everitt B 1994 A Handbook of Statistical Analyses Using S PLUS Chapman amp Hall London Hardle W 1991 Smoothing Techniques with Implementation in sS Springer Verlag New York Hastie T and Tibshirani R 1990 Generalized Additive Models Chapman amp Hall Huet Sylvie et al 1997 Statistical Tools for Nonlinear Regression with S PLUS Springer Verlag Help Support and Learning Resources Kaluzny S P Vega S C Cardoso T P and Shelly A A 1997 S SpatialStats User s Manual Spr
223. d additive logistic models for classification problems Tree models are fit by successively splitting the data to form homogeneous subsets The result is a hierarchical tree of decision rules useful for prediction or classification The Tree Models dialog is used to fit a tree model Fitting a tree model From the main menu choose Statistics gt Tree gt Tree Models The Tree Models dialog opens as shown in Figure 6 60 Tree Models BE Model Results Plot Prune Shrink Predict m Data m Fitting Options Data Set kyphosis x Min No of Obs Before Split Weights w 5 Subset Rows with e o Min Node Size 10 M Omit Rows with Missing Values Min Node Deviance 0 010 r Save Model Object Save As my tree Variables Dependent kyphosis v Independent Formula kyphosis Age N umber Start Create Formula Cancel Apply 4 current Help Figure 6 60 The Tree Models dialog Example The kyphosis data set has 81 rows representing data on 81 children who have had corrective spinal surgery The outcome Kyphosis is a binary variable and the other three columns Age Number and Start are numeric Kyphosis is a post operative deformity which is present Tree Tools Tree in some children receiving spinal surgery We are interested in examining whether the child s age the number of vertebrae operated on or the starting vertebra influence the likelihood of the ch
224. d be prepared to modify the model or abandon it altogether if it does not satisfactorily explain the data Residuals sqrt abs Residuals 0 4 0 6 0 8 1 0 1 2 1 4 0 2 J 23 45 T T T T T T 2 0 2 5 3 0 3 5 4 0 4 5 Fitted temperature q7 P 45 o o fo o o oo o oo o o o o o 8 0 2 0 2 5 3 0 fits 3 5 4 0 Figure 6 36 Seven plots created by the Linear Regression dialog 45 275 Chapter 6 Statistics ozone 2 0 2 5 3 0 3 5 4 0 45 Fitted temperature Residuals T T T T I 2 1 0 1 2 Quantiles of Standard Normal Figure 6 36 Seven plots created by the Linear Regression dialog Continued 276 Fitted Values Regression Residuals ozone f value 20 Cook s Distance 77 0 0 tll 0 20 40 60 ndex 80 100 Figure 6 36 Seven plots created by the Linear Regression dialog Continued 277 Chapter 6 Statistics 278 o ag o o o o 5 o o o ae o o 4 206 o o o o o oOo 2 o ono 2 ae o oO s o8 2 S go 8 o9 Qo O o oH o 8 0o z o 5 o o o 2 o o8 a o 0 o 9 88 o o o o O20 o o o o o o g o a Oe o x 8 o g o o o 6 oo o o o o o NA o T T T T 60 70 80 90 temperature Figure 6 36 Seven plots created by the Linear Regression dialog Continued The simplest and most informative method for
225. d earlier Spotfire S ships with a large number of sample data sets for your use in exploring Spotfire S You can display any of these data sets as well as any of your own data sets stored in the working data by using the Select Data dialog To display a data set stored in an S PLUS database do the following Data Set 1 From the main menu choose Data gt Select Data The Select Data dialog opens as shown in Figure 2 10 Select Data loli Source ExistngData Existing Data Name x C New Data C Import File SERED i I Show Dialog on Startup Cancel Apply current Figure 2 10 The Select Data dialog In the Source group the Existing Data radio button is selected by default 2 Inthe Name field of the Existing Data group either type the name of the data set you want to open or select its name from the dropdown list and click OK Hint Databases 24 You can also display a data set by double clicking its name in the Object Explorer For a detailed discussion of the Object Explorer see Chapter 7 Working With Objects and The data set last opened in a Data window or last selected in the Object Explorer is referred to as the current data set To change the current data set click in the Data window of the data set you want to make current or select it from the list at the bottom of the Window menu When no data set is explicitly referenced in an operation the current data set is the default
226. d functions are automatically stored by Spotfire S in internal databases Other types of objects like Graph Sheets and scripts exist only in the current session and must be saved to disk to be permanently stored We begin this chapter by describing the three types of S PLUS objects and examine how they relate to databases and files Next we take a close look at the Object Explorer a powerful interface for manipulating and visually organizing your objects into a meaningful structure Finally we end the chapter with a discussion of project folders and chapters and describe how to use these tools to organize your work into projects Understanding Object Types and Databases UNDERSTANDING OBJECT TYPES AND DATABASES S PLUS Object There are three basic types of objects in S PLUS Types Engine objects such as data frames functions and lists e Interface objects such as menu items toolbars and dialogs e Document objects such as Graph Sheets reports and scripts Engine Objects Engine objects are created and used by the Spotfire S interpreter during the execution of code These types of objects are automatically stored by Spotfire S in internal databases Vectors A vector is the most basic object in S PLUS It is a one dimensional array of data values of a single mode usually numbers Vectors may also contain other types of data such as character strings and logical variables Some statistical techniques produce vectors and the
227. d histogram we expect Yet every single data set is normal we know this because we generated the data from a normal distribution The lesson in this example is that we should not be overly concerned if we have a small data set and its histogram does not look exactly normal If we have reason to believe the data set is normally distributed the histogram needs to be very skewed for us to change our mind For large data sets the normal curve should be more evident in the histogram but even then we should not concern ourselves too much by slight variations from the normal curve that we expect to see Figure 6 89 was generated using 100 000 observations Try plotting some different distributions using large sample sizes say 1 000 or 10 000 observations Note the shapes of the different distributions 375 Chapter 6 Statistics REFERENCES 376 Box G E P Hunter W G amp Hunter J S 1978 Statistics for Experimenters New York Wiley Chambers J M Cleveland W S Kleiner B amp Tukey P A 1983 Graphical Methods for Data Analysis Belmont California Wadsworth Cleveland W S 1979 Robust locally weighted regression and smoothing scatterplots Journal of the American Statistical Association 74 829 836 Cleveland W S 1985 The Elements of Graphing Data Monterrey California Wadsworth Fleiss J L 1981 Statistical Methods for Rates and Proportions 2nd ed New York Wiley Friedman J H 1984
228. d in the File Name field Note After you make your selections on the Data Specs page of the Export To File dialog Spotfire S has the basic information necessary to export a data set For greater control over your exporting parameters use the Options and Filter pages discussed below The Options page of the Export To File dialog is shown in Figure 5 5 Export To File ioli Data Specs Options Filter General M Column names I Row Names M Quotes Formatting Format string oo nny Delimiter Date format Ma x Time format frimmsstt Cancel Apply current m Help Figure 5 5 The Options page of the Export To File dialog 175 Chapter 5 Importing and Exporting Data 176 Options page General group Column names When selected Spotfire S includes the column names of the data set as the first row in the file Row Names When selected Spotfire S includes the row names of the data set as the first column in the file Quotes When selected Spotfire S exports factor and character variables as character strings enclosed with quotation marks Options page Formatting group Format string When exporting to an ASCII text file specify the data types and formats for the exported columns For more information on the syntax accepted by this field see Notes on Importing and Exporting Files of Certain Types on page 190 Delimiter When exporting to an ASCII text fi
229. data perfectly However as the model grows more complex it begins to reflect the random variation in the sample obtained rather than a more general relationship between the response and the predictors This may make the model less useful than a simpler one for predicting new values or drawing conclusions regarding model structure The general strategy in regression is to choose a simpler model when doing so does not reduce the goodness of fit by a significant amount In linear regression and ANOVA an F test may be used to compare two models In logistic and log linear regression a chi square test comparing deviances is appropriate The Compare Models dialog lets you compare the goodness of fit of two or more models Typically the models should be nested in that the simpler model is a special case of the more complex model Before using the Compare Models dialog first save the models of interest as objects Comparing models From the main menu choose Statistics gt Compare Models The Compare Models Likelihood Ratio Test dialog opens as shown in Figure 6 62 Compare Models Compare Models Likelihood Ratio Test Select Model M Test Statistic Model Objects kyph ful kyph aa CE Name String Match M Chi Square i I Cp Model Class Im X Bo Jo Robust weld Bobustk r Results Save As IV Print Results Cancel Apply f current Figure 6 62 The Compare Mode
230. dialog 2 In the Source group click the New Data radio button 3 In the New Data group type a name for the new data set in the Name text box and click OK 17 Chapter 2 Working With Data Entering and Editing Data Typing data into a Data window is easy just do the following 1 Click the cell in which you want to enter a data value 2 Type the value 3 Press ENTER or an arrow key to enter the data in the cell Pressing ENTER enters the value in the cell and moves the cursor to the next cell the Spotfire S smart cursor feature moves the cursor in the direction of the last movement If you press an arrow key after typing a data value the cursor moves in the direction of the arrow Note 18 Spotfire S requires the columns of a data set to be of equal length and thus pads any shorter columns it encounters with NAs When you enter data into a new empty column Spotfire S assigns the column a type that most closely matches the type of data you enter The default column type for new columns is double for floating point double precision real numbers If you type character data into an empty column Spotfire S creates a factor column for categorical data To change the default column type for character data from factor to character do the following 1 From the main menu choose Options gt General Settings to open the General Settings dialog 2 Click the Data tab to display the Data page
231. dialog lets you create a subset of a data set based on a subsetting expression While the dialog provides tools for helping you write this expression some knowledge of S PLUS language syntax is required The Merge Two Data Sets dialog can be used to combine data from two data sets into a single data set The Tabulate dialog allows you to create a tabular summary of data from a data set Selected columns of the data set are identified as variables and the count of each combination of variable values is returned Numeric variables can be binned before the counting occurs The table of the counts can be printed and also returned in a data set suitable for multipanel conditioning plots For statistics and other summary information choose Statistics gt Data Summaries gt Crosstabulations The Expand Grid dialog lets you create a new data set containing all combinations of sets of values in an existing data set Each set of values may be either all unique values in a column or a specified number of equispaced values covering the range of values in a column This dialog is useful for producing columns representing a grid of values over which to evaluate a function or obtain predictions from a model The Random Sample of Rows dialog can be used to generate random samples or permute the observations in a data set 51 Chapter 2 Working With Data LIBRARIES INCLUDED WITH SPOTFIRE S All data sets in Spotfire S are stored in libraries W
232. dictors and adding random variation error The error is assumed to have a Gaussian normal distribution with constant variance and to be independent of the predictor values Linear regression uses the method of least squares in which a line is fit that minimizes the sum of the squared residuals Suppose a set of n observations of the response variable y correspond to a set of values of the predictor x according to the model f f where Y Yb Yo Yp and amp xX Xo X The ith residual r is defined as the difference between the ith observation y and the ith fitted value y Taos that is r y The method of least n bo Je ene 2 squares finds a set of fitted values that minimizes the sum gt rj i l If the response of interest is not continuous then logistic regression probit regression log linear regression or generalized linear regression may be appropriate If the predictors affect the response in a nonlinear way then nonlinear regression local regression or generalized additive regression may be appropriate If the data contain outliers or the errors are not Gaussian then robust regression may be appropriate If the focus is on the effect of categorical variables then ANOVA may be appropriate If the observations are correlated or random effects are present then a mixed effects or generalized least squares model may be appropriate 271 Chapter 6 Statistics 272 Other dialogs rela
233. dity depends on the assumption that the cell counts are at least moderately large Even when cell counts are adequate the chi square is only a large sample approximation to the true distribution of McNemar s statistic under the null hypothesis Compare Samples Performing McNemar s test From the main menu choose Statistics gt Compare Samples gt Counts and Proportions gt McNemar s Test The McNemar s Chi Square Test dialog opens as shown in Figure 6 24 McNemar s Chi Square Test _ oy x Data r Options Data Set mcnemar trial E IV Apply Continuity Correction Yerable 7 z Results a Save As IV Print Results Figure 6 24 The McNemar s Chi Square Test dialog Example The data set shown in Table 6 6 contains a contingency table of matched pair data in which each count is associated with a matched pair of individuals Table 6 6 Contingency table of matched pair data B Survive B Die A Survive 90 16 A Die 5 510 In this table each entry represents a pair of patients one of whom was given treatment A while the other was given treatment B For instance the 5 in the lower left cell means that in five pairs the person with treatment A died while the individual the person was paired with survived We are interested in the relative effectiveness of treatments A and B in treating a rare form of cancer A pair in the table for which one member of a matched pair s
234. double click the icon or label To select a topic within the category double click its question mark icon or the topic title e The Index tab lists available help topics by keyword Keywords are typically function names for S PLUS language functions and topic names for graphical user interface topics Simply type in a keyword and HTML Help will find the keyword that most closely matches it Click Display or double click the selected title to display the help topic e The Search tab provides a full text search for the entire help system Simply type in a keyword and all the help files containing that keyword are listed in a list box Select the desired topic and click Display or double click the selected title to display the help topic Using the right pane The right pane is where the help information actually appears It usually appears with both vertical and horizontal scrollbars but you can expand the HTML Help window to increase the width of the right pane Many help files are too long to be fully displayed in a single screen so choose a convenient height for your HTML Help window and then use the vertical scrollbars to scroll through the text Chapter 1 Introduction The right pane contains a search in topic feature To use it 1 Type CTRL F to open the Find dialog this dialog is a feature of HTML Help inherited from Internet Explorer t x Find what Find Next I Match whole word only BIEBER Cancel C Up
235. e 11 21 Color Schemes icl x User Colors Image Colors Standard p Cyan Magenta of Colors fa of Colors 4 of Shades 48 0 48 of Shades 3 0 3 Edit Colors Edit Colors Trellis r Topographical of Colors fja of Colors 5 of Shades 48 0 48 of Shades 1 15 18 4 Edit Colors Edit Colors m Trellis Black on White r User 1 of Colors f2 of Colors 2 of Shades fas of Shades 14 Edit Colors Edit Colors J White on Black User 2 of Colors B of Colors 2 of Shades fi 4 of Shades 14 Edit Colors Edit Colors p Figure 11 21 The Image Colors page of the Color Schemes dialog Image colors are a series of fill colors that can be used for draped surfaces flooded contours and levels plots The specification of image colors consists of up to sixteen core colors and a list defining the number of shades or color gradations between each core color Use the fields on the Image Colors page to set the number of colors and shades for each of the eight color schemes To modify the Image Colors for a color scheme click the Edit Colors button and use the Color dialog to edit the color palette for core image colors of Colors Specify the number of core colors to use in the image colors definition Only the number of colors specified in this field will be used in the image colors scheme of Shades This field is defined by a list of numbers separat
236. e DataTips IV Show Imported Data in View M Enable Smart Cursor IV Prompt on import overwrite M Butter Data Entry Date Time Input Default Text Col factor Date format Mas l Excel Link Time format h mm ss tt z M Save link information p Date Time Output IV Prompt on overwrite Date format Mid yyyy z M Remove data on doc close l Vaoliim fonas tt H IV Remove data on link close I Selection includes col names I Selection includes row names Cancel Figure 11 3 The Data page of the General Settings dialog Data Options group Show Auto Created Data in View When selected a new Data window automatically opens to display a data object created through one of the Data or Statistics dialogs When this option is turned off the data object appears in the Object Explorer Autoload Data Sets When a Graph Sheet is created Spotfire S automatically loads the corresponding data into a Data window when this option is selected 527 Chapter 11 Customizing Your Spotfire S Session 528 Enable DataTips When selected column descriptions if any appear in small pop up windows when you hover the mouse over column names in a Data window Enable Smart Cursor When selected the cursor always moves in the direction of the last movement when the ENTER key is pressed while entering data in cells Buffer Data Entry When selected a special buffer is used to speed up data editing It is recommended that
237. e Object Explorer click the Object Explorer button I on the Standard toolbar 9 Highlight the Data folder In the right pane double click the new data matrix called summary air the name we specified in Step 7 above This loads the summary statistics into a Data window We are done As you can see calculating summary statistics is straightforward Other statistical procedures use the same basic steps that we did in this example 206 Crosstabula tions Summary Statistics The Crosstabulations dialog produces a table of counts for all combinations of specified categorical factor variables In addition it calculates cell percentages and performs a chi square test for independence The Crosstabulations dialog returns results in an ASCII formatted table The chi square test for independence is useful when the data consist of the number of occurrences of an outcome for various combinations of categorical covariates It is used to determine whether the number of occurrences is due to the marginal values of the covariates or whether it is influenced by an interaction between covariates To construct a table of counts for use in further analysis use the Tabulate dialog available from the Data menu Computing crosstabulations From the main menu choose Statistics gt Data Summaries gt Crosstabulations The Crosstabulations dialog opens as shown in Figure 6 5 Crosstabulations Bie ks Model Options m Data r Res
238. e QQ normal with line plot is intended for comparing a single set of data with the quantiles of a distribution function by default the normal distribution The QQ plot is intended for comparing two sets of data and does not automatically display a distribution line To create a QQ normal with line plot for a single set of data 1 Select the y column 2 Click the Ka button on the Plots 2D palette 123 Chapter 4 Creating Plots To create the same plot without the distribution line click the a button on the Extra Plots palette QQ normal with line plot Ka 4000 7 3500 7 3000 4 2500 7 2000 7 3 2 1 0 1 2 Normal Distribution To create a QQ plot comparing two sets of data 1 Select the y1 and y2 columns to plot y2 against y1 2 Click the button on the Extra Plots palette QQ plot 1 074 e 0 8 4 e aE d 0 44 ge 0 2 o BP Pa 0 0 T 0 0 0 2 0 4 0 6 0 8 1 0 124 Plotting One Dimensional Data Probability A probability plot is similar to a QQ plot except that it compares your Plots data with the quantiles of a cumulative probability distribution function Probability plots can be created with or without a distribution line To create a probability plot with a distribution line for a single set of data fi probability Of x 1 Select the y column 2 Click the E button on the Plots 2D palette To create
239. e Report window and a histogram with a density line is plotted in a Graph Sheet Jackknife Inference Resample Example 2 In this example we obtain bootstrap estimates of mean and variation for the coefficients of a linear model The model we use predicts Mileage from Weight and Disp in the fuel frame data set 1 Open the Bootstrap Inference dialog 2 Type fuel frame in the Data Set field 3 Type the following in the Expression field coef Im Mileage Weight Disp data fuel frame 4 On the Options page type 250 in the Number of Resamples field to perform fewer than the default number of resamples This speeds up the computations required for this example 5 Click on the Plot page and notice that the Distribution of Replicates plot is selected by default 6 Click OK A bootstrap summary appears in the Report window In addition three histograms with density lines one for each coefficient are plotted in a Graph Sheet In the jackknife new samples are drawn by replicating the data leaving out a single observation from each sample The statistic of interest is calculated for each set of data and this jackknife distribution is used to construct estimates Performing jackknife inference From the main menu choose Statistics Resample gt Jackknife The Jackknife Inference dialog opens as shown in Figure 6 78 Jackknife Inference _ x Model Options Results Plot Data Save Model Object
240. e Spotfire S gt Modify Plots from the Excel main menu A dialog opens showing you a list of the graph areas in this graph you can have multiple graph areas in a graph that is one graph area might be 2D and another might be 3D in the same graph and for each graph area a list showing all the plots in this graph area Select the graph area and the plot in this area that you want to edit and then click Next A Spotfire S plot properties dialog opens in Excel allowing you to modify the properties of this plot 499 Chapter 10 Using Spotfire S With Other Applications Selecting data for graphs Before you can create a graph you must first select some data in your current worksheet You must select a block of data that is greater than one cell in width or length before you can continue with the Create Spotfire S Graph wizard Spotfire S plots accept data in a variety of formats Some plots require at least three columns of data with the three columns being interpreted as x y and zdata values Other plots require at least four columns of data with the four columns being interpreted as x y z and w data values The list of plot types on the last page of the Create Spotfire S Graph wizard indicates what kind of data specification is required If no x y z or w specification is shown for a plot type in the list that means it accepts x single or multiple columns or x and y data with single or multiple columns Warning
241. e a new column description or edit the existing description and click OK If you pause your mouse cursor over the name box in the column header a DataTip displays the column description as shown in Figure 2 14 i fuel frame iol x Weight Disp Mileage g Fuel Type Eagle Summit 4 2560 00 97 00 33 01 Miles Per Gallon Highway mall Ford Escort 4 2345 00 114 00 33 00 3 03 Small Ford Festiva 4 1845 00 81 00 37 00 2 70 Small RRAN PRAE anen nn nnn mnn aan cml Figure 2 14 A DataTip displays the column description A column list is a list of column names or numbers in a dialog field specifying a group or sequence of columns on which to operate For example selecting the column names Weight and Type produces the column list Weight Type Viewing and Formatting Data To create a column list simply select the column names using CTRL click if necessary from the dialog field s dropdown list Note Dialog fields display only column names not column numbers You can also create a column list in a dialog field by typing the column numbers separated by commas For example 1 3 4 refers to columns 1 3 and 4 To specify a sequence of columns type the beginning and ending column names or numbers separated by a colon For example 3 7 refers to columns 3 through 7 To specify all columns in a data set select the special key word lt ALL gt Changing the To increase or decrease a column s width
242. e assumption probably holds To check this assumption we calculate the variances exactly 1 Open the Summary Statistics dialog 2 Enter weight gain as the Data Set 3 Click on the Statistics tab and select the Variance check box 4 Click OK The following output appears in the Report window xxx Summary Statistics for data in weight gain gain high gain low Min 83 00000 70 00000 Ist Qu 106 25000 89 50000 Mean 120 00000 101 00000 Median 121 00000 101 00000 3rd Qu 130 25000 112 50900 Max 161 00000 132 00000 Total N 12 00000 12 00000 NA s 0 00000 5 00000 Variance 457 45455 425 33333 Std Dev 21 38819 20 62361 The actual variances of our two samples are 457 4 and 425 3 respectively These values support our assertion of equal variances We are interested in two alternative hypotheses the two sided alternative that U U 0 and the one sided alternative that Hyg Hz gt 0 To test these we run the standard two sample t test twice once with the default two sided alternative and a second time with the one sided alternative hypothesis greater 1 Open the Two sample t Test dialog 2 Type weight gain in the Data Set field Select gain high as Variable 1 and gain 1 ow as Variable 2 By default the Variable 2 is a Grouping Variable check box should not be selected and the Assume Equal Variances check box should be selected 229 Chapter 6 Statistics 230 4 Click Apply The result appears
243. e dropdown list Brush and Spin Graph Styles Changing Defaults and Settings Height Multiplier Specify an amount by which to multiply the height of the point The default value is 1 8 Line Weight Incr Select a line weight for nonsolid styles Panning group Use the Vertical Overlap and Horiz Overlap fields to specify a number between 0 and 990 to be used with the vertical and horizontal Pan buttons on the Graph Tools palette A smaller number corresponds to less overlap a larger number to more overlap The default value is 0 15 The Brush and Spin page of the Graphs dialog is shown in Figure 11 14 Graphs ox Options Interactive Brush and Spin Brush and Spin Font fail Font Size fi 2 Background Color L Black x Foreground Color J Bright wr x Figure 11 14 The Brush and Spin page of the Graphs dialog Use the Font Font Size Background Color and Foreground Color fields to specify the settings to be used in the Brush and Spin window Graph styles are used to initialize the properties of new Graph Sheets Two graph styles can be defined Color and Black and White To specify graph styles choose Options Graph Styles from the main menu and then select either Color or Black and White 541 Chapter 11 Customizing Your Spotfire S Session Options 542 Note that the options you select in the Color Style and Black and White Style dialogs can be changed in the Graph Sheet dialog Yo
244. e hypothesized distribution is continuous the Kolmogorov Smirnov test is more likely than the chi square test to reject the null hypothesis when it should be rejected The Kolmogorov Smirnov test is more powerful than the chi square test and hence is preferred for continuous distributions Performing Pearson s chi square test From the main menu choose Statistics gt Compare Samples gt One Sample gt Chi square GOF The One sample Chi Square Goodness of Fit Test dialog opens as shown in Figure 6 12 Compare Samples One sample Chi Square Goodness of Fit Test BBE m Data r Distribution Parameters Data Set face process x Minimun Variable fx x Mazmun J m Options Mean fo 8 Number of Classes Sidi Deviations a Cut Points Number of Parameters Estimated Hypotheses ape Distribution normal x eg of Freedom 7 J Results Wea cf Rreedar Save As E T IV Print Results Probability l Cancel Appt current Figure 6 12 The One sample Chi Square Goodness of Fit Test dialog Example In the previous section we created a data set called qcc process that contains a simulated process with 200 measurements Ten measurements per day were taken for a total of twenty days We can use the chi square goodness of fit test to confirm that qcc process is Gaussian 1 If you have not done so already create the qcc process data set with the instructions given
245. e not grouped and treated the same as single delimiters For example if the comma is a delimiter two commas are interpreted as a missing field Double quotes are treated specially They are always treated as an enclosure marker and must always come in pairs Data contained within double quotes are read as a single unit of character data Thus spaces and commas can be used as delimiters and spaces and commas can still be used within a character field as long as that field is enclosed by double quotes Double quotes cannot be used as standard delimiters Note that when importing from or exporting to ASCII Spotfire S truncates column names to 256 characters When importing dBASE and dBASE compatible files the file name and file type are often the only things you need to specify Column names and data types are obtained from the dBASE file However you can select a rectangular subset of the data by specifying starting and ending columns and rows An application that can support multiple tables or data sets such as Informix Microsoft Access Microsoft SQL Server Oracle SAS SigmaPlot SYBASE will support exporting multiple tables or data sets to a file Spotfire S currently only supports importing the first table from the file unless the file type is ODBC Formatted ASCII FASCII Files Format Strings Notes on Importing and Exporting Files of Certain Types You can use FASCII import to specify how each ch
246. e of the Object Explorer and select Find from the shortcut menu The Pattern field of the Find Objects dialog shown in Figure 7 11 takes as input a pattern Wildcards are acceptable and regular expressions can also be used Patterns from previous searches are saved and can be selected from the dropdown list Working With Objects Find Objects x m Find Pattern d I Regular Expression Results Eolder Found Objects Container Pae Cancel Help Figure 7 11 The Find Objects dialog Spotfire S searches all the attached databases in the search path and places shortcuts to all matching objects in the folder specified in the Folder field The Container field reflects the name of the Explorer Page containing the results folder Note The Find Objects feature only searches for interface objects and objects in attached databases If the database in which an object is stored is not in the search path the object will not be found Filtering on By using the unique filtering mechanism provided by folders you can Objects restrict the types of objects displayed in a folder to those of particular interest In addition organizing your objects with folders makes it easier for example to perform a data analysis or statistical model building task To set the filtering properties of a folder do one of the following e Click the folder s icon to select the folder and then click the Prop
247. e performed in Spotfire S It is not necessary to read this entire chapter before you perform a statistical analysis Once you ve acquired a basic understanding of the way statistics are performed you can refer directly to a section of interest We begin this chapter by presenting general information on using the statistics dialogs and devote the remaining sections to descriptions and examples for each of these dialogs Figure 6 1 displays many elements of the Spotfire S interface SS ee Oe Se Re Summary Statistics for data ini sir t ozone redietion tempereture wing 1 0000000 7 0000 2 300000 2 6207414 113 5000 7L 7 400000 2477030 104 0010 77 7927 9 930739 79 000000 9 700000 84 500000 11 500000 7 000000 20 700000 000000 111 000000 0 000000 0 000000 9 529969 3 599210 Meinn Urge Nonie Veser i Nanos d ero Nae Nae x coe aoon S e Figure 6 1 Statistics related menus and windows Introduction Statistics menu The Statistics menu gives you access to most of the statistical procedures available in Spotfire S The procedures are logically grouped with submenus that allow you to precisely specify the procedure you want to use For example in Figure 6 1 the menu tree for summary statistics is shown It is selected by choosing Statistics gt Data Summaries gt Summary Statistics Statistics dialogs The open dialog shown in Figure 6 1 entitled Summary Statistics and is u
248. e the name of the first column to x Open the Distribution Functions dialog Verify that the name of the data set appears in the Data Set field and select x as the Source Column 6 To calculate p values we leave the Probability result type selected Choose t from the Distribution pull down menu and enter 12 in the Deg of Freedom 1 field 7 Click OK A new column named Probability appears in the data set The column contains the cumulative probabilities 0 02877247 and 0 97122753 corresponding to the values in x To see more decimal places in the display highlight the Probability column and click the 0 Increase Precision button on the Data window toolbar To convert a computed probability into a p value we calculate p 2 1 F We multiply our result by 2 because our alternative hypothesis is two sided How we calculate p values from the cumulative probability function always depends on our alternative hypothesis H U lt Uy value F t H U gt W value 1 F t Hy Uy p value 2 1 F Here F t is the cumulative distribution function of the t distribution with v degrees of freedom For the statistic of 2 10 this gives us a p value of 2 1 0 97122753 0 05754494 369 Chapter 6 Statistics 370 Example 2 Calculating rejection regions Calculating a rejection region for a level test is straightforward Suppose we are testing if the variance of one population is larger t
249. ear in the Data Target dropdown list or if the list is blank you may need to configure one or more data targets To do so either use the ODBC applet available in the Control Panel or click the Add Targets or Modify Target button in the Export To ODBC dialog For more information on adding or modifying data targets see the online help 184 Importing From and Exporting to ODBC Tables Table Name By default Spotfire S creates a new ODBC table with the same name as the data set being exported If you prefer you can replace the default table name with a different name Note After you make your selections on the ODBC page of the Export To ODBC dialog Spotfire S has the basic information necessary to export an ODBC table For greater control over your exporting parameters use the Filter page discussed below The Filter page of the Export to ODBC dialog is shown in Figure 5 10 Export to ODBC olx ODBC Fiter Keep or drop columns M Preview column names Select columns ar X Keep selected C Drop selected Filter rows IV Preview row names Select Rows all gt S V Export Rownames Cancel Apply current Help Figure 5 10 The Filter page of the Export to ODBC dialog Filter page Keep or drop columns group Preview column names When selected Spotfire S populates the Select columns dropdown list with the column names of the data set you are exporting Select
250. eater than alternative The Wilcoxon rank sum test is used to test whether two sets of observations come from the same distribution The alternative hypothesis is that the observations come from distributions with identical shape but different locations Unlike the two sample t test this test does not assume that the observations come from normal Gaussian distributions The Wilcoxon rank sum test is equivalent to the Mann Whitney test For paired data specify signed rank as the type of Wilcoxon rank test Performing a two sample Wilcoxon rank test From the main menu choose Statistics gt Compare Samples gt Two Samples gt Wilcoxon Rank Test The Two sample Wilcoxon Test dialog opens as shown in Figure 6 15 Two sample Wilcoxon Test loli m Data m Hypotheses gt Data Set weight gain d Mean Under Null Hypothesis Variable 1 gain high v 0 Variable 2 faaniow z Alternative Hypothesis F two sided hd I Variable 2 is a Grouping Variable Options Test M Use Exact Distribution Type of Rank Test Rank Sum 3 oe f Signed Rank I Continuity Correction Results Save As IV Print Results Cancel Apply current Help Figure 6 15 The Two sample Wilcoxon Test dialog Example In Two Sample t Test on page 224 we conducted a test to see if the mean weight gain from a high protein diet differs from that of a low protein diet The two sample test was significant at the
251. eating quality control charts continuous grouped From the main menu choose Statistics gt Quality Control Charts gt Continuous Grouped The Quality Control Charts Continuous Grouped dialog opens as shown in Figure 6 74 Quality Control Charts Continuous Grouped op x Model Results Plot Data r Calibration Data Set facc process x Type Groups x Variable fk x Groups 1 23 45 x Group By Column 7 Saved OBEC z Group Column Day Save Calibration Object Gree Save As Chart Type Type Mean sbar Cancel Apply current Figure 6 74 The Quality Control Charts Continuous Grouped dialog Continuous Ungrouped Quality Control Charts Example In Kolmogorov Smirnov Goodness of Fit on page 219 we created a data set called qcc process that contains a simulated process with 200 measurements Ten measurements per day were taken for a total of twenty days In this example we create an xbar Shewhart chart to monitor whether the process is staying within control limits The first five days of observations are treated as calibration data for use in setting the control limits 1 If you have not done so already create the qcc process data set with the instructions given on page 220 2 Open the Quality Control Charts Continuous Grouped dialog Type qcc process in the Data Set field Select X as the Variable Select Day as the Group Column Select Groups as the Cal
252. eaturing low protein Do the two diets lead to differences in mean weight gain Consider the data in Table 6 1 which shows the weight gains in grams for two lots of female rats under the two diets The first lot consisting of 12 rats was given the high protein diet and the second lot consisting of seven rats was given the low protein diet These data appear in section 6 9 of Snedecor and Cochran 1980 Table 6 1 Weight gain data High Protein Low Protein 134 70 146 118 104 101 119 85 225 Chapter 6 Statistics 226 Table 6 1 Weight gain data Continued High Protein Low Protein 124 107 161 132 107 94 83 113 129 97 123 The high protein and low protein samples are presumed to have mean value location parameters Uy and uz and standard deviation scale parameters Oy and O respectively While you are primarily interested in whether there is any difference in the mean values you may also be interested in whether the two diets result in different variabilities as measured by the standard deviations This example shows you how to use Spotfire S to answer such questions Setting up the data The data consist of two sets of observations so they are appropriately described in Spotfire S as a data frame with two variables 1 Open an empty data set by clicking the New Data Set button B on the Standard toolbar 1607 gain high
253. ect is selected the menu option reads Save Selected Objects as Default The properties of the selected object are now saved as the default values The next time you create an object of this type it will use these new defaults 523 Chapter 11 Customizing Your Spotfire S Session General To specify general session settings choose Options gt General Settings Settings from the main menu The General Settings dialog consists of four tabbed pages of options discussed below General The General page of the General Settings dialog is shown in Figure 11 1 General Settings 5 x General Data Startup Computations m Prompts Closing Documents DDE Server Support IV Prompt to Save Graph Sheets IV Respond to DDE Requests I Prompt to Save Data Files I Old Format for DDE Request Remove Data from Database I7 Enable ToolTips Never Remove I Color Toolbar I Show Commit Dialog on Exit I Large Buttons Automation IV Echo ExecuteStrinal J Show EvecuteStrinal outpu IV Send Missings as VT_ERROR IV Enable Graph DataTips Figure 11 1 The General page of the General Settings dialog Prompts Closing Documents group Prompt to Save Graph Sheets When selected Spotfire S issues a dialog prompt whenever you close a Graph Sheet window displaying a new or modified Graph Sheet Note Graph Sheets like scripts and reports are transient document objects that exist only in your current session The pro
254. ect the columns you want to delete in the right pane and press DELETE To delete an object from your working data do one of the following e Select the object and press DELETE Working With Objects Select the object and click the Delete button X on the Object Explorer toolbar Select the object and choose Edit Clear from the main menu Right click the object and select Delete from the shortcut menu To delete an object shortcut do one of the following Select the object and press CTRL DELETE Select the object and choose Edit Delete Short Cut from the main menu Right click the object and select Delete Short Cut from the shortcut menu Note menu You can delete objects from your working data but you cannot delete objects stored in system databases For system objects only the Delete Short Cut selection is available on the shortcut 405 Chapter 7 Working With Objects and Databases ORGANIZING YOUR WORK Using Project Folders 406 If you work on several different projects simultaneously you may find it convenient to keep the data and results of each project separate Spotfire S project folders and chapters give you a way to do that making it easy to organize your work You can tell Spotfire S to display the dialog shown in Figure 7 16 each time you start the program Open S PLUS Project 1 x Open the PLUS project in which folder D Program Files Insightful splus6 users
255. ed by commas indicating how many shades to use between each core color For example if you specify three core colors black red and white Changing Defaults and Settings and 5 15 for number of shades a total of 23 colors will be used for the image colors scheme black five shades between black and red and fifteen shades between red and white Auto Plot You can choose whether to have your plots redrawn automatically Redraw after each change you make This feature is turned on by default but you may want to turn it off to save redraw time for computationally intensive or complicated plots The Auto Plot Redraw feature can be toggled on and off by choosing View Auto Plot Redraw from the main menu When you have it turned off and want to redraw a plot choose View gt Redraw Now from the main menu 549 Chapter 11 Customizing Your Spotfire S Session CUSTOMIZING YOUR SESSION AT STARTUP AND CLOSING Setting Your Startup Options 550 If you routinely set one or more options each time you start Spotfire S or you want to automatically attach library sections or S PLUS chapters you can store these choices and have Spotfire S set them automatically whenever the program starts When you start Spotfire S the following initialization steps occur 1 Basic initialization brings the evaluator to the point of being able to evaluate expressions Spotfire S then looks for the standard initialization file SHOM
256. ed onto a 3D plane Projection plots are useful for combining multiple 2D plots in 3D space and then rotating the results You can use either menus or drag and drop to create projection plots For details on creating projection plots see the online help Projection plot oa 04 05 05 Trellis Graphs TRELLIS GRAPHS Trellis graphs let you view relationships between different variables in a data set through conditioning A series of panels is displayed with each panel containing a subset of the data divided into intervals of a conditioning variable To create a scatter plot of y against x conditioned on z 1 Open the Plots 2D palette and then click the Set Conditioning Mode button on the Standard toolbar A yellow bar appears at the top of each plot button in the palette FUER _ oy x 2 Select the x y and z columns 3 Click the E button on the Plots 2D palette Trellis graph z 37 8 to 98 9 z 98 9 to 99 9 n e F410 2 e e bd e fF 5 2 0 0 to 25 0 2 25 0 to 37 8 10 gt N n Mi Pa 57 Pi 1000 3000 5000 7000 v For more examples of Trellis graphs see Trellis Graphs on page 96 159 Chapter 4 Creating Plots 160 IMPORTING AND EXPORTING DATA Introduction Supported File Types for Importing and Exporting Importing From and Exporting to Data Files Importing From a Data File Exporting to a Data File Importing From and Exportin
257. eet inside S PLUS Choose Open from the File menu and set Files of Type to Excel Worksheets M Show tips on startup Previous tip Next tip E Figure 1 2 A Tip of the Day You can also access the Spotfire S Tips of the Day at any time by choosing Help gt Tip of the Day from the main menu If you prefer to turn off this feature simply clear the Show tips on startup check box in the dialog You can find Spotfire S on the TIBCO Web site at www tibco com In these pages you will find a variety of information including e FAQ pages e The most recent service packs e Training course information e Product information Information on classroom use and related educational materials TIBCO Spotfire Educational Services offers a number of courses designed to quickly make you efficient and effective at analyzing data with Spotfire S The courses are taught by professional statisticians and leaders in statistical fields Courses feature a hands on approach to learning dividing class time between lecture and online exercises All participants receive the educational materials used in the course including lecture notes supplementary materials and exercise data on diskette Chapter 1 Introduction Technical Support Books Using Spotfire S 10 For technical support please visit http spotfire tibco com support and register for a support account General Becker R A Chambers J M an
258. elect the folder and click the Delete button x on the Object Explorer toolbar 389 Chapter 7 Working With Objects and Databases Customizing the Object Explorer 390 e Right click the folder s icon and select Delete from the shortcut menu As a safeguard Spotfire S prompts you to confirm the action Click Yes to delete the folder S PLUS AN Are you sure you want to delete the folder New Folder 7 oe The Object Explorer is a fully customizable interface To set your preferences open the Object Explorer dialog by doing one of the following From the main menu choose Format gt Object Explorer e Double click in the white space of the right pane of the Object Explorer Right click in the white space of the right pane of the Object Explorer and select Explorer from the shortcut menu Explorer page Doing any of the foregoing opens the Object Explorer dialog with the Explorer page in focus as shown in Figure 7 7 Object Explorer 1 oi x Explorer Right Pane Advanced Name Object Explorer Description kile Program Files lr 6 users melinda Prefs Obje IV Bitmap Tab Bar Cancel Daa current Figure 7 7 The Explorer page of the Object Explorer dialog Introducing the Object Explorer Name As with other document objects the name of the Object Explorer is the name of the file in which it is archived If you want to rename the Object Explorer for example to
259. els for the second column We now need to name the levels in our two factor columns In the Factor Names field type the following expression c Blend 1 Blend 2 Blend 3 Blend 4 Blend 5 CCAS BY Ca D Type penicillin in the Save In field and click OK In the third column of the penicillin Data window type the twenty yield values from Table 6 3 Rename the columns blend treatment and yield so that they match the names in the table Statistical inference We use the Friedman rank test to test the null hypothesis that there is no treatment effect 1 Open the Friedman Rank Sum Test dialog 2 Type penicillin in the Data Set field 243 Chapter 6 Statistics Counts and Proportions Binomial Test 244 3 Select yield as the Variable treatment as the Grouping Variable and blend as the Blocking Variable 4 Click OK A summary for the Friedman test appears in the Report window The p value is 0 322 which is not significant This p value is computed using an asymptotic chi squared approximation Spotfire S supports a variety of techniques to analyze counts and proportions Binomial Test an exact test used with binomial data to assess whether the data come from a distribution with a specified proportion parameter e Proportions Parameters a chi square test to assess whether a binomial sample has a specified proportion parameter or whether two binomial samples have the same propor
260. ensional Data 3 Click the Bubble Color button L on the Plots 2D palette As in a standard scatter plot V1 will be used as the x axis values and V2 as the y axis values The size of the symbols will vary with the value of V3 Similarly the symbol color will vary with the value of V4 with lighter values when V4 is larger 4 Now click the Color Scale Legend button W onthe Graph toolbar to add a legend relating the V4 value to the color of each point You can modify symbol size and color ranges on the Vary Symbols page of the Line Scatter Plot dialog The resulting plot is shown in Figure 3 33 p Ea v2 0 6 0 4 7 ip 0 2 7 ee rr rr ps io 0 0 T T T T V1 Figure 3 33 Bubble color plot of the sensors data In this data set the four variables V1 through V4 tend to move together For example as we move to the upper right of the plot the symbols are larger and lighter Another useful way to compare relationships is to create multiple scatter plots in the same graph This will plot multiple y variables sharing the same x variable 1 Select the first four columns of the sensors data 2 Click the Scatter button E on the Plots 2D palette Three scatter plots are created on the same graph 93 Chapter 3 Exploring Data 94 3 Click the Auto Legend button to add a legend The resulting graph is shown in Figure 3 34 v2 Figure 3 34 Multiple plo
261. ensity Plots 125 Pie Charts 127 Dot Plots 127 Bar Plots 128 Pareto Plots 130 Plotting Two Dimensional Data 132 Scatter and Line Plots 132 Curve Fitting Plots 134 Nonlinear Curve Fitting Plots 135 Smoothing Plots 136 Text as Symbols Plots 138 Y Series Plots 138 XY Pairs Line Plots 139 Grouped Box Plots 140 Grouped Bar Plots 140 Stacked Bar Plots 142 Polar Plots 143 Plotting Multidimensional Data 144 3D Scatter and Line Plots 144 Bubble and Color Plots 145 Bubble Color Plots 146 High Low Plots 146 117 Chapter 4 Creating Plots 118 Candlestick Plots Error Bar Plots Vector Plots Area Charts Scatterplot Matrices Contour Levels Plots Surface 3D Bar Plots Comment Plots Smith Plots Projection Plots Trellis Graphs 147 148 149 150 151 152 153 155 156 158 159 Introduction INTRODUCTION The Plot Palettes You probably need to create graphics for a variety of purposes some quick and dirty for your own use in visually exploring your data or evaluating a model some for sharing informally with colleagues and some highly refined for publication in journals or marketing materials Spotfire S offers a tremendous variety of plot types for all these uses In this chapter we present a pictorial overview of all the various plots you can create The Plots 2D Plots 3D and Extra Plots palettes contain buttons for quickly creating plots See Figure 4 1 below To create a plot simply select your data col
262. ensity corresponding to each x value and places it in a new Density column 10 Highlight the x column in the Data window and then CTRL click to simultaneously highlight the Density column 11 Open the Plots 2D palette and click on the Line plot button Spotfire S plots Density versus x in a Graph Sheet The above procedure can be followed to generate plots of many different distributions You can generate random numbers from a variety of distributions using the Random Numbers dialog Generating random numbers From the main menu choose Data gt Random Numbers The Random Numbers dialog opens as shown in Figure 6 88 Random Numbers and Distributions Random Numbers SDF1 ea man Sample Manmanm fo oma Z Loo Figure 6 88 The Random Numbers dialog Example One way to develop an intuitive sense for the shape of a distribution is to repeatedly plot histograms of sampled data Consider the shape of the normal density in Figure 6 87 If we state that a population distribution is normal we are saying that the data have this shape In other words if we sample 100 observations from the population and generate a histogram from the data we would expect the histogram to look similar to Figure 6 89 Let s test how well this works in practice 373 Chapter 6 Statistics 374 12000 7 10000 7 8000 4 6000 4 4000 4 2000 7 4 3 3 1 19 0 7 0 5 t7 2 9 4 1 5 3 Sample Figure 6
263. eparate Plot tab By default plots produced from the statistics dialogs are not editable If you prefer you can change the default behavior for statistics dialogs from traditional to editable graphics 1 From the main menu choose Options gt Graph Options The Graphs dialog opens as shown in Figure 6 2 2 Check the Create Editable Graphics option in the Statistics Dialogs Graphics group 3 Click OK Graphs 5 x Options Interactive Brush and Spin M Graph Sheet Initialization Graph Style Color w T Default to Draft Mode Default 2D Axes Linear v m Traditional Graphics Within Expressi x I Create Editable Graphics Auto Pages I Suppress Wamings Gath tee a F Condition Mode On Statistics Dialogs Graphics Condition Yars fi IV Create New Graph Sheet Snap to Grid Mi f2 T Use Printer Page Defaults Grids per Inch Resize with Graph T Resize Fonts with Graph T Resize Symbols with Graph Cancel Figure 6 2 The Options page of the Graphs dialog Help For faster performance we recommend creating complicated plots such as multipanel Trellis displays with traditional graphics To convert a non editable graph into an editable one right click the data part of the graph and select Convert to Objects from the context menu Introduction Saving Results A statistical model object may be created by specifying a name for the From an object in the Save
264. eracoes tira IV Enable Graph DataTips IV Show EvecuteStrinal output IV Send Missings as VT_ERROR Cancel Figure 2 8 The General page of the General Settings dialog 21 Chapter 2 Working With Data Setting this preference causes Spotfire S to automatically open the Save Database Changes dialog shown in Figure 2 9 whenever you end a session in which you have created or modified any data objects Save Database Changes x The following database objects have been created or modified OK during this session Current versions of selected objects will be saved in the database Cancel Jcreated modified Deselect All Select All IV Display Dialog On Exit Figure 2 9 The Save Database Changes dialog By default all the data objects created or modified during the current session are selected in the Save Database Changes dialog For each data set in the list do one of the following and then click OK e To save a new data set or a changed version of an existing data set leave its name highlighted e To discard a new data set or any changes made to an existing data set CTRL click its name to deselect it Note After setting this option in the General Settings dialog you can later disable it by clearing the Display Dialog On Exit check box in the Save Database Changes dialog Of course you can remove a data object from your working data at any time during a session by using the
265. eric columns are treated as interval scaled variables factors are treated as nominal variables and ordered factors are treated as ordinal variables Other variable types should be specified as such through the fields in the Special Variable Types group Calculating dissimilarities From the main menu choose Statistics Cluster Analysis gt Compute Dissimilarities The Compute Dissimilarities dialog opens as shown in Figure 6 63 329 Chapter 6 Statistics Compute Dissimilarities gt Data r Dissimilarity Measure Data Set ffuel frame x Metric euclidean x aj m Standardize Variables Variables p Special Variable Types Ordinal Ratio Ba Subset Rows with Log Ratio x I Omit Rows with Missing Values Asymm Binary 7 m Save Model Object Save As fuel diss Cancel Apply 4 current Help Figure 6 63 The Compute Dissimilarities dialog Example The data set fuel frame is taken from the April 1990 issue of Consumer Reports It contains 60 observations rows and 5 variables columns Observations of weight engine displacement mileage type and fuel were taken for each of sixty cars In the fuel frame data we calculate dissimilarities as follows 1 Open the Compute Dissimilarities dialog 2 Type fuel frame in the Data Set field 3 Type fuel diss in the Save As field 4 Click OK The dissimilarities are calculated and s
266. erm reflecting the influence of treatment In order to fit a nonlinear regression model we must specify the form of the nonlinear model the name of the data set and starting values for the parameter estimates Examination of Figure 6 43 suggests starting values of V 200 and K 0 1 treating all observations as a single group We fit a Michaelis Menten relationship between velocity and concentration as follows 1 Open the Nonlinear Regression dialog 2 Type Puromycin in the Data Set field 3 Type the Michaelis Menten relationship vel Vm conc K conc into the Formula field 4 Type the parameter starting values Vm 200 K 0 1 into the Parameters field 5 Click OK The following results appear in the Report window xxx Nonlinear Regression Model Regression Formula vel Vm conc K conc Parameters Value Std Error t value Vm 190 8050000 8 7644700 21 77030 K 0 0603863 0 0107682 5 60785 Residual standard error 18 6146 on 21 degrees of freedom Correlation of Parameter Estimates Vm K 0 776 The printed results provide parameter estimates standard errors and t values as well as the residual standard error and correlation of parameter estimates We now fit a model containing a treatment effect 1 Open the Nonlinear Regression dialog 2 Type Puromycin in the Data Set field 3 Type the Michaelis Menten relationship vel VmtdelV state treated conc K conc into the Formula field 4 F
267. error file For example to store errors from the update job in the file update txt use the following command Splus SBATCH logfile update slg output update txt update dat You can type your batch commands directly from the keyboard if you specify stdin as the inputfi Te as follows Splus SBATCH output update txt update dat USING THE SCRIPT AND REPORT WINDOWS Introduction The Script Window Working With Scripts Using Find and Replace Context Sensitive Help Script Window Features Automatic Matching of Delimiters Automatic Insertion of Right Braces Automatic Indentation Modifying Script Window Settings Time Saving Tips for Using Scripts History Log Dragging Graph Objects Into a Script Window Dragging Function Objects Into a Script Window The Report Window Printing a Script or Report 462 464 464 469 471 472 472 472 472 473 475 475 478 479 480 482 461 Chapter 9 Using the Script and Report Windows INTRODUCTION 462 With the Script window you can write scripts programs to automate the more repetitive aspects of analyzing data and creating graphs You use a Script window to edit your scripts Each Script window has an output pane that displays output from the running script and a program pane that is used to type in the commands that make up the script Scripts give you access to the S PLUS programming language You can write commands to be executed to import or export data transform dat
268. erties button on the Object Explorer toolbar e Click the folder s icon to select the folder and then choose Format gt Selected Folder from the main menu e Right click the folders icon and select Folder from the shortcut menu 397 Chapter 7 Working With Objects and Databases 398 Folder page Doing any of the foregoing opens the Folder dialog with the Folder page in focus as shown in Figure 7 12 The Folder page allows you to set a very general level of filtering Folder 1 ii rs Folder Objects Advanced Name Data Data Objects M Data MV Models I Functions Documents I Graphs I Scripts I Reports Cancel Apply current Figure 7 12 The Folder page of the Folder dialog Name The name of the folder To change the folder s name type a new name in this field Folder page Data Objects group The Data Objects group is for filtering on engine objects stored in the database s you select on the Advanced page of the dialog see page 401 Data If selected the folder filters on data objects Most of the data objects you will be interested in are data frames objects of type data frame and sometimes matrices matrix and vectors vector In general data objects are or are derived from one of these classes of objects Working With Objects Models If selected the folder filters on model objects Most of the model objects you will be interested in are derived from lists and
269. es several features designed to simplify typing S PLUS functions Each of these features can be enabled or disabled independently of the others The Script window automatically matches braces parentheses O brackets and single and double quotation marks and For example whenever you type a right parenthesis the editor automatically highlights the matching left parenthesis This behavior is the same for braces brackets and quotation marks Delimiter matching helps you ensure that the matches are as you intended After you type say a right parenthesis the cursor moves automatically to the matching left parenthesis and highlights it for a predetermined length of time by default 0 5 seconds or 500 milliseconds The cursor then moves to the space following the right parenthesis Any intervening keystrokes are buffered so that no keystrokes are lost if you keep typing while the matching parenthesis is being highlighted The length of time for highlighting can be changed By default the Script window searches through the entire script to find an automatic match Since this can be very time consuming for large scripts you can restrict the search to a specified number of characters When automatic insertion of right braces is enabled pressing ENTER after typing a left brace will result in the automatic insertion of a matching right brace two lines below and the cursor will be placed on the intervening line When au
270. es your system for the necessary version of Microsoft Excel and if detected automatically installs the Excel add in Note To disable the automatic installation of the Excel add in during Setup choose the Custom install option and clear Excel Add in from the list of components to install Note 496 The menu options and user interface you see in Microsoft Excel will be determined by the version of that application you are running If you choose not to install the Excel add in during the installation of Spotfire S you can install it at any later time by running Setup choosing the Custom install option and selecting Excel Add in from the list of components to install If the Excel add in is installed on your computer but the Spotfire S menu and toolbar do not appear in Excel you can enable the add in from within Excel To do so follow these steps 1 Start Excel 2 Create a new worksheet if one does not already exist 3 From the Tools menu choose Add Ins Using Spotfire S With Microsoft Excel 4 In the Add Ins dialog select the Spotfire S Add In check box as shown in Figure 10 10 and click OK Add Ins 2 x Add Ins available Analysis ToolPak IT Analysis ToolPak VBA Cancel I AutoSave I Conditional Sum Wizard Paes I Lookup Wizard I Microsoft Bookshelf Integration A emplate Utilities I Update Add in Links 4 5 PLUS Add In 5 PLUS Wizards which assist you in
271. esent their points in three dimensional space The contour plot is a 2D plot in which the third variable is represented by contour lines or fill colors A 3D scatter plot displays the variable values as locations in three dimensional space Sliced ball data We begin by examining a set of simulated data contained in the sliced ball sample data set The points are uniformly distributed within a sphere with the exception of a structural feature that we will attempt to discover 105 Chapter 3 Exploring Data First we create a scatterplot matrix and a Trellis plot with these data and then create 3D scatter plots Scatterplot matrix 1 Use the Select Data dialog to display the s1iced ba11 data in a Data window 2 Select all three columns 3 Click the Scatter Matrix button on the Plots 2D palette Each panel displays an apparently random ball of data as shown in Figure 3 43 1 0 0 5 0 0 0 5 1 0 J L f J f f i i i i J L i i OO o oo M 1 0 B amp Bq Sm So oo o A o 4 o 2 amp QB BP 99 o Son o 28 Pe o E E bo OF a Seps gepe W E E E E vi o eee Go ERE ee 0 BBY 092 50 80 Soe 0 0 2 Boe So R 8 o 200 PR g g o O amp o o 8 o o0 Pepe MH oe 88 So Bho 3078 Bos 0 5 ee 08 28 6 7 a g 008 Se J op G 1 0 1 07 o D o 8 o o E o Qo o o gt TE at GAT 0 57 og SVE Be o amp o 0 om 59 o Boos Sg o E BS E EIR EA es 99 Soa oo amp io bo J o0 8 2 0 BOM 68H Bro o 0D 0 O S 0
272. et To remove a row do one of the following e Click in the row and then click the Remove Row button on the Data window toolbar Select the row and then press the DELETE key or choose Cut from the Edit or shortcut menu e From the main menu choose Data gt Remove gt Row The Remove Rows dialog opens as shown in Figure 2 30 Remove Rows Big ks From Data Set ethanol X Rows E Cancel Appt K current Figure 2 30 The Remove Rows dialog Type the row number of the row you want to remove in the Rows text box and click OK Spotfire S provides toolbar buttons for performing quick sorts on whole data sets as well as a dialog that allows you to customize your sorting parameters 47 Chapter 2 Working With Data Quick Sorts To quickly sort all the columns of a data set in place by the column containing the active cell do the following e Click in the column you want to sort by and then click the Sort Ascending button 24 or the Sort Descending button z Ad as appropriate on the Data window toolbar Customized Sorts For greater control in specifying your sorting parameters use the Sort Columns dialog available through the Data menu The dialog allows you to e Specify whether to sort the entire data set or a subset of its columns Select more than one column to sort by When specifying multiple columns to sort by the data are first ranked according to the first column selected Then
273. et select a data set from the Data Set dropdown list or type a new name in this field to create a data set and select the desired columns from the Columns dropdown list Note The number of columns selected in the To group must match the number of columns selected in the From group Note also that existing data in target columns will be overwritten 5 By default columns are sorted in ascending order To sort in descending order select the Descending check box 6 Click OK Other Data In addition to the basic tools discussed so far the Data menu Manipulation provides many more useful data manipulation options What follows Opti is a brief description of those not already covered Chapter 6 gives prions examples using the Random Numbers Distribution Functions Tabulate and Random Sample tools For details on using all the data manipulation dialogs see the online help 49 Chapter 2 Working With Data Transpose Exchange Restructure Fill Recode Transform Create Categories Random Numbers 50 The Transpose Columns and Transpose Rows dialogs allow you to convert columns to rows and vice versa Use the Transpose Block dialog to transpose a block of text that is turn the block on its side The Exchange Columns and Exchange Rows dialogs let you trade the positions of columns or rows between different data sets Append The Append Columns dialog can be used to append a column of data to the end o
274. etected Presentation automatically installs the PowerPoint Presentation Wizard Wizard Note To disable the automatic installation of the PowerPoint Presentation Wizard during Setup choose the Custom install option and clear PowerPoint Presentation Wizard from the list of components to install Note The menu options and user interface you see in Microsoft Powerpoint will be determined by the version of that application you are running If you choose not to install the PowerPoint Presentation Wizard during the installation of Spotfire S you can install it at any later time by running Setup choosing the Custom install option and selecting PowerPoint Presentation Wizard from the list of components to install Removing the To remove the PowerPoint Presentation Wizard do the following PowerPoint 1 Run Spotfire S Setup choosing the Modify option Presentation Wizard 2 Select PowerPoint Presentation Wizard from the list of components 516 Using Spotfire S With Microsoft PowerPoint 3 Follow the on screen instructions Note If you install the PowerPoint Presentation Wizard during the installation of Spotfire S and later remove Spotfire S the PowerPoint Presentation Wizard is removed automatically Creating a To create a PowerPoint presentation using your Spotfire S Graph PowerPoint Sheets do the following Presentation 1 Click the PowerPoint Presentation button ea on the St
275. ethod of least squares and display the result superimposed on a scatter plot of the data Proceed as above when making a scatter plot except this time click the Linear Fit button Va on the Plots 2D palette rather than the Scatter button The result is shown in Figure 3 7 below tel gain i T T F T 0 85 0 60 0 35 0 10 0 15 0 40 0 65 diff hstart Figure 3 7 Scatter plot with least squares line of tel gain versus diff hstart Notice in the graph that the two outliers in the data appear to influence the least squares line fit by pulling the line downward and reducing its slope relative to the remainder of the data The least squares fit of a straight line is not robust in that outliers can have a large influence on the location of the line A robust method is one that is not influenced very much by outliers no matter how large To fit a robust line by a method called least trimmed squares LTS 65 Chapter 3 Exploring Data and display the result select the data and click the Robust LTS button on the Plots 2D palette The result is shown in Figure 3 8 below Save your Graph Sheet as exmain sgr 2 0 7 T 0 85 0 60 0 35 0 10 0 15 0 40 0 65 diff hstart Figure 3 8 Scatter plot of tel gain versus diff hstart with robust LTS line Compare Figure 3 7 to Figure 3 8 and note how much the two outliers influenced the least squares line Line fits with selected points deleted Since the lea
276. evels plots 1 Select the x y and z columns 2 Click the Plots 2D or Plots 3D palette button corresponding to the desired plot Contour plot k4 Plotting Multidimensional Data Levels plot E T Surface 3D Bar A surface plot draws a mesh or grid of your data in three dimensional space and a spline plot is a smoothed surface of gridded data A 3D bar plot is a gridded surface drawn with bars for two variables a 3D bar plot produces a binomial histogram showing the joint distribution of the data A color surface plot lets you specify color fills for the Plots You can create surface and 3D bar plots from either gridded or irregular data For more information see the online help bands or grids on a surface plot To create any of the 3D surface bar plots 1 Select the x y and z columns 2 Click the Plots 3D palette button corresponding to the desired plot 3D coarse surface plot g fox n A IS NN ne iS oe Be IZ ae ad SUS ND 153 Chapter 4 Creating Plots 3D filled data grid surface plot ae OY N WER KX WS NNN n re ak 3D filled spline surface plot or i Vie My We Wie N Ng Yip Wie a fn Tis Wis ies ine y MON ZR SS 154 3D 32 color surface plot Z Comment Plots Plotting Multidimensional Data 3D
277. example Line Plot x yl y2 takes a variable number of inputs where the first is interpreted as the x value and the rest as y values 6 In the Number of Inputs field in the Graph Setup Wizard specify the required number of inputs for your plot type 7 To create a Trellis plot select the Use Last Input for Conditioning Variables check box The last input may contain multiple columns all of which are used as conditioning variables 8 Click Finish to insert the component An empty rectangle appears in your Mathcad worksheet with one or more placeholders for your specified inputs 511 Chapter 10 Using Spotfire S With Other Applications 9 Specify appropriate inputs and the plot appears As an example suppose you want to create two linear fit scatter plots with a single conditioning variable 1 Define data points for variables x y1 y2 and cond in your Mathcad worksheet Insert a Spotfire S graph component From the Graph Setup Wizard click the Choose Axis Plot Type button In the Choose Graph and Plot Type dialog select Linear in the Axes Type list and Fit Linear Least Squares as the Plot Type Click OK In the Graph Setup Wizard type 4 in the Number of Inputs field and select the Use Last Input for Conditioning Variables check box Click OK Click in the input variable placeholders and enter your variable names x yl y2 and cond Click out of the component to activate the plot Forma
278. f another column Pack The Pack Columns dialog allows you to delete missing values in a column and shift the remaining values up to close the space Stack The Stack Columns dialog lets you stack separate columns of data into a single column with the values in the other columns replicated as necessary Unstack The Unstack Columns dialog can be used to break up a single column into several columns of specified lengths The Fill Numeric Columns dialog allows you to fill columns in a data set with NAs or with a series of generated numbers The Recode dialog lets you recode all occurrences of a specific value in specified columns to a new value The Transform dialog can be used to create a new variable based on a transformation of other variables The Create Categories dialog allows you to create new categorical variables from numeric continuous variables or to redefine existing categorical variables by renaming or combining groups The Random Numbers dialog lets you generate random numbers from a specified distribution Distribution Functions Split Subset Merge Tabulate Expand Grid Random Sample Manipulating Data The Distribution Functions dialog can be used to compute density values cumulative probabilities and quantiles from a specified distribution The Split Data by Group dialog allows you to split a data set into multiple new data sets based on the values of a splitting variable The Subset
279. faults and Settings The User Colors page of the Color Schemes dialog is shown in Figure 11 20 Color Schemes joialxt User Colors Image Colors Standard Cyan Magenta Background Color J Bright wr x Background Color _ Bright wr x Edit Colors Edit Colors m Trellis m Topographical Background Color L Gray x Background Color Bright wr z Edit Colors Edit Colors m Trellis Black on White gt p User 1 Background Color jf Bright Wr x Background Color Bright wr z Edit Colors Edit Colors m White on Black User 2 Background Color L Black x Background Color __ Bright Wr x Edit Colors Edit Colors Cancel Figure 11 20 The User Colors page of the Color Schemes dialog The User Colors scheme you select on the Options page of the Color Style or Black and White Style dialog is used to set the user colors in any newly created Graph Sheet These colors appear in the color lists for all of the graphical objects within the Graph Sheet as User1 User2 etc Use the fields on the User Colors page to set the background color for each of the eight color schemes To modify the User Colors for a color scheme click the Edit Colors button and use the Color dialog to edit the color palette 547 Chapter 11 Customizing Your Spotfire S Session Image Colors 548 The Image Colors page of the Color Schemes dialog is shown in Figur
280. feature and use the default project folder each time you start Spotfire S clear this check box or select the Always start in this project check box in the dialog Load Bigdata library When selected the bigdata library loads on startup If you work with a large data set and you do not select this option you could see unexpected results when you try to perform actions If your projects typically include large data sets then select this option to always load the bigdata library when you start 531 Chapter 11 Customizing Your Spotfire S Session 532 Open S PLUS Project 21x Open the PLUS project in which folder D Program Files Insightful splusB users meinda x Browse If you check this box this dialog will no longer appear at startup To get it back check the option in the Options General Settings Startup dialog box I Always start in this project e J ea Figure 11 5 The Open Spotfire S Project dialog Show splash screen When selected the Spotfire S splash screen appears when you start Spotfire S To turn off this feature clear this check box Update project prefs This check box controls whether your project s Prefs folder will be updated at startup to the latest version of preferences installed in the MasterPrefs folder of your Spotfire S program folder When selected a dialog prompts you if your Prefs folder needs to be updated If you elect to update your files a backup folder is created and
281. ferred to as parametric survival models In this context they are used throughout engineering to discover reasons why engineered products fail They are called accelerated failure time models or accelerated testing models when the product is tested under more extreme conditions than normal to accelerate its failure time The Parametric Survival and Life Testing dialogs fit the same type of model The difference between the two dialogs is in the options available The Life Testing dialog supports threshold estimation truncated distributions and offsets In addition it provides a variety of diagnostic plots and the ability to obtain predicted values This functionality is not available in the Parametric Survival dialog In contrast the Parametric Survival dialog supports frailty and penalized likelihood models which is not available in the Life Testing dialog Performing life testing From the main menu choose Statistics gt Survival Life Testing The Life Testing dialog opens as shown in Figure 6 59 319 Chapter 6 Statistics 320 Figure 6 59 Life Testing x Model Options Results Plot Predict r Data p Model Data Set capacitor x Distribution Weibull x Weights x Truncation Subset Rows with Eormula Create Subset Create Formula M Omit Rows with Missing Values ji Save Model Object eee Save As fi Threshold Parameter Methods Value 7 Value oY
282. file Printing a Script To print the contents of a Script window do the following File 1 Select the Script window to print 2 From the File menu choose Print Script 3 Use the common Print dialog to specify your print options and click OK the actual Print dialog you see will depend on which platform you use and which default printer is currently in use You can also print a script as follows 1 Click the Print button amp on the Standard toolbar 2 A dialog will pop up to confirm which Script window you want to print Print Script 2 Print the Script savetrees ssc i 3 Click Yes to print using the default printer and default settings or No to abort printing Stopping a Script While running a script or a selected portion of a script you can usually stop it using the ESC key This will prevent the script from being evaluated any further 467 Chapter 9 Using the Script and Report Windows Interpreting Errors and Warnings Selecting Text in a Script Window Clearing Cutting Copying and Pasting Text in a Script Window 468 If the interpreter encounters a problem with an expression or a command you enter in a script file it will display an error or warning in the output pane of the Script window The warning or error message explains the problem and in some cases likely causes of the problem You can then move to this line and edit the script to correct the problem and rerun the scri
283. fire S by establishing a link between the worksheet and an S PLUS data frame e The Excel add in application allows you to create and modify Spotfire S graphs from within Excel If you have Microsoft Excel 2000 or later installed on your computer you can create open and save Excel worksheets from within Spotfire S Then by creating a link between a specific cell range in Excel and a data frame in Spotfire S you can use the data stored in the Excel worksheet to create graphics or perform statistical analyses in Spotfire S To create an Excel worksheet from within Spotfire S do the following 1 Click the New button D on the Standard toolbar or choose File gt New from the main menu 2 In the New dialog select Microsoft Excel Worksheet and click OK To open an Excel worksheet from within Spotfire S do the following 1 Click the Open button on the Standard toolbar or choose File gt Open from the main menu 2 In the Open dialog select Excel WorkSheets xls or xlsx from the Files of type dropdown list navigate to the desired Excel file and click Open When you create or open an Excel worksheet it is displayed in a window inside Spotfire S as shown in Figure 10 1 Because this window is actually Excel embedded inside Spotfire S it gives you all the functionality you expect in Excel Using Spotfire S With Microsoft Excel H S PLUS my Excel data BEE File Edit View Insert Format Tools Data
284. fit a random effects ANOVA model to assess the within batch and between batch variation 1 Open the Random Effects Analysis of Variance dialog 2 Type pigment in the Data Set field 3 Enter the following Formula Moisture Batch Sample in Batch or click the Create Formula button and use the Formula builder to construct the formula Use the Special Terms group with Term Category of nested effect to create the term Sample in Batch 4 Click OK A summary of the model is printed in the Report window 302 Multiple Comparisons Analysis of Variance Analysis of variance models are typically used to compare the effects of several treatments upon some response After an analysis of variance model has been fit it is often of interest to determine whether any significant differences exist between the responses for the various treatment groups and if so to estimate the size of the differences Multiple comparisons provides tests for equality of effects and also estimates treatment effects The Multiple Comparisons dialog calculates simultaneous or non simultaneous confidence intervals for any number of estimable linear combinations of the parameters in a fixed effects linear model It requires the name of an analysis of variance model aov or linear model 1m and specification of which effects are of interest The multiple comparisons functionality is also available on the Compare page of the ANOVA dialog Performin
285. folder or object and click the Expand Item button or the Collapse Item button toolbar on the Object Explorer e Select the folder or object and choose View gt Expand Selected or View gt Collapse Selected from the main menu When you select an object in the left pane of the Object Explorer the right pane displays its immediate components You can choose from among four right pane views as shown in Figure 7 6 by clicking the corresponding button on the Object Explorer toolbar Introducing the Object Explorer Large Icons Ea Small Icons Ubpect Explorer slol ata E Giepte E Reports E Scripts T IN SeactPath z S SeactPath Details Ubyact Explorer x Uboct Explorer slol OD SeactPath m IN SeactPath Figure 7 6 Optional views in the right pane of the Object Explorer If the name of an object is truncated in the right pane display simply pause your cursor over it to display a ToolTip containing the full text ToolTips are displayed in both panes of the Object Explorer for any object that cannot be fully displayed at the current column or pane width 387 Chapter 7 Working With Objects and Databases In addition you can resize any column in the right pane of the Object Explorer by doing the following 1 Position your cursor on the vertical line to the right of the column heading you want to resize The cursor changes to a resize tool Obj Data Class Dimensions _ E D Pro
286. format Select the format you prefer to use when importing time data The available choices mirror those in your Windows Regional Options the default value for this field is the current Windows default Note To change the default delimiter for importing ASCII files choose Options gt General Settings from the main menu and then click the Data tab In the ASCII Import Export Options group type your preferred default in the Import Delimiter field The Filter page of the Import From File dialog is shown in Figure 5 3 Import From File 5 x Data Specs Options Filter m Columns to import Show names Select column FO z Filter columns Filter expression Alt col names Cancel Apply current Figure 5 3 The Filter page of the Import From File dialog Filter page Columns to import group Select Columns Specify the columns you want to import by clicking the Show Names button and selecting the columns from the list in this field 173 Chapter 5 Importing and Exporting Data Exporting to a Data File 174 Filter page Filter columns group Filter expression By specifying a query or filter expression you can subset the data you import By default this field is blank resulting in all the data being imported For more information on the syntax accepted by this field see Filter Expressions on page 187 Alt col names If the data file you are importing either does not cont
287. from a nearly normal distribution and there is no indication of outliers The plots for the low protein group which we do not show support the same conclusions Statistical inference Is the mean weight gain the same for the two groups of rats Specifically does the high protein group show a higher average weight gain From our exploratory data analysis we have good reason to believe that Student s t test provides a valid test of our hypotheses As in the one sample case you can obtain confidence intervals and hypothesis test p values for the difference u Mo between the two mean value location parameters u and us To do this we use the Two sample t Test and Two sample Wilcoxon Test dialogs Each two sample test is specified by a hypothesis to be tested the confidence level and a hypothesized ug that refers to the difference of the two sample means However because of the possibility that the two samples may be from different distributions you may also specify whether the two samples have equal variances To determine the correct setting for the option Assume Equal Variances you can either use informal inspection of the variances and box plots or conduct a formal F test to check for equality of variance If the heights Compare Samples of the boxes in the two box plots are approximately the same then so are the variances of the two samples In the wei ght gain example the box plots indicate that the equal varianc
288. function to use In such cases you can fit a nonparametric curve which does not assume a particular type of nonlinear relationship Nonparametric curve fits are also called smoothers since they attempt to create a smooth curve showing the general trend in the data The simplest smoothers use a running average where the fit at a particular x value is calculated as a weighted average of the y values for nearby points with the weighting given to each point decreasing as the distance between its x value and the x value of interest increases In the simplest type of running average smoother all points within a certain distance or window from the point of interest are used in the average for that point The window width is called the bandwidth of the smoother Making the bandwidth wider results in a smoother curve fit but may miss rapidly changing features Making the bandwidth narrower allows the smoother to track rapidly changing features more accurately but results in a rougher curve fit 69 Chapter 3 Exploring Data More sophisticated smoothers add variations on this approach such as using smoothly decreasing weights or local linear fits However all smoothers have some type of smoothness parameter bandwidth controlling the smoothness of the curve The issue of good bandwidth selection is complicated and has been treated in many statistical research papers You can however get a feeling for the practical consequences of varying t
289. functionality in your own application or within Spotfire S Application Developer s Guide Are familiar with the S language and Spotfire S and you are looking for information about creating or editing graphics either from a Commands window or the Windows GUI or using Spotfire S supported graphics devices Guide to Graphics Are familiar with the S language and Spotfire S and you want to use the Big Data library to import and manipulate very large data sets Big Data User s Guide Want to download or create Spotfire S packages for submission to the Comprehensive S PLUS Archive Network CSAN site and need to know the steps Guide to Packages vi Spotfire S documentation Continued Information you need if you See the Are looking for categorized information about individual S PLUS functions Function Guide If you are familiar with the S language and Spotfire S and you need a reference for the range of statistical modelling and analysis techniques in Spotfire S Volume 1 includes information on specifying models in Spotfire S on probability on estimation and inference on regression and smoothing and on analysis of variance Guide to Statistics Vol 1 If you are familiar with the S language and Spotfire S and you need a reference for the range of statistical modelling and analysis techniques in Spotfire S Volume 2 includes information on multiva
290. g 389 inserting 388 filtering using folders 397 finding objects in 396 folders in collapsing 386 deleting 389 expanding 386 filtering 397 saving as the default 402 inserting 389 left pane of 386 object shortcuts in deleting 405 objects in collapsing 386 copying 404 creating 402 deleting 404 dragging and dropping 404 editing 404 expanding 386 moving 404 selecting 403 viewing 403 opening 385 overview of 385 right pane of 386 changing the view in 392 resizing columns in 388 Index assigning 429 collapsing 386 copying using the Object Explorer 404 creating using the Object Explorer 402 deleting using the Object Explorer 404 displaying 430 dragging and dropping in the Object Explorer 404 editing using the Object Explorer 404 expanding 386 finding 396 listing 430 managing 429 model 206 moving using the Object Explorer 404 objects function for 430 removing 430 search path for 381 382 383 384 396 401 selecting in the Object Explorer 403 storing 430 types of document 380 Graph Sheets 381 Object Explorers 381 reports 381 scripts 381 engine 379 data frames 379 functions 380 sorting columns in 388 lists 380 Object Explorer button 385 matrices 379 Object Explorer toolbar 386 other 380 objects vectors 379 561 Index 562 interface 380 vectors arithmetic for 434 viewing using the Object Explorer 403 objects function 430 object shortcuts deleting in the Object Explorer 405 ODBC data so
291. g With Data Removing a column deletes the entire column and shrinks the size of the data set To remove a column do one of the following e Click in the column and then click the Remove Column button igi on the Data window toolbar Select the column and then press the DELETE key or choose Cut from the Edit or shortcut menu e From the main menu choose Data gt Remove gt Column The Remove Columns dialog opens as shown in Figure Remove Columns o al xi From Data Set ethanol ki Columns NOx X Cancel Appt K current Figure 2 28 The Remove Columns dialog Select the column you want to remove from the Columns dropdown list and click OK Clearing and Clearing a row deletes the data in the row but otherwise leaves the Removing Rows row s position and name if any intact To clear a row do one of the following e Click in the row and then click the Clear Row button on the Data window toolbar e Select the row and choose Clear from the Edit or shortcut menu 46 Sorting Data Manipulating Data From the main menu choose Data gt Clear gt Row The Clear Rows dialog opens as shown in Figure 2 29 Clear Rows Bek From Data Set ethanol X Rows E Cancel Appt current Figure 2 29 The Clear Rows dialog Type the row number of the row you want to clear in the Rows text box and click OK Removing a row deletes the entire row and shrinks the size of the data s
292. g multiple comparisons From the main menu choose Statistics gt ANOVA P Multiple Comparisons The Multiple Comparisons dialog opens as shown in Figure 6 51 303 Chapter 6 Statistics Multiple Comparisons Of x Model Selection r Options Model Object anova blood X Method Tukey bd Name String Match Confidence Level 10 95 Bounds upper and lowe Eror Type famiy wise fad Levels Of diet Adjust For v Comparison Type mca x Contrast Matrix Eompare fie Level X Critical Point Variable r Results Save As rt Simulation Size ee Print Results Scheffe Bank M Validity Check IM Estimability Check I Plot Intervals Cancel Apply f current Help Figure 6 51 The Multiple Comparisons dialog Example In One Way Analysis of Variance on page 234 we performed a simple one way ANOVA on the blood data set listed in Table 6 2 These data give the blood coagulation times for four different diets In Fixed Effects ANOVA on page 300 we revisited the blood data set and concluded that diet affects blood coagulation times The next step is to generate multiple simultaneous confidence intervals to see which diets are different from each other We can do this using either the Compare page on the ANOVA dialog or the Multiple Comparisons dialog 1 Ifyou have not done so already create the blood data set with the instructions
293. g to ODBC Tables The ODBC Data Source Administrator ODBC Drivers Defining a Data Source Importing From an ODBC Table Exporting to an ODBC Table Filter Expressions Variable Expressions Sampling Functions Notes on Importing and Exporting Files of Certain Types ASCII Delimited ASCII Files dBASE Files Files With Multiple Tables Formatted ASCII FASCI Files Informix Files Lotus Files Microsoft Access Files Microsoft Excel Files Oracle Files SYBASE Files 162 163 168 168 174 179 179 180 180 181 183 187 187 189 190 190 190 190 191 193 193 193 193 193 194 161 Chapter 5 Importing and Exporting Data INTRODUCTION 162 One easy method of getting data into Spotfire S for plotting and analysis is to import the data from another source Spotfire S can read and write a wide variety of data formats making importing and exporting data easy Spotfire S also supports the Open DataBase Connectivity ODBC standard allowing you to import and export between Spotfire S and any ODBC compliant database You can also import data from the leading financial databases Bloomberg FAME and MIM for statistical and graphical analysis To do this you must load the financedb library from the main menu by navigating to File gt Load Library or from the Commands window gt library financedb This library contains all the relevant functions for importing and exporting data including customizing the dialog
294. given on page 236 2 Ifyou have not done so already perform the one way analysis of variance on page 235 and save the results in the object anova blood 3 Open the Multiple Comparisons dialog 304 Analysis of Variance 4 Select anova blood as the Model Object from the pull down menu 5 We want to compare the levels of diet using Tukey s multiple comparison procedure Select diet from the pull down menu for Levels Of and set the Method to Tukey 6 Click OK to generate the multiple comparisons The Report window displays the result 95 simultaneous confidence intervals for specified linear combinations by the Tukey method critical point 2 7987 response variable time intervals excluding 0 are flagged by Estimate Std Error Lower Bound Upper Bound A B 5 00e 000 1 33 226 sel eo EAER A C 7 00e 000 1 52 11 38 2 120 Sees A D 8 93e 014 1 45 4 06 4 060 B C 2 00e 000 Lowe EA 1 820 B D 5 00e 000 128 1 42 5 580 ees C D 7 00e 000 es 3 42 LOD Sars From the above results and from the plot of the confidence intervals we can see that diets A and D produce significantly different blood coagulation times than diets C and B 305 Chapter 6 Statistics MIXED EFFECTS Linear 306 Mixed effects models are regression or ANOVA models that include both fixed and random effects The Linear Mixed Effects Models dialog fits a linear mixed effects model in the formulation of Laird and Ware 1982 but allo
295. gram Files Ins 1 attached 0 E splus 2 attached 2383 El stat 3 attached 265 E data 4 attached 226 El trellis 5 attached 187 El nlme3 6 attached 771 E menu attached 215 E squi 8 attached 505 EJ winspj 9 attached 21 Emain 10 attached 0 2 Do one of the following e Double click to automatically widen the column to the width of its longest entry e Drag the resize tool to the right to increase the column width or to the left to decrease the width To sort any column of information displayed in the right pane simply click the column s header Inserting and As noted earlier you can add any number of Explorer Pages to the Deleting Object Explorer To access a specific page simply click its tab in the Expl or r Pages lower left hand corner of the Object Explorer window To insert an Explorer Page do one of the following e Click the Insert Page button EF on the Object Explorer toolbar From the main menu choose Insert gt Explorer Page e Right click in the white space of either pane of the Object Explorer and select Create Explorer Page from the shortcut menu Doing any of the foregoing opens the Explorer Page dialog You can use this dialog to format your Explorer Pages as discussed on page 393 or click OK to accept the defaults and insert the page 388 Inserting and Deleting Folders Introducing the Object Explorer To delete an Explorer Page first click its tab and then do one of the follow
296. gression gt Local Loess The Local Loess Regression dialog opens as shown in Figure 6 41 Local Loess Regression _ yx Model Options Results Plot Predict m Data Data Set Puromycin ii Weights hd Subset Rows with r Save Model Object Omit Rows with Missing Values Save As Variables Dependent feone X Independent Formula Joonc vel state Create Formula Cancel Apply current Help Figure 6 41 The Local Loess Regression dialog Example The data set Puromycin has 23 rows representing the measurement of initial velocity of a biochemical reaction for 6 different concentrations of substrate and two different cell treatments Nonlinear Regression on page 288 describes these data in detail and discusses a theoretical model for the data Before fitting a theoretical model we can use the Local Loess Regression dialog to fit nonparametric smooth curves to the data Our model consists of a separate curve for each treatment group We predict the response conc by the variables vel and state Since state is a factor this fits a separate smooth curve in vel for each level of state 1 Open the Local Loess Regression dialog 2 Type Puromycin in the Data Set field 287 Chapter 6 Statistics Nonlinear Regression 288 3 Type conc vel state in the Formula field Alternatively select conc as the Dependent variable and CTRL click to select vel and state as
297. h combination of factors between your variables and generates statistics for the table e Correlations calculates correlations or covariances between variables These three procedures can be found under the Statistics gt Data Summaries menu In addition the Data Tabulate dialog provides a tabular summary of data and also creates a data set convenient for use in Trellis graphs The Summary Statistics dialog provides basic univariate summaries for continuous variables and it provides counts for categorical variables Summaries may be calculated within groups based on one or more grouping variables Computing summary statistics From the main menu choose Statistics gt Data Summaries gt Summary Statistics The Summary Statistics dialog opens as shown in Figure 6 4 Summary Statistics Summary Statistics BBE Data Statistics m Data r Results Data Set air X Save As fsummary air Variables ALL M Summarize Categorical Variables ozone radiation IV Print Results temperature Ll Group Variables Mevinum Unique Numen 10 Number of Bins for Wumenc value Cancel Appt K current Figure 6 4 The Summary Statistics dialog Example We use the data set air This data set measures the ozone concentration wind speed temperature and radiation of 111 consecutive days in New York In this example we calculate summary statistics for these data 1 Open the Summary Statistics di
298. h and then click Next to start the Graph Setup Wizard as shown in Figure 10 16 Asis Type Linear Plt Type Le with Scaler Choose Axis Plot Type Figure 10 16 The Graph Setup Wizard Using Spotfire S With MathSoft Mathcad 4 The default plot type is a linear line with scatter plot which takes one input This plots a single column vector against a set of indices If you want the default plot type click Finish If you want to change the plot type click the Choose Axis Plot Type button to open the Choose Graph and Plot Type dialog as shown in Figure 10 17 Choose Graph and Plot Type x Axes Type Plot Type High Density Line Plot x y1 y2 High Density Horiz x y1 y2 High Density Y Zero x y1 y2 High Low Plot x open close high low Candlestick x open close high low Histogram x Histogram with Density Line x Levels Plot x y 5 or 21 2n Line Plot x y1 y2 Line Plots BV Baie x1 y1 x2 y2 Line with Isolated e KYL y2 Lnx Line with Scatter x yas Ln Ln z Line with Tert as cmt fx v text 3 Cancel Figure 10 17 The Choose Graph and Plot Type dialog 5 Select an axis type from the Axes Type list and a plot type from the Plot Type list and then click OK Note The argument list after each plot type specifies how the data must be input for a particular type Many plot types accept a variable number of inputs For
299. h is displayed in Figure 3 45 N3 0 500 0 000 0 500 Figure 3 45 Three dimensional scatter plot of the sliced ball data Note that unlike the previous plot this plot uses 3D axes reflecting the location of points in three dimensional space Rotating 3D plots A single 3D scatter plot gives one view on the point cloud To gain an understanding of the overall structure it s useful to look at the point cloud from multiple viewing angles We can rotate the axes to view the points from a different angle 1 Click an empty space within the graph region to select the 3D workbox Four green circles and a green triangle appear If only a solitary green knob appears you have clicked on the plot rather than the workbox try again Visualizing Multidimensional Data 2 Drag horizontally on one of the green circles A bounding box will appear and rotate in the direction you drag the mouse When you release the mouse the graph is redrawn at the new perspective The green triangle can be dragged up and down to rotate the graph vertically Experiment with rotating the graph Multiple panels It s often useful to look at the point cloud from different angles simultaneously We can do this by using a multipanel plot with different rotations of the data in each panel 1 Click within the graph to select the graph region Eight green boxes appear along the border of the graph to indicate that the graph is selected 2 Click the 6
300. h of twenty five students We use factor analysis to look for structure in the scores 1 5 6 If you have not done so already choose File gt Load Library to load the examp1e5 library This library contains a few example data sets that are not in the main Spotfire S databases Open the Factor Analysis dialog Type testscores df in the Data Set field Specify that we want 2 factors in the Number of Factors field Select lt ALL gt in the Variables field Click OK A summary of the factor analysis appears in the Report window For investigations involving a large number of observed variables it is often useful to simplify the analysis by considering a smaller number of linear combinations of the original variables For example Multivariate scholastic achievement tests typically consist of a number of examinations in different subject areas In attempting to rate students applying for admission college administrators frequently reduce the scores from all subject areas to a single overall score Principal components is a standard technique for finding optimal linear combinations of the variables Performing principal components From the main menu choose Statistics Multivariate gt Principal Components The Principal Components Analysis dialog opens as shown in Figure 6 72 Principal Components Analysis ap x Model Results Plot Predict m Data r Model Data Set ftestscores df 7 Scaling
301. han the variance of a second population That is the null and alternative hypotheses are 2 2 2 2 Ay Oj gt Oo Suppose we calculate an F statistic of f 3 9 from the sample data and suppose that the two samples are of sizes m 12 and n 10 For a level 0 05 test we reject the null hypothesis if fZFoam 1 n 1 F005 11 9 10 find the rejection region we must calculate the quantile at 1 0 05 0 95 for the F distribution with 11 and 9 degrees of freedom 1 Open an empty data set by clicking the New Data Set button E on the Standard toolbar 2 Enter the value 0 95 in the first row of the first column Change the name of the first column to x 3 Open the Distribution Functions dialog 4 Verify that the name of the data set appears in the Data Set field and select x as the Source Column To calculate quantiles check the Quantile result type Select f from the Distribution pull down menu Enter 11 and 9 respectively in the Deg of Freedom 1 and Deg of Freedom 2 fields 7 Click OK A new column named Quantile appears in the data set The column contains the quantile 3 102485 corresponding to the value in x Thus the rejection region for the F distribution with 11 and 9 degrees of freedom at the 0 05 level is 3 102485 To see more decimal places in the display highlight the Quantile column and click the Increase t 0 Precision button 0 on the Data window toolbar Random Numbers and Distributions S
302. hange the background color of the Object Explorer and other windows Completely customize your graphs through various options styles and color schemes Have Spotfire S automatically set certain options or perform certain tasks each time you start or end a session You can even tailor the Spotfire S interface itself by using customizable menus and toolbars For more information on menus and toolbars see Chapter 8 Extending the User Interface in the Application Developer s Guide Changing Defaults and Settings CHANGING DEFAULTS AND SETTINGS Object In Spotfire S you can define your own default for any type of object Defaults including symbols plots titles Graph Sheets and data objects To do so 1 Select an object of the type for which you want to define the default 2 Modify the object s properties to match the exact specifications you want to save as the default for that type of object Save your changes for example by clicking OK in a dialog 4 Select the object if it is not already selected 5 Do one of the following e From the main menu choose Options gt Save Object as Default Right click the object and select Save Object as default from the shortcut menu The actual name of the selected object replaces the word Object in the menu option For example if you select the x axis title on a graph and change the font the menu option reads Save X Axis Title as Default When more than one obj
303. hapter 11 Customizing Your Spotfire S Session Setting Your SPLUS EXE S_FIRST options digits 4 options expressions 128 Due to operating system specific line length limitations or for ease of use you can also place the commands in a file and put the filename on the command line preceded by the symbol Echo options digits 4 options expressions 128 gt c myInitialization txt SPLUS EXE S_FIRST c myInitialization txt You can also combine several commands into a single S PLUS function and then set S_FIRST to this function For example gt startup lt function options digits 4 options expressions 128 You can call this function each time you start Spotfire S by setting S_FIRST as follows SPLUS EXE S_FIRST startup Variables cannot be set while Spotfire S is running just at initialization Any changes to S_FIRST will only take effect upon restarting Spotfire S When Spotfire S quits it looks in your data folder for a function Closing called Last If Last exists Spotfire S runs it A Last function can Opti be useful for cleaning up your folder by removing temporary objects ptions or files For example gt Last lt function dos paste del getenv S_Tmp Tmp dept Trans T Note Last 552 If the Last function contains errors when you quit the Spotfire S GUI these errors are reported in a dialog box Click OK to close the GUI and then restart to correc
304. hat the gains in new residence telephone extensions were at their very lowest during the first two years of the 14 year span that they rose rapidly in the third year and that the gain had increasingly wide up and down swings starting in year 6 Line with isolated points plot To see the points more clearly you can create a line plot with scatter plot points isolated from the lines To make this graph 1 Click any point or line in the plot to select the plot 79 Chapter 3 Exploring Data 80 2 Click the Isolated Points button L on the Plots 2D palette The resulting graph is shown in Figure 3 22 2 0 4 o RS Figure 3 22 Line with isolated points plot of tel gain Multiple line plots Now that you have seen the time series behavior of tel gain you may be interested in seeing that of diff hstart as well with the line plots for the two time series in the same graph You can do this in one of two ways as follows The first way is appropriate if you have already made one of the line plots and want to add the second The second way is appropriate if you want to make both of the line plots at once Adding a second line plot If you have already made the first line plot as we did above then you can just add the second line plot in the usual way 1 Click inside the graph region but not on the plot to select the existing graph Eight green boxes appear along the border of the graph to indicate that the g
305. hat you want to add another predictor you can press the up arrow to recall the command and then edit it to read gt Im Mileage Weight Disp data fuel frame Now click the Commands History button BE on the Standard toolbar The Commands History dialog shown in Figure 8 1 displays a list of your previously issued commands and gives you another way to edit them Note that you can also use the Commands History dialog for searching and executing your commands Commands History x File Search Help Previous Commands MV Echo in Commands Window MV Append upon Save Save As Figure 8 1 The Commands History dialog Commands Window Basics Getting Help in If you need help at any time during a Spotfire S session you can Spotfire S obtain it easily with the and help functions The function has simpler syntax it requires no parentheses in most instances For example gt 21m opens the 1m help file shown in Figure 8 2 below Both and help display help files in HTML format E S PLUS Language Reference iof x Ae e o amp amp Show Back Foward Refresh Print Options ln 5 PLUS Language Reference Fit Linear Regression Model DESCRIPTION Returns an object of class 1m or mlm that represents a linear model fit USAGE lm formula data lt lt see below gt gt weights lt lt see below gt gt subset lt lt see below gt gt na action na fail method qr model F x F y F contrasts N
306. have to double the backslashes because Spotfire S treats the backslash as an escape character 439 Chapter 8 Using the Commands Window Editing Data 440 Other data objects can be read from ASCII files as well particularly tables of data that can be used to create data frames For example suppose you have a data file auto dat containing the following information Model Price Country Reliab Mileage Type AcuralIntegra4 11950 Japan 5 NA Small Audi1005 26900 Germany NA NA Medium BMW325i6 24650 Germany 94 NA Compact ChevLumina4 12140 USA NA NA Medium FordFestiva4 6319 Korea 4 of Smal Mazda929V6 23300 Japan 5 21 Medium MazdaMX 5Miata 13800 Japan NA NA Sporty Nissan300ZXV6 27900 Japan NA NA Sporty OldsCalais4 9995 USA 2 3 Compact ToyotaCressida6 21498 Japan 3 23 Medium You can read this into an S PLUS data frame using the read table function as follows gt auto lt read table auto dat header T The optional argument header T tells Spotfire S to use the first line of the file for variable names You can also read ASCII files with the importData function using type ASCII After you have created an S PLUS data object you may want to change some of the data you have entered For editing data objects use the Edit data function which opens the data in a Spotfire S Data window To edit S PLUS functions the easiest way to modify the data is to use the Edit function which dumps the function into a Spotfire S Scrip
307. he Commands Window 414 Hypothesis Testing Statistical Models Defining Functions Using Spotfire S in Batch Mode 452 454 457 459 Introduction INTRODUCTION S PLUS is a rich language developed specially for exploratory data analysis and statistics The Commands window is a window on the Spotfire S programming environment giving you direct access to interactive programming in the powerful S PLUS language Note Throughout this chapter we use the term Spotfire S to denote the product We use S PLUS to denote the language In this chapter we can only present a brief introduction to the S PLUS language For a thorough treatment of both the language and how to program in it be sure to consult the Programmer s Guide 415 Chapter 8 Using the Commands Window COMMANDS WINDOW BASICS Entering Expressions 416 To open the Commands window do one of the following From the main menu choose Window gt Commands Window Click the Commands Window button on the Standard toolbar You use the Commands window by typing expressions at the prompt and then pressing the RETURN key Spotfire S responds typically with a value although sometimes as when creating graphics by simply returning a new prompt in the Commands window while creating the graphic in a Spotfire S Graph Sheet Among the simplest S PLUS expressions are arithmetic expressions such as the following eee 1
308. he bandwidth by actually using some smoothers on real data This section describes how to use three different types of smoothers kernel smoothers spline smoothers and loess smoothers and select their bandwidths to control the degree of smoothness of your curve fit or smooths of the data We will use the sample data set sensors which contains the responses of eight different semiconductor element sensors to varying levels of nitrous oxide NOx in a container of air The engineers who design these sensors study the relationship between the responses of these eight sensors to determine whether using two sensors instead of one allows a more precise measurement of the concentration of NOx Prior investigation has revealed that there may be a nonlinear relationship between the responses of the two sensors but not much is known about the details of the relationship Kernel Smoothers A kernel smoother is a generalization of local averaging in which 70 different weight functions kernels may be used to provide a smoother transition between points than is present in simple local averaging The default kernel is a box which provides the local averaging approach described in the introduction We will make a scatter plot of sensor 5 versus sensor 6 and experiment with the bandwidth of a simple moving average smoother sometimes called a boxcar smoother We begin by using the 2D graph capabilities to simultaneously make the sca
309. he new default by doing one of the following e With the Object Explorer in focus and no objects selected in either pane choose Options Save Window Size Properties as Default from the main menu Right click in the white space of the right pane of the Object Explorer and select Save Object Explorer as default from the shortcut menu Introducing the Object Explorer e Right click in the white space of the right pane of the Object Explorer and select Save from the shortcut menu From the main menu choose File gt Save e Click the close button X in the upper right hand corner of the Object Explorer window When Spotfire S prompts you to save the Object Explorer in a file click Yes You can also customize a new Object Explorer that you created and save it as your new default simply use the first or second method above Note The Object Explorer is a document object and the name of a document object is the name of the file in which it is saved Because Spotfire S looks for a file named Object Explorer sbf found in the Prefs folder of your Spotfire S project folder to use as the default you cannot rename the Object Explorer and use your renamed version as the default Opening the If you want to have Spotfire S automatically open the default Object Explorer Object Explorer at startup do the following at Startup 1 From the main menu choose Options gt General Settings 2 Click the Startup tab in
310. he response 1 2 3 7 Open the Multivariate Analysis of Variance dialog Type wafer in the Data Set field Click the Create Formula button to open the Formula builder While holding down the CTRL key select pre mean and post mean in the Variables list Click the Response button to add these variables to the Formula as the response Select maskdim Scroll through the Variables list until etchtime appears Hold down SHIFT and select etchtime This selects all columns between maskdim and etchtime Click the Main Effect button to add these variables to the Formula as predictors Click OK to dismiss the Formula builder The Formula field of the MANOVA dialog contains the formula you constructed Click OK A summary of the MANOVA appears in the Report window 345 Chapter 6 Statistics QUALITY CONTROL CHARTS Continuous Grouped 346 Quality control charts are useful for monitoring process data Continuous grouped quality control charts monitor whether a process is staying within control limits Continuous ungrouped charts are appropriate when variation is determined using sequential variation rather than group variation It is also possible to create quality control charts for counts the number of defective samples and proportions proportion of defective samples The Quality Control Charts Continuous Grouped dialog creates quality control charts of means xbar standard deviations s and ranges r Cr
311. hed working with a particular chapter simply Chapter detach it by doing one of the following In the right pane of the Object Explorer right click the icon of the chapter you want to detach and select Detach Database from the shortcut menu At the confirmation prompt click OK 409 Chapter 7 Working With Objects and Databases e From the main menu choose File gt Chapters gt Detach Chapter In the Detach Chapter dialog see Figure 7 18 select the database you want to detach and click OK Detach Chapter x p hi OK D Program Files Insightful splus6 users me Cancel Figure 7 18 The Detach Chapter dialog Note Spotfire S prohibits you from detaching any of the system databases Selecting a New To select a new working chapter in a Spotfire S session that is Working Chapter already running open the New Working Chapter dialog by doing the following e From the main menu choose File gt Chapters gt New Working Chapter The New Working Chapter dialog opens as shown in Figure 7 19 New Working Chapter xi Chapter Folder D Program Files Insightful splus6 users jeff 7 Browse To create and attach a new working chapter enter a folder name in the field above Label regression Cancel Help Figure 7 19 The New Working Chapter dialog 1 In the Chapter Folder text box do one of the following e To attach an existing working database type the pathname
312. hen we speak of S PLUS however we usually mean the executable program and the objects in the libraries that are attached automatically at startup The Spotfire S distribution contains additional libraries To see a list and a description of each at the command prompt type library Your default text editor opens and displays the information You can attach a library from the main menu by clicking File gt Load Library or by using the library function from the Commands window see Chapter 8 Many of these libraries including the robust library and the libraries contributed by Frank Harrell and Brian Ripley include graphical user interfaces Others such as the examples and example5 directories contain simple command line functions Note In addition to loading libraries from the menu or from the Commands window you can find and download packages From the main menu click File Find Packages select the repository URL or local file path and then from the list select the package to download You can also get the latest versions of packages you have downloaded using the File Update Packages menu command See the Guide to Packages for more information about using packages 52 As an example of what you can do with these libraries attach the maps library and try a few of its commands in the Commands window library maps map county Washington Create a map of Washington map Create a ma
313. ht lt 150 Note When constructing a filter expression be sure to type the variable name on the left side of the relational operator For example type Age gt 12 rather than 12 lt Age 188 You can also use the wildcard characters for single characters and for strings of arbitrary length to select subgroups of character variables For example the expression account 22 selects all rows for which the account variable is six characters long and ends in 22 The expression id 3 selects all rows for which id starts with 3 regardless of the length of the string To import specific row numbers use the built in variable rownun For example the expression rownum lt 200 imports the first 199 rows of a data file Filter Expressions Sampling Three functions that permit the random sampling of data are Functions available for use in filter expressions samp rand allows for simple random sampling This function accepts the single numeric argument prop where 0 lt prop lt 1 Rows are selected randomly with a probability equal to prop samp fixed selects a random sample of fixed size This function accepts two numeric arguments sample size and total observations The first row is drawn with a probability of sample size total observations The ith row is drawn with a probability of sample size 7 total observations i where i 1 2 sample size samp syst performs a syste
314. i Square Test _ oy x Data m Options Data Set mantel raw X IV Apply Continuity Correction Variable 1 Group ha Results Variable 2 Passive 7 Saves I Print Results Stratification Variable Smoker X m Cancel Apply current Figure 6 25 The Mantel Haenszel s Chi Square Test dialog Example The data set shown in Table 6 7 contains a three way contingency table summarizing the results from a cancer study The first column indicates whether an individual is a smoker In the second column Case refers to an individual who had cancer and Control refers to an individual who did not have cancer The third column indicates whether an individual is a passive smoker A passive smoker is a person who lives with a smoker so it is therefore possible for a person to be considered both a smoker and a passive smoker The fourth column indicates the number of individuals with each combination of Smoker Group and Passive values 253 Chapter 6 Statistics 254 Table 6 7 A three way contingency table summarizing the results of a cancer study Smoker Group Passive Number Yes Case Yes 120 Yes Case No 111 Yes Control Yes 80 Yes Control No 155 No Case Yes 161 No Case No 117 No Control Yes 130 No Control No 124 Setting up the data We use the Orthogonal Array Design dialog to create a mantel trial data set containing the information in
315. i x m Data r Options Data Set catalyst X Function mean Subset Rows with I Omit Rows with Missing Values Variables Dependent Yield v Independent T emp Lonc Cancel Appt current Figure 6 31 The Design Plot dialog Example The catalyst data set comes from a designed experiment Its eight rows represent all possible combinations of two temperatures Temp two concentrations Conc and two catalysts Cat The fourth column represents the response variable Yield We are interested in determining how temperature concentration and catalyst affect the Yield Prior to fitting an ANOVA model we can use various plots to examine the relationship between these variables We start with a design plot 1 Open the Design Plot dialog 2 Type catalyst in the Data Set field 3 Select Yield as the Dependent variable Factor Plot Experimental Design 4 CTRL click to select Temp Conc and Cat as the Independent variables 5 Click OK A design plot appears in a Graph Sheet This plot has a vertical bar for each factor and a horizontal bar indicating the mean of Yield for each factor level A factor plot consists of side by side plots comparing the values of a variable for different levels of a factor By default box plots are used See the plot factor help file for details Creating a factor plot From the main menu choose Statistics Design gt Factor Plot The Factor Plot dia
316. iables The order of the selected variables is important because the order determines how Spotfire S graphs the data A similar dialog allows you to select variables for conditioning the graph you create Selecting data for conditioning graphs When using the Create Spotfire S Graph wizard in Step 2 you can specify variables to use for conditioning the graph you are creating 507 Chapter 10 Using Spotfire S With Other Applications 508 Create S PLUS Graph Step 2 of 4 Conditioning x Conditioned graphs allow you to view your data in a series of panels where each panel contains a subset of the original data The subset in each panel is determined by intervals of values of the conditioning variable s Variables Selected variables Move On Use the Move button to move the variables to the Selected variables list Use the Up and Drn buttons to set the order of the selected variables Use the Del button to remove selected variables Cancel lt lt Back Next gt gt Einish Figure 10 14 The Create Spotfire S Graph Conditioning dialog A conditioned graph allows you to view your data in a series of panels where each panel contains a subset of the original data The subset in each panel is determined by the levels of the conditioning data range you select You can skip conditioning by leaving the Selected variables list in this dialog empty Handling errors during graph creatio
317. iables and options for the procedure you have chosen These are slightly different for each dialog Click the OK or Apply button to conduct the analysis If you click OK the dialog closes when the graph is generated if you click Apply the dialog remains open 4 Check for messages If a message is generated it appears in a Message window If no Message window opens then no message was generated 5 Check the result If everything went well the results of your analysis are displayed in a Report window Some statistics procedures also generate plots If you want you can change the variables parameters or options in the dialog and click Apply to generate new results Spotfire S makes it easy to experiment with options and to try variations on your analysis Dialogs Most of the statistical functionality of Spotfire S can be accessed through the Data and Statistics menus The Data menu includes dialogs for tabulating data calculating distribution functions and generating random samples and random numbers The Data menu also includes dialogs for manipulating data including sorting stacking and transforming data sets See Chapter 2 Working With Data for detailed information The Statistics menu includes dialogs for creating data summaries and fitting statistical models Many of the dialogs consist of tabbed pages that allow for a complete analysis including model fitting plotting and prediction Each dialog has a correspond
318. ialog The Distribution is normal by default 2 Select qcc process as the Data Set 3 Select X as the Variable 4 Click OK A summary of the goodness of fit test appears in the Report window The p value of 0 5 indicates that we do not reject the hypothesis that the data are normally distributed The summary also contains estimates of the mean and standard deviation for the distribution The Message window contains a warning indicating that the Dallal Wilkinson approximation used in this test is most accurate for extreme p values p values lt 0 1 Our actual calculated p value is 0 776 which is set to 0 5 in the summary to indicate that the null hypothesis is not rejected but our estimate of the p value is not highly accurate 221 Chapter 6 Statistics Chi Square Goodness of Fit 222 The chi square goodness of fit test uses Pearson s chi square statistic to test whether the empirical distribution of a set of observations is consistent with a random sample drawn from a specific theoretical distribution Chi square tests apply to any type of variable continuous discrete or a combination of these If the hypothesized distribution is discrete and the sample size is large n gt 50 the chi square is the only valid test In addition the chi square test easily adapts to the situation in which parameters of a distribution are estimated However for continuous variables information is lost by grouping the data When th
319. ibrary Highlight example5 in the Library Name list and click OK This library contains a few example data sets that are not in the main Spotfire S databases Open the Autocorrelations and Autocovariances dialog Type lynx df in the Data Set field Select lynx as the Variable Click OK Figure 6 81 displays the resulting autocorrelation plot The peaks at 10 and troughs at 5 reflect a ten year cycle ACF 0 0 Series lynx df lynx plier 10 5 10 15 20 Lag Figure 6 81 Autocorrelation plot of the 1ynx df data Autoregressive integrated moving average ARIMA models are useful for a wide variety of time series analyses including forecasting quality control seasonal adjustment and spectral estimation as well as providing summaries of the data 361 Chapter 6 Statistics 362 Fitting an ARIMA model From the main menu choose Statistics gt Time Series gt ARIMA Models The ARIMA Modeling dialog opens as shown in Figure 6 82 ARIMA Modeling BBE Model Options Diagnostics Forecast m Data r Initial Parameters Object flor af x T Enter Initial Parameter Values Variable flor X gt ARIMA Model Order Hic Autoregressive p fi r Other Predictors Difference d fo I Add Covariates Moving Avg q fo BIE z ARIMA Model Periodicity m Results Seasonality C None 1 Save As Quarterly 4 I Print Results Monthly 12
320. ibration Type NDA eR w CTRL click to select 1 2 3 4 5 from the Groups list box 8 Click OK A Shewhart chart of the X data grouped by Day appears in a Graph Sheet The Quality Control Charts Continuous Ungrouped dialog creates quality control charts of exponentially weighted moving averages ewma moving averages ma moving standard deviations ms and moving ranges mr These charts are appropriate when variation is determined using sequential variation rather than group variation Creating quality control charts continuous ungrouped From the main menu choose Statistics Quality Control Charts gt Continuous Ungrouped The Quality Control Charts Continuous Ungrouped dialog opens as shown in Figure 6 75 347 Chapter 6 Statistics Counts and Proportions 348 Figure 6 75 Quality Control Charts Continuous Ungrouped of xi Model Results Plot m Data _ Calibration ______ Data Set ace process x Type Self x Variable fx F Saved Object z Chart Type p Save Calibration Object Type Mean xbar Save As ayy Averaging Options Averaging Method Exp Wt Moving Sigma Method fstdDev F Span for Sigma R Span for Stat e o o Exp Weight fo 25 Cancel Appt f current The Quality Control Charts Continuous Ungrouped dialog Example For this example we ignore the fact that qcc process contains grouped data
321. iding existing groups producing a hierarchical structure that displays the order in which groups are merged or divided Divisive methods start with all observations in a single group and proceed until each observation is in a separate group Cluster Analysis Performing divisive hierarchical clustering From the main menu choose Statistics Cluster Analysis gt Divisive Hierarchical The Divisive Hierarchical Clustering dialog opens as shown in Figure 6 68 Divisive Hierarchical Clustering Model Results Plot Data r Dissimilarity Measure Data Set state z Metric euclidean gt Variables eee la I Standardize Variables F r Save Model Object Frost Save As rs Subset Rows with fs M Saye Data M Save Dissimilarities M Omit Rows with Missing Values r Dissimilarity Object I Use Dissimilarity Object d Object saved Cancel Apply K current Help Figure 6 68 The Divisive Hierarchical Clustering dialog Example In K Means Clustering on page 330 we clustered the information in the state df data set using the k means algorithm In this example we use a divisive hierarchical method 1 me fe oS 5 If you have not done so already choose File gt Load Library to load the examp1e5 library This library contains a few example data sets that are not in the main Spotfire S databases including state df Open the Divisive Hierarchica
322. igure 6 43 suggests starting values of Vm 160 and delV 40 while the previous model suggests K 0 05 Type the starting values Vm 160 delV 40 K 0 05 into the Parameters field 5 Click OK The following results appear in the Report window xxx Nonlinear Regression Model Formula vel Vm delV state treated conc K conc Parameters Value Std Error t value Vm 166 6010000 5 80726000 28 68840 delV 42 0245000 6 27201000 6 70032 K 0 0579659 0 00590968 9 80863 291 Chapter 6 Statistics Generalized Linear Models 292 Residual standard error 10 5851 on 20 degrees of freedom Correlation of Parameter Estimates Vm delV delV 0 5410 K 0 6110 0 0644 The printed results provide parameter estimates standard errors and t values as well as the residual standard error and correlation of parameter estimates The magnitude of the statistic for delV confirms that the treatment affects the maximum velocity Generalized linear models are generalizations of the familiar linear regression model to situations where the response is discrete or the model varies in other ways from the standard linear model The most widely used generalized linear models are logistic regression models for binary data and log linear Poisson models for count data Fitting a generalized linear model From the main menu choose Statistics P Regression gt Generalized Linear The Generalized Linear Models dialog opens as shown
323. ild having a deformity We fit a classification tree to the data in which a tree structure is used to classify individuals as likely or unlikely to have kyphosis based on their values of Age Number and Start The resulting classification tree divides individuals into groups based on these variables 1 Open the Tree Models dialog 2 Type kyphosis in the Data Set field 3 Specify Kyphosis Age Number Start in the Formula field 4 Type my tree in the Save As field A tree model object is saved under this name which we explore in a later example using Tree Tools 5 Click OK A summary of the model is printed in the Report window and a tree plot is displayed in a Graph Sheet Spotfire S provides a rich suite of tools for interactively examining a regression tree To use Tree Tools first use the Tree Models dialog to create a tree model Save the tree model by specifying a name in the Save As field of the dialog All of the Tree Tools begin by creating a plot of the specified tree model The Browse Burl Histogram Identify and Snip tools let you select splits or nodes on the plot and provide information on the selection Click the left mouse button to make a selection and click the right or center mouse button to leave the selection mode With these tools it may be necessary to arrange your windows prior to clicking OK or Apply so that the necessary Graph and Report windows are in view while making selections The tools
324. in manufacturing process The response variable is yield and the 241 Chapter 6 Statistics 242 treatment variable is treatment There is a second factor blend since a separate blend of the corn steep liquor had to be made for each application of the treatments Our main interest is in determining whether the treatment factor affects yield The blend factor is of only secondary interest it is a blocking variable introduced to increase the sensitivity of the inference for treatments The order of the treatments within blocks was chosen at random Hence this is a randomized block experiment Table 6 3 The effect of four treatments on the yield of penicillin blend treatment yield 1 A 89 2 A 84 2 A 81 4 A 87 5 A 79 1 B 88 2 B Z7 3 B 87 4 B 9a 5 B 81 1 97 C 92 3 C 87 4 89 5 E 80 Compare Samples Table 6 3 The effect of four treatments on the yield of penicillin Continued blend treatment yield 1 D 94 2 D 79 3 D 85 4 D 84 5 D 88 Setting up the data We use the Factorial Design dialog to create a penicillin data set containing the information in Table 6 3 For more information on this dialog see Factorial on page 264 1 Select Statistics Design gt Factorial Design from the main menu In the Levels field type 5 4 This step specifies 5 levels for the first column in our data set and 4 lev
325. in Figure 6 44 Generalized Linear Models BEI Model Options Results Plot Predict m Data r Model Data Set solder kad Family Poisson z Weights E Link log Subset Rows with Variance Functor eon tant 7 M Omit Rows with Missing Values r Save Model Object Save s Variables Dependent skips X Independent ALL gt Eormula Create Formula Cancel Appt current Figure 6 44 The Generalized Linear Models dialog Regression Example The solder data set contains 900 observations rows that are the results of an experiment that varied five factors relevant to the wave soldering procedure for mounting components on printed circuit boards The response variable skips is a count of how many solder skips appeared in a visual inspection We can use the Generalized Linear Models dialog to assess which process variables affect the number of skips 1 Open the Generalized Linear Models dialog 2 Type solder in the Data Set field 3 Select skips as the Dependent variable and lt ALL gt in the Independent variable list This generates skips in the Formula field 293 Chapter 6 Statistics Log Linear Poisson Regression 294 Figure 6 45 4 Select poisson as the Family The Link changes to 10g which is the canonical link for a Poisson model 5 Click OK A summary of the Poisson regression appears in the Report window Count data are frequently
326. in the Report window Standard Two Sample t Test data x gain high in weight gain and y gain low in weight gain t 1 8914 47 17 p value 0 0757 alternative hypothesis true difference in means is not equal to 0 95 percent confidence interval 2 193679 40 193679 sample estimates mean of x mean of y 120 101 The p value is 0 0757 so the null hypothesis is rejected at the 0 10 level but not at the 0 05 level The confidence interval is 2 2 40 2 In other words we conclude at the 0 05 level that there is no significant difference in the weight gain between the two diets To test the one sided alternative that U U gt 0 we change the Alternative Hypothesis field to greater in the Two sample t Test dialog Click OK to perform the test and see the following output Standard Two Sample t Test data x gain high in weight gain and y gain low in weight gain t 1 8914 af 17 peralue 0 0379 alternative hypothesis true difference in means is greater than 0 95 percent confidence interval 1525171 NA sample estimates mean of x mean of y 120 101 Two Sample Wilcoxon Test Compare Samples In this case the p value is just half of the p value for the two sided alternative This relationship between the p values holds in general You also see that when you use the greater alternative hypothesis you get a lower confidence bound This is the natural one sided confidence interval corresponding to the gr
327. in the cell where you want to begin the block selection and then drag the cursor to increase or decrease the size of the highlighted block When the desired area is highlighted release the mouse button Click in the cell where you want to begin the block selection and then SHIFT click in the cell whose column and row positions describe the block you want to select Hint Selecting Columns and Rows You can extend a cell selection by holding down the SHIFT key while pressing one of the arrow keys To select all the cells in a Data window click in the empty shaded area in the upper left hand corner of the Data window To select a single column or row click in the column or row header To select a block of contiguous columns or rows do one of the following Click in the column or row header of the first column or row to begin the selection and then SHIFT click in the column or row header of the last column or row describing the block you want to select Press and hold down the mouse button in the column or row header of the first column or row to begin the selection and then drag the cursor across the columns or rows you want to select and release the mouse button To select a group of noncontiguous columns or rows or to select a group of columns or rows in a special order do the following CTRL click in the header of each column or row you want to select in the order in which you want to make the selection
328. in x To create a vertical grouped box plot fi grouped box Of x 1 Select the x and y columns 2 Click the B amp button on the Extra Plots palette To create a horizontal grouped box plot click the a button instead Vertical grouped box plot Be Horizontal grouped box plot lar 167 TE Grouped Bar A grouped bar plot displays data as clusters of bars The x values are Plots the labels Multiple y columns determine the bar heights that is the height of the first bar in each group is determined by the values in the first y column the height of the second bar in each group by the values in the second y column etc To create a vertical grouped bar plot FETT Oo x 1 Select the x and multiple y columns 2 Click the Mi button on the Plots 2D palette 140 Vertical grouped bar plot a Plotting Two Dimensional Data To create a horizontal grouped bar plot 1 Select the multiple y columns first then CTRL click to select the x column last 2 Click the button on the Plots 2D palette Horizontal grouped bar plot SS W amp M AAV NUY ANN NNN SSG SSSsss3 xc cws RQ 2g SOQ N SNNN X N RS 2000 4 te is A 1999 7 Cl m YY A 1998 ie M WI x ANN o e
329. ince our calculated F statistic of 3 9 falls in our rejection region we reject the null hypothesis and conclude that the variance of population 1 is greater than the variance of population 2 A similar procedure can be used to calculate the rejection region of many common tests Example 3 Plotting the normal distribution The Distribution Functions dialog allows us to easily graph a probability distribution such as the normal distribution shown in Figure 6 87 0 4 5 0 15 0 0 7 T T T T T 4 2 0 2 4 x Figure 6 87 The normal distribution with mean O and standard deviation 1 The following steps generate the plot of the normal distribution in Figure 6 87 1 Open an empty data set by clicking the New Data Set button E on the Standard toolbar 2 From the main menu choose Data gt Fill Type x in the Columns field and 100 as the Length Specify a Start value of 4 and an Increment of 0 0808 4 Click OK This generates a column named x containing 100 equispaced values between 4 and 4 5 Open the Distribution Functions dialog 371 Chapter 6 Statistics Random Numbers 372 6 Verify that the name of the data set appears in the Data Set field and select x as the Source Column 7 To calculate density values check the Density option 8 Select normal from the Distribution pull down menu Note the default values of 0 and 1 for the Mean and Std Deviation 9 Click OK Spotfire S generates the d
330. ing e With no objects selected in the left pane click the Delete button on the Object Explorer toolbar e Right click in the white space of the left pane of the Object Explorer and select Delete Explorer Page from the shortcut menu By populating your Explorer Pages with folders and tailoring each folder s filtering parameters you can organize and display objects of particular interest to you Because filtering is such an important tool we devote a separate section to it later in the chapter see Filtering on Objects on page 397 To insert a folder do one of the following e With no objects selected in the left pane click the New Folder button on the Object Explorer toolbar or choose Insert gt Folder from the main menu e Right click in the white space of the left pane of the Object Explorer and select Insert Folder from the shortcut menu To insert a folder into an existing folder do one of the following e Select the folder into which you want to insert a new folder and then click the New Folder button on the Object Explorer toolbar or choose Insert Folder from the main menu Right click the icon of the folder into which you want to insert a new folder and select Insert Folder from the shortcut menu Doing either of the foregoing inserts a new folder with the default name Folderx where x is a sequential number To delete a folder do one of the following e Select the folder and press DELETE e S
331. ing Properties from the shortcut menu For properties dialogs containing multiple pages select the page s name from the shortcut menu Note that any changes you make through the Object Explorer are reflected immediately in the object You can copy and move object shortcuts using CTRL C and CTRL V or by selecting the Cut Copy and Paste commands on the Edit or shortcut menu You can also drag and drop objects between folders Hint Deleting Objects and Object Shortcuts 404 When you drag and drop an object within the Object Explorer window the object is moved to copy the object press CTRL while dragging In general you can drag and drop just about any S PLUS object onto any other object and get results For example e Dropping a data object onto a graph changes the data used in the graph e Dropping any object onto a Script window creates a script that when run recreates the object e Dropping an object onto a Report window produces a summary of the object e Dropping a folder or an Explorer Page onto a toolbar creates a toolbar button for recreating the folder or Explorer Page respectively Deleting objects through the Object Explorer is particularly useful for deleting multiple objects For example if you want to delete all the arrows on a plot it is much easier to select them in the Object Explorer than to select them directly on the graph It s also easy to delete columns from a data frame Just sel
332. ing a Data Source 180 An ODBC driver is a dynamically linked library DLL that connects an application or database to the ODBC interface Applications call functions in the ODBC interface which are implemented in the database specific drivers The use of drivers isolates applications from database specific calls in the same way that printer drivers isolate word processing programs from printer specific commands Spotfire S is automatically installed with an ODBC driver that connects to Spotfire S but you must provide an appropriate ODBC driver for your database Contact your database vendor or a third party ODBC driver vendor for assistance To determine which drivers are already installed on your computer start the Administrator and click the Drivers tab The name version company file name and file creation date of each ODBC driver installed on the computer are displayed To add a new driver or to delete an installed driver use the driver s setup program A data source is a logical name for a data repository or database It points to the data you wish to access the application that has the data and the computer and network connections necessary to reach the data Adding or configuring a data source can be done using the Administrator or from within Spotfire S see the online help To add a data source open the Administrator by double clicking the Administrator icon in the Control Panel If you are running Administrator
333. ing function that is executed using dialog inputs as values for function arguments Usually it is only necessary to fill in a few fields on the first page of a tabbed dialog to launch the function call 200 Dialog Fields Introduction Many dialogs include a Data Set field This field is automatically filled in with the name of the data set most recently opened with either File gt Open or the Select Data dialog To specify another data set you can either type its name directly in the Data Set field or make a selection from the dropdown list The data sets that appear in the dropdown list are limited to those that have been filtered by an Object Explorer Most dialogs that fit statistical models include a Subset Rows field that you can use to specify only a portion of a data set To use a subset of your data in an analysis enter an S PLUS expression in the Subset Rows field that identifies the rows to use The expression can evaluate to a vector of logical values true values indicate which rows to include in the analysis and false values indicate which rows to drop Alternatively the expression can specify a vector of row indices For example e The expression Species bear includes only rows for which the Species column contains bear e The expression Age gt 13 amp Age lt 20 includes only rows that correspond to teenage values of the Age variable e The expression 1 20 includes the first 20 rows of the data To use all ro
334. ing of capacitors It includes time to failure days indicator of failure or censoring event and the voltage at which the test was run voltage We use a parametric survival model to examine how voltage influences the probability of failure 1 Open the Parametric Survival dialog 2 Type capacitor in the Data Set field 3 Enter the Formula Surv days event voltage or click the Create Formula button to construct the formula The Surv function creates a survival object which is the appropriate response variable for a survival formula To use the Formula builder click the Create Formula button In the dialog that appears highlight the days variable and click the Time 1 button Highlight the event variable click the Censor Codes button and then click Add Response Finally highlight the voltage variable and click Main Effect This generates the formula Surv days event type right voltage Life Testing Survival By default the censoring argument type is set to right when only one time variable is given Thus this formula is equivalent to Surv days event voltage 4 Click OK A summary of the fitted model appears in the Report window The Life Testing dialog fits a parametric regression model for censored data These models are used in a variety of contexts ranging from manufacturing to studies of environmental contaminants Because of their frequent use for modeling failure time or survival data they are often re
335. ing the different levels of E o ombo o o a o o o Figure 3 39 Compression ratio versus nitrogen oxide conditioned on equivalence ratio defaults 99 Chapter 3 Exploring Data 100 Modifying the conditioning rules By default the conditioning variable is broken into four disjoint groups with an equal number of observations per group We can break the data into more groups and change other values controlling the conditioning 1 Right click an empty space within the graph to display the shortcut menu for the graph and select Multipanel 2 In the Continuous Conditioning group type 9 for of Panels and 0 25 for Frac Shared Pts 3 In the Layout group type 2 for of Rows 4 Click the Position Size tab of the dialog Type 2 5 for Aspect Ratio and click OK The resulting graph is shown in Figure 8 14 8 14 fi fi nl fi d E 1 0 to W1 JE 1 1 to 1B 2 8 aa e m gt o o a ie cassette 2 o o o o o o e amp 4 SoS olag e a o Eo 5 to 0 7 E 0 7 to 0 8 E G7 to 0 8 E 0 8to 0 9 E 0 9 M 1 0 o o 4 o clo gee 0 2 o a co o o o o gs o o 27 8 sg Sa og S o a o a s 2 o T T T T T T 8 14 8 14 8 14 Figure 3 40 Compression ratio versus nitrogen oxide conditioned on equivalence ratio The Trellis graph now has nine panels in two rows and each panel has an aspect ratio of 2
336. inger Verlag New York Marazzi A 1992 Algorithms Routines and S Functions for Robust Statistics Wadsworth amp Brooks Cole Pacific Grove CA Millard Steven 1998 User s Manual for Environmental Statistics Compansion book to the S Environmental Stats module The S Environmental Stats module is available through Dr Millard Selvin S 1998 Modern Applied Biostatistical Methods Using S PLUS Oxford University Press Venables W N and Ripley B D 1999 Modern Applied Statistics with S PLUS Third Edition Springer Verlag New York Graphical techniques Chambers J M Cleveland W S Kleiner B and Tukey P A 1983 Graphical Techniques for Data Analysis Duxbury Press Belmont CA Cleveland W S 1993 Visualizing Data Hobart Press Summit NJ Cleveland W S 1994 The Elements of Graphing Data revised edition Hobart Press Summit NJ 11 Chapter 1 Introduction TYPOGRAPHIC CONVENTIONS 12 Throughout this User s Guide the following typographic conventions are used This font is used for S PLUS expressions and code samples This font is used for elements of the Spotfire S user interface for operating system files and commands and for user input in dialog fields This font is used for emphasis and book titles CAP SMALLCA2P letters are used for key names For example the Shift key appears as SHIFT When more than one key must be pressed simultaneously the two key names a
337. ings and click OK 3 Right click in the column and select Save Column Type Column as default from the shortcut menu Spotfire S automatically numbers each row in a data set The row number is displayed in the row header and indicates the row s position in the Data window Because Spotfire S is column oriented most formatting options apply only to columns You can however add names to your rows When used row names are displayed in the header to the right of the row numbers To add or change a row name do the following 1 Double click in the name box of the row header 2 Type a row name or edit the existing name 3 Press ENTER or click elsewhere in the Data window to accept the changes A row list is a list of row numbers in a dialog field specifying a group or sequence of rows on which to operate To create a row list type the row numbers separated by commas For example 1 3 4 refers to rows 1 3 and 4 To specify a sequence of rows type the beginning and ending row numbers separated by a colon For example 3 7 refers to rows 3 through 7 To specify all rows in a data set type the special key word lt ALL gt Manipulating Data MANIPULATING DATA Spotfire S provides a wide assortment of data manipulation tools Buttons on the Data window toolbar are convenient for performing the most common tasks but many more options are available through the Data menu Moving and You can move or copy data within a Data wi
338. ion As shown in Figure 1 1 the HTML help window has three main areas the toolbar the left pane and the right pane E S PLUS Help _ 5 x f Hide Back Foward Refresh Print Options Contents Index Search Formatting Panels on a Multipanel Graph Importing and Exporting Data a ic Using the Object Explorer ic Exchanging Objects with Other Applicatic ic Creating a Graph Graph Gallery ic Working with Trellis Graphics QQ Formatting a Graph a Formatting a Graph Sheet 12 Formatting the Graph Area 2 Formatting the Plot Area 2 Formatting Panels on a Multipanel Graph 2 jatting 2D Axes 2 Formatting 2D Axis Labels Formatting and Rotating 3D Axes 2 Formatting 3D Axis Labels 2 Formatting 3D Planes 2 Rotating a 3D Graph 2 Displayig 3D Multipanel Graphs 2 Formatting Polar Axes 2 Adding Multi Line Text 2 Adding Special Characters and Form Q Adina Ties adiaee gt Whenever a plot is placed on a multipanel graph a plot panel object is created for each panel The panels are contained by the plot in the Object Explorer and their properties can be changed by right clicking on their icon These panel objects have display attributes of their own that can be used to override the plot s display attributes For example you might want to set the line color of the plot to green in the third panel and to blue in the rest of the panels
339. ion of 431 Edit data 440 engine object type of 380 for hypothesis testing 452 for statistical modeling 454 for summary statistics 451 high level plotting 447 importData 438 low level plotting 448 objects 430 operators 432 arithmetic 432 comparison 433 exponentiation 433 logical 433 precedence hierarchy of 435 sequence 433 unary 433 par command 449 plot 446 qqnorm for linear models 279 read table 440 rm 430 scan 438 439 fuzzy analysis 333 G generalized models linear 292 Go To Cell dialog 27 graph objects dragging into a Script window 478 graph options 537 graphs brush and spin brushing 114 spinning 114 creating in Excel add in 498 creating in Mathcad component 510 creating in SPSS add in 505 multiple plots on 93 Trellis introduction to 97 Graph Sheet document object type of 381 graph styles 541 grouped bar plots 140 grouped box plots 140 H Help online 5 421 for methods 421 manuals 8 organization of 422 reading 422 help online 8 high low open close plot 147 high low plots 146 histograms 125 History Log 475 476 clearing 477 executing commands in 477 size of 475 starting and ending entries in 476 viewing in a Script window 475 hypothesis testing 452 I importData function 438 Import From File dialog 168 Import From ODBC dialog 181 importing data 16 438 from data files 168 from ODBC tables 181 Increase Precision button 35 Increase Width button 33 indentation automatic 4
340. ir na action na exclude Residuals Min 1Q Median 30 Max 1 49 0 4258 0 02521 0 3636 2 044 Coefficients Value Std Error t value Pr gt t Intercept 2 2260 0 4614 4 8243 0 0000 temperature 0 0704 0 0059 11 9511 0 0000 Residual standard error 0 5885 on 109 degrees of freedom Multiple R Squared 0 5672 F statistic 142 8 on 1 and 109 degrees of freedom the p value is 0 The Value column under Coefficients gives the coefficients of the linear model allowing us to read off the estimated regression line as follows ozone 2 2260 0 0704 x temperature The column named Std Error in the output gives the estimated standard error for each coefficient The Multiple R Squared term tells us that the model explains about 57 of the variation in ozone The F statistic is the ratio of the mean square of the regression to the estimated variance if there is no relationship between temperature and ozone this ratio has an F distribution with 1 and 109 degrees of freedom The ratio here is clearly significant so the true slope of the regression line is probably not 0 Diagnostic plots for linear models How good is the fitted linear regression model Is temperature an adequate predictor of ozone concentration Can we do better Questions such as these are essential any time you try to explain data with a statistical model It is not enough to fit a model you must also Regression assess how well the model fits the data an
341. is evaluated with and without parentheses In the expression 1 x 1 x 1 is evaluated first with 4 being the result so Spotfire S displays the integers from 1 to 4 7 ieee 1 1234 With the parentheses left off the expression becomes 1 x 1 Because the operator has greater precedence than the operator 1 x 1 is interpreted by Spotfire S as meaning take the integers from 1 to 5 and then subtract 1 from each integer Hence the output is of length 5 instead of length 4 and starts at 0 instead of 1 as follows gt Lex fo t23 4 When using Spotfire S keep in mind the effect of parentheses and of the default operator hierarchy One powerful feature of S PLUS functions is considerable flexibility through the use of optional arguments At the same time simplicity is maintained because sensible defaults for optional arguments have been built in and the number of required arguments is kept to a minimum S PLUS Language Basics You can determine which arguments are required and which are optional by looking in the help file in the REQUIRED ARGUMENTS and OPTIONAL ARGUMENTS sections For example to produce 50 random normal numbers with mean 0 and standard deviation 1 use the following gt rnorm 50 If you want to produce 50 random normal numbers with mean 3 and standard deviation 5 you can use any of the following rnorm 50 3 5 rnorm 50 sd 5 mean 3 rnorm 50 m 3 s 5 rnorm m 3 s 5 50 wo A OM ON
342. ith M Omit Rows with Missing Values Cancel Apply current Figure 6 69 The Monothetic Clustering dialog 338 Cluster Analysis Example The catalyst data set comes from a designed experiment Its eight rows represent all possible combinations of two temperatures Temp two concentrations Conc and two catalysts Cat The fourth column represents the response variable Yield We are interested in determining how temperature concentration and catalyst affect the Yield Before fitting a model to these data we can group observations according to the three binary predictors by using monothetic clustering 1 Open the Monothetic Clustering dialog 2 Type catalyst in the Data Set field 3 CTRL click to highlight the Variables Temp Conc and Cat 4 Click OK A summary of the monothetic clustering appears in the Report window 339 Chapter 6 Statistics MULTIVARIATE Discriminant Analysis 340 Multivariate techniques summarize the structure of multivariate data based on certain classical models The Discriminant Analysis dialog lets you fit a linear or quadratic discriminant function to a set of feature data Performing discriminant analysis From the main menu choose Statistics gt Multivariate Discriminant Analysis The Discriminant Analysis dialog opens as shown in Figure 6 70 Discriminant Analysis BBE Model Results m Data r Model Data Set fist Family classical
343. ivariate analyses However if the responses are correlated then MANOVA can be more informative than the univariate analyses as well as less repetitive Performing MANOVA From the main menu choose Statistics gt Multivariate MANOVA The Multivariate Analysis of Variance dialog opens as shown in Figure 6 73 Multivariate Analysis of Variance i p x Model Options Results m Data Data Set wafer e Weights h Subset Rows with Save Model Object M Omit Rows with Missing Values Save As Formula Formula cbind pre mean post mean maskdim visc tem spinsp bake OK Cancel Appt current Help Figure 6 73 The Multivariate Analysis of Variance dialog Example The data set wafer has eighteen rows and thirteen columns of which eight contain factors four contain responses and one is the auxiliary variable N It is a design object based on an orthogonal array design for an experiment in which two integrated circuit wafers were made for each combination of factors On each wafer the pre and post Multivariate etch line widths were measured five times The response variables are the mean and deviance of the measurements As three of the wafers were broken the auxiliary variable N gives the number of measurements actually made We are interested in treating the pre mean and post mean variables as a multivariate response using MANOVA to explore the effect of each factor upon t
344. k 6 No Active Link character R Figure 2 22 Inserting a column of a specific type e From the main menu choose Insert gt Column The Insert Columns dialog opens as shown in Figure 2 23 Insert Columns op x Name s Bews ss SS Eill Expression a Start Column ho Count fi Data Set ethan Column Type double Figure 2 23 The Insert Columns dialog 42 Manipulating Data Select the column you want to have shifted to the right to make room for the new column from the Start Column dropdown list Type a name for the new column in the Name s text box and click OK Hint You can also use the Insert Columns dialog to insert multiple columns Simply type the number of columns you want to insert in the Count text box and a comma delimited list of names in the Names s text box Inserting Rows To insert a row do one of the following e Click in the row you want to have shifted down to make room for the new row and then click the Insert Row button on the Data window toolbar From the main menu choose Insert gt Row The Insert Rows dialog opens as shown in Figure 2 24 Insert Rows ol x Data Set ethanol E Start Row 3 Count 1 Cancel Apply current Help Figure 2 24 The Insert Rows dialog In the Start Row text box type the row number of the row you want to have shifted down to make room for the new row and click OK Hint You can als
345. k sum test confirms the results of our one way ANOVA The Friedman rank test is appropriate for data arising from an unreplicated complete block design In these kinds of designs exactly one observation is collected from each experimental unit or block under each treatment The elements of y are assumed to consist of a groups effect plus a blocks effect plus independent and identically distributed residual errors The interaction between groups and blocks is assumed to be zero In the context of a two way layout with factors groups and blocks a typical null hypothesis is that the true location parameter for y net of the blocks effect is the same in each of the groups The alternative hypothesis is that it is different in at least one of the groups Performing a Friedman rank test From the main menu choose Statistics gt Compare Samples gt k Samples gt Friedman Rank Test The Friedman Rank Sum Test dialog opens as shown in Figure 6 20 Friedman Rank Sum Test Big ks Data r Results Data Set pericilin gt Save As sis Variable fied x MV Print Results Grouping Variable treatment gt Blocking Variable bend Be x EA E d f current Hep Figure 6 20 The Friedman Rank Sum Test dialog Example The data set shown in Table 6 3 was first used by Box Hunter and Hunter in 1978 The data were collected to determine the effect of treatments A B C and D on the yield of penicillin in a penicill
346. l Weights 9 Covariance Struct heteroscedastic m Frequencies Group Prior proportional Na Subset Rows with ee a Save Model Object M Omit Rows with Missing Values Save As Variables Dependent variety E Independent Formula variety iris Sepal L iris Sepal W iris Petal L iris Petal w Create Formula Cancel Apply f current Help Figure 6 70 The Discriminant Analysis dialog Example We perform a discriminant analysis on Fisher s iris data This data set is a three dimensional array giving 4 measurements on 50 flowers from each of 3 varieties of iris The measurements are in centimeters and include sepal length sepal width petal length and petal width Factor Analysis Multivariate The iris species are Setosa Versicolor and Virginica The built in data frame iris df located in the example5 library is a two dimensional version of iris 1 Choose File Load Library to load the examp1e5 library Highlight example5 in the Library Name list and click OK This library contains a few example data sets that are not in the main Spotfire S databases Open the Discriminant Analysis dialog Type iris df in the Data Set field Choose variety as the Dependent variable Sv ms Ge oh CTRL click to select iris Sepal L iris Sepal W iris Petal L and iris Petal W as the Independent variables 6 Choose heteroscedastic as
347. l observations with NAs Exploratory data analysis Box plots are a quick and easy way to get a first look at the data Highlight the two columns diet and time in the blood Data window Open the Plots 2D palette and click the Box button ER to generate the box plots 237 Chapter 6 Statistics 238 The resulting box plots are similar to those in Figure 6 18 This plot indicates that the responses for diets A and D are quite similar while the median responses for diets B and C are considerably larger relative to the variability reflected by the heights of the boxes Thus you suspect that diet has an effect on blood coagulation time 707 i s 60 7 time 557 diet Figure 6 18 Box plots for each of the four diets in the blood data set The one way layout model and analysis of variance The classical model for experiments with a single factor is Vig Wit ey j l J i 1 I where u is the mean value of the response for the ith level of the experimental factor There are J levels of the experimental factor and J measurements y 1 Yio gt Vj J are taken on the response variable for level of the experimental factor Using the treatment terminology there are 7 treatments and y is called the ih treatment mean This is often called the one way layout model For the blood coagulation experiment there are 7 4 diets and the means L Compare Samples Ly Hg and u4 correspond to
348. l Clustering dialog Type state df in the Data Set field CTRL click to select the Variables Population through Area Click OK A summary of the clustering appears in the Report window 337 Chapter 6 Statistics Example 2 In Compute Dissimilarities on page 329 we calculated dissimilarities for the fuel frame data set In this example we cluster the fuel frame dissimilarities using the divisive hierarchical algorithm 1 If you have not already done so create the object fuel diss from the instructions on page 330 2 Open the Divisive Hierarchical Clustering dialog 3 Select the Use Dissimilarity Object check box 4 Select fuel diss as the Saved Object 5 Click OK A summary of the clustering appears in the Report window Monothetic When all of the variables in a data set are binary a natural way to Clustering divide the observations is by splitting the data into two groups based on the two values of a particular binary variable Monothetic analysis produces a hierarchy of clusters in which a group is split in two at each step based on the value of one of the binary variables Performing monothetic clustering From the main menu choose Statistics Cluster Analysis gt Monothetic Binary Variables The Monothetic Clustering dialog opens as shown in Figure 6 69 Monothetic Clustering BEI Model Results Plot Data r Save Model Object Data Set catalyst X Save As Variables Subset Rows w
349. l Object Dat Set Puromycin gt Save As pooo Model Eormula vel m conc K conc Parameters name value fvm 200 K 0 1 Cancel Apply curent Help Figure 6 42 The Nonlinear Regression dialog Example The data set Puromycin has 23 rows representing the measurement of initial velocity of a biochemical reaction for 6 different concentrations of substrate and two different cell treatments Figure 6 43 plots velocity versus concentration with different symbols for the two treatment groups treated and untreated 200 o untreated 4 treated 1504 vel o 100 4 a o bo 50 7 T 0 0 0 2 0 4 0 6 0 8 1 0 12 conc Figure 6 43 Scatter plot of the Puromycin data 289 Chapter 6 Statistics 290 The relationship between velocity and concentration is known to follow a Michaelis Menten relationship V V max E K c where V is the velocity c is the enzyme concentration V ax is a parameter representing the asymptotic velocity as c gt K is the Michaelis parameter and is experimental error Assuming the treatment with the drug would change V as but not K the x optimization function is Vi xtAVnm I state c 2 SCV maw K D a aa K c where is the function indicating whether the cell was treated treated with Puromycin We first fit the simpler model in which a single curve is fit for both groups We then add a t
350. l as the slope of the surface in various regions Filled contour plots A useful enhancement to a contour plot is to add color indicating the magnitude of the z variable at each location 1 Click any contour in the plot to select the plot 2 Click the Filled Contour button on the Plots 2D palette The plot is changed to a filled contour plot Dynamic Graphics The Sliced Ball Data Visualizing Multidimensional Data 3 Right click the plot to display the shortcut menu and select Contour Fills to open the Contour Plot dialog Change the Fill Type to 2 Color Range Click OK The resulting contour plot has colors between Blue and Lt Cyan as shown in Figure 3 50 1 0 1 Vi Figure 3 50 Filled contour plot of the exsurf data 4 Click the Color Scale Legend button W on the Graph toolbar to add a legend With a scatterplot matrix and a 3D scatter plot you can generate interactive dynamic graphics With them you can view a matrix plot of the data select and label groups of points and rotate the data interactively to obtain dynamic views of your multidimensional data set We will demonstrate how to use Spotfire S s dynamic graphics using the sample data set sliced ball a three dimensional data set representing points within a sphere It contains structure that is not apparent in a scatterplot matrix 1 Use the Select Data dialog to display the s1iced ba11 data in a Data window 2 Examine the data Do you
351. l gain and diff hstart contained in the exmain data set introduced earlier in the chapter Both are time series of values recorded once per year on the first of January for the 14 years beginning in 1971 We will use these variables to make line plots and time series plots The simple line plot To make a line plot of the tel gain variable 1 Use the Data gt Select Data dialog to open exmain or create the data set as described on page 60 2 Select the tel gain column 77 Chapter 3 Exploring Data 78 3 Click the Line button A on the Plots 2D palette The resulting graph is shown in Figure 3 20 2 0 7 tel gain Figure 3 20 Line plot of tel gain When you select a single variable to plot Spotfire S assumes that this variable is the y axis variable and supplies as the x axis variable the integers 1 2 n where n is the length of your variable that is the number of values in the variable You will not get a meaningful line plot if you select both tel gain and diff hstart as Spotfire S then assumes the first variable you selected is the x axis variable Line with points plot To create a line plot with symbols superimposed 1 Click the line plot to select it Visualizing Two Dimensional Data 2 Click the Line Scatter button on the Plots 2D palette The resulting graph is shown in Figure 3 21 2 0 7 tel gain Figure 3 21 Line with points plot of tel gain Notice t
352. label table 4 data frame 13 5 E axis tick table 4 data frame 34x9 ml bar old 4 list Figure 7 3 A red X painted through an object s icon indicates it is being masked Note also that objects stored in databases other than the working data are for all practical purposes read only While you can modify any object stored in any database in the search path the modified version of the object will be saved in the working data the original object remains unchanged in its original location Introducing the Object Explorer INTRODUCING THE OBJECT EXPLORER The Object Explorer is the simple but powerful interface both for manipulating and for visually organizing your S PLUS objects As shown in the example in Figure 7 4 the Object Explorer window is split into two panes that provide different views of objects their components and attributes Object Explorer iol x Contents of Graph2D Pos Data Class Dimensions _ 1 L320 Scatter LinePlot La Axis241 1 Axis2dx EJ Axis2dv1 1 Axis2d a ee D Graph2D fe ML Axis2dX1 H E Axis2dv1 L 2D Scatter E3 Reports E Scripts H E SearchPath z Figure 7 4 The Object Explorer window The left pane of the Object Explorer represents a single Explorer Page The Object Explorer can contain any number of Explorer Pages each represented by a tab in the lower left hand corner of the window Each Explorer Page in turn can contain any numbe
353. le specify the delimiter to use to separate the elements in the file By default Spotfire S uses the comma Date format Select the format you prefer to use when exporting date data The available choices mirror those in your Windows Regional Options the default value for this field is the current Windows default Time format Select the format you prefer to use when exporting time data The available choices mirror those in your Windows Regional Options the default value for this field is the current Windows default Importing From and Exporting to Data Files The Filter page of the Export To File dialog is shown in Figure 5 6 Export To File 5 x Data Specs Options Filter m Keep or drop columns T Preview column names Select columns fa X Keep selected Drop selected Filter rows T Preview row names Select Rows kalb m Filter expression Cancel Apply current Help Figure 5 6 The Filter page of the Export To File dialog Filter page Keep or drop columns group Preview column names When selected Spotfire S populates the Select columns dropdown list with the column names of the data set you are exporting Select columns Depending upon your choice of Keep selected or Drop selected specify either the columns you want to export or the columns you do not want to export respectively When Preview column names is selected this field becomes a dropdown list from
354. le weight Disp Engine displacement 6 liter 8 liter etc e Mileage Mileage in miles per gallon e Fuel 100 mileage 87 Chapter 3 Exploring Data e Type Category of vehicle Large Medium Smal1 Compact Sporty Van How do these variables relate to each other Color Plots First let s focus on the relationship between automobile weight mileage and type of car We will begin by generating a scatter plot with symbol color varying based on type Generating a color plot with a legend 1 Use the Select Data dialog to display the fuel frame data set in a Data window 2 Click Weight and then CTRL click Mileage and Type 3 Click the Color button Ez on the Plots 2D palette 4 Click the Auto Legend button on the Graph toolbar Varying the symbol style To further highlight the different categories of vehicle we can vary the symbol style 1 Right click any of the plot symbols and select Vary Symbols from the shortcut menu 2 Change Vary Style By to z Column Verify by looking in the Data Columns group on the Data to Plot page of the dialog that Type has been specified as the z column 88 Visualizing Multidimensional Data 3 Click OK You can change the defaults for symbol styles and colors see Chapter 11 Customizing Your Spotfire S Session for more information Figure 3 29 displays the resulting color and symbol plot 35 o Compact yo 4 Large o Medium
355. lect it and click Remove Note Clicking Remove only removes the object s shortcut from the folder it does not delete the object itself Objects page Objects Excluded From Filter group Object Paths This text box displays references to objects matching the folder s filtering properties but whose shortcuts you removed from the folder To add one of these object shortcuts back into the folder select it and click Add Advanced page The Advanced page of the Folder dialog shown in Figure 7 14 allows you to refine your filtering criteria Folder 1 Mm x Folder Objects Advanced Object Creation data frame x Interface Objects AutomationClien a ClassInfo Documents GraphSheet Functionlnfo z Report Script Menultem ObjectDefault gt Database Filter V Search Working Chapter Only 7 Z OO O Classes fact anova aovaovlist arima bootstrap censorReg charact x IV Include Derived Classes JV Archive Database Position Gr Cancel Apply current Help Figure 7 14 The Advanced page of the Folder dialog 400 Working With Objects Object Creation Select a default class for objects to be created in the folder Note that selecting a default class here does not restrict you to creating only that class of objects in the folder it merely provides a handy menu selection on the folder s shortcut menu When you create an object that does not match the classes of obje
356. lick the Las button on the Plots 2D palette Histogram D Density plot lA Plotting One Dimensional Data Histogram density plot A 3 i A P 0 1 0 3 0 5 0 7 0 9 14 Pie Charts A pie chart shows the share of individual values in a column relative to the column sum To create a pie chart 1 Select the x column 2 Click the button on the Plots 2D palette Pie chart 3 0 Dot Plots A dot plot plots independent data against categorical dependent data using gridlines to mark the dependent levels 127 Chapter 4 Creating Plots To create a dot plot Bar Plots 1 Either select a single x column to plot its values along the horizontal axis against an integer sequence along the vertical or select both x the categorical data and y to plot y against x Click the button on the Plots 2D palette Dot plot Oo NNWVWANNDOD NNOO 0 0 MVNwWMDwWoOROUOR o i NE w o A bar plot displays a bar of a height or width for a horizontal bar plot determined by its corresponding data value A bar plot with error displays an error bar on top or at the end of each bar To create a vertical bar with base at Y min plot 128 1 Either select a single column to create a bar plot
357. lity Performing a Kruskal Wallis rank sum test From the main menu choose Statistics gt Compare Samples gt k Samples gt Kruskal Wallis Rank Test The Kruskal Wallis Rank Sum Test dialog opens as shown in Figure 6 19 Kruskal Wallis Rank Sum Test aioli xt Data r Results Data Set blood X Save As Variable time v Print Results Grouping Variable diet ka Cancel Appt current Help Figure 6 19 The Kruskal Wallis Rank Sum Test dialog Example In One Way Analysis of Variance on page 234 we concluded that diet affects blood coagulation times The one way ANOVA requires the data to be normally distributed The nonparametric Kruskal Wallis rank sum test does not make any distributional assumptions and can be applied to a wider variety of data We now conduct the Kruskal Wallis rank sum test on the blood data set 1 If you have not done so already create the blood data set with the instructions given on page 236 2 Open the Kruskal Wallis Rank Sum Test dialog Type blood in the Data Set field 4 Select time as the Variable and diet as the Grouping Variable 5 Click OK The Report window displays the result Kruskal Wallis rank sum test Friedman Rank Test Compare Samples data time and diet from data set blood Kruskal Wallis chi square 17 0154 df 3 p value 0 0007 alternative hypothesis two sided The p value is 0 0007 which is highly significant The Kruskal Wallis ran
358. ll colors rotate in the order specified on the Pattern Color and Fill Color pages of this dialog If no rotation is specified the first fill pattern pattern color and fill color are used by default 543 Chapter 11 Customizing Your Spotfire S Session Lines The Lines page of the Color Style dialog is shown in Figure 11 16 Color Style 5 x Options Lines Symbols Pattem Color FilColor Line Style1 Plot Defaut LineColort Plot Defaut lineSiyle2 LineColor2 Miuse z LineStle3 LineColr3 Muses F LineSiled LineColord uses F Line Style 5 Line Color 5 Buses Line Style 6 X Line Color 6 B Line Style 7 Line Color 7 mm User8 x Line Style 8 z Line Color 8 E User2 z Line Style 9 cette ea Line Color 9 B s 7 Line Style 10 7 Line Color 10 L User4 x E Figure 11 16 The Lines page of the Color Style dialog The Lines page has ten style and ten color fields Use these fields to select line styles and colors in the order in which you want them to cycle Symbols The Symbols page of the Color Style dialog is shown in Figure 11 17 Color Style MEE Options Lines Symbols Pattern Color Fill Color Symbol Stylel Plot Defaut SymbolColort Plot Defaut Symbol Style2 A Triange Up Z SymbolColr2 Muse z Symbol Style3 O Box Empty SymbolColrs Muses
359. log opens as shown in Figure 6 32 Factor Plot olx m Data T Options Data Set catalyst x Ippe Boxplot x Subset Rows with I Rotate X Axis Labels IV Omit Rows with Missing Values T Include Boxplot Means Variables gt Layout Dependent field zj Rows 2 Columns 2 Independent OK Cancel Apply current Help o Figure 6 32 The Factor Plot dialog Example We create factor plots for the catalyst data set as follows 1 2 3 4 Open the Factor Plot dialog Type catalyst in the Data Set field Select Yield as the Dependent variable CTRL click to select Temp Conc and Cat as the Independent variables 267 Chapter 6 Statistics 5 Change the number of Rows and number of Columns to 2 6 This specifies a 2 x 2 grid of plots Click OK A factor plot appears in a Graph Sheet For each factor there is a set of box plots for Yield with a separate box plot for each factor level Interaction An interaction plot displays the levels of one factor along the x axis the Plot response on the y axis and the points corresponding to a particular level of a second factor connected by lines This type of plot is useful for exploring or discovering interactions Creating an interaction plot From the main menu choose Statistics gt Design gt Interaction Plot The Interaction Plot dialog opens as shown in Figure 6 33 Interaction Plot l
360. ls Likelihood Ratio Test dialog Example In the kyphosis analysis of Logistic Regression on page 295 we suggested that Start had a significant effect upon Kyphosis but Age and Number did not We can use a chi square test to determine whether a model with just Start is sufficient 1 2 3 Open the Logistic Regression dialog Type kyphosis in the Data Set field Specify Kyphosis Aget Number Start in the Formula field Type kyph full in the Save As field and click Apply Information describing this model is saved as an object named kyph full Change the Formula field to Kyphosis Start Change the Save As name to kyph sub and click OK Information describing this model is saved as an object named kyph sub Open the Compare Models Likelihood Ratio Test dialog CTRL click to select kyph full and kyph sub in the Model Objects list Select Chi Square as the Test Statistic 327 Chapter 6 Statistics 328 8 Click OK The analysis of deviance table below appears in the Report window The table displays the degrees of freedom and residual deviance for each model Under the null hypothesis that the simpler model is appropriate the difference in residual deviances is distributed as a chi squared statistic The Pr Chi column provides a p value for the hypothesis that the simpler model is appropriate If this value is less than a specific value typically 0 05 then the more complex model causes a large enough change i
361. ls are regression or ANOVA models in which the residuals have a nonstandard covariance structure The covariance structures supported include correlated and heteroscedastic residuals The Generalized Least Squares dialog fits a linear model using generalized least squares Errors are allowed to be correlated and or have unequal variances Performing generalized least squares regression From the main menu choose Statistics Generalized Least Squares gt Linear The Generalized Least Squares dialog opens as shown in Figure 6 54 Generalized Least Squares SEs Model Options Results Plot Predict m Data Data Set Ovary 7 Subset Rows with Save Model Object M Omit Rows with Missing Values Save As m Variables Dependent Independent Eormula ffolicles sin 2 pi Time cos 2 pi T ime Create Formula Cancel Apply current Help Figure 6 54 The Generalized Least Squares dialog Example The Ovary data set has 308 rows and three columns giving the number of ovarian follicles detected in different mares at different times in their estrus cycles Biological models suggest that the number of follicles may be modeled as a linear combination of the sine and cosine of 2 pi Time We expect that the variation increases with Time Nonlinear Generalized Least Squares and hence use generalized least squares with a Power variance structure instead of standard linear regression
362. lues of 20 random variables with a common but unknown mean value location u If the experimental setup for measuring the speed of light is free of bias then it is reasonable to assume that p is the true speed of light 55 Chapter 3 Exploring Data In this and subsequent sections we examine the distribution of these observations In Chapter 6 Statistics we pose some questions regarding the mean of the data and perform various statistical tests to answer the questions The data form a single ordered set of observations so they are appropriately described as a data set with one variable We will use a Data window to create a new data set containing the 20 observations listed above 1 From the main menu choose Data gt Select Data to display the Select Data dialog 2 In the Source group click the New Data radio button to select it 3 In the New Data group type exmichel in the Name field and click OK 4 Now enter the 20 data points in the first column Change the column or variable name from the default v1 by double clicking V1 and typing speed Press ENTER or click elsewhere in the Data window to accept the change Exploratory To obtain a useful exploratory view of the Michelson data create the Plots following plots a box plot a histogram density plot and a QQ normal plot 1100 7 1000 7 900 7 speed 800 7 700 7 600 Figure 3 1 A box plot of the Michelson d
363. matic sample of every nth case after a random start This function accepts the single numeric argument n Because expressions are evaluated from left to right you can sample a subset of rows in a data file by first subsetting and then sampling For example to import a random sample of half the rows corresponding to high school graduates use the expression schooling gt 12 amp samp rand 0 5 The sampling functions use the S PLUS random number generator to create random samples Therefore you can use the set seed function in the Commands window to produce the same data sample repeatedly For more details see the help files for set seed and Random seed 189 Chapter 5 Importing and Exporting Data NOTES ON IMPORTING AND EXPORTING FILES OF CERTAIN TYPES ASCII Delimited ASCII Files dBASE Files Files With Multiple Tables 190 In this section we offer some additional comments to help you when importing and exporting data in particular file formats You have the option of specifying column names when importing columns from an ASCII file To do so type a list of names separated by any of the delimiters specified in the Delimiter field in the Alt col names field For each imported column specify one column name for example Apples Oranges Pears You can use an asterisk to denote a missing name for example Apples Pears A row of data must always end with a new line Multiple delimiter characters ar
364. me Series gt Lag Plot The Lag Plot dialog opens as shown in Figure 6 83 Lag Plot a x Data Options Object fins df X Lag a Variable flan X Rows 2 Subset Rows with l Columns 2 M Omit Rows with Missing Values Cancel Apply f f current Help Figure 6 83 The Lag Plot dialog 363 Chapter 6 Statistics Spectrum Plot 364 Example In Autocorrelations on page 359 we computed autocorrelations for the lynx df time series In this example we use a lag plot to example the correlation between observations at different lags l OF OY Be Go iS 7 If you have not done so already choose File gt Load Library to load the example5 library The example5 library contains the lynx df data Open the Lag Plot dialog Type lynx df in the Data Set field Select lynx as the Variable Select a Lag of 4 Select a layout of 2 Rows by 2 Columns Click OK A lag plot of the lynx df data appears in a Graph Sheet The Spectrum Plot dialog plots the results of a spectral estimation This plot displays the estimated spectrum for a time series using either a smoothed periodogram or autoregressive parameters Creating a spectrum plot From the main menu choose Statistics gt Time Series gt Spectrum Plot The Spectrum Plot dialog opens as shown in Figure 6 84 Time Series Spectrum Plot cixl Data r Options Object insat Method Periodogram gt Variable be
365. ments above gt x O 4 3 2 1 y 110 429 Chapter 8 Using the Commands Window Storing Data Objects Listing Data Objects Removing Data Objects Displaying Data Objects 430 The lt form of the assignment operator is highly suggestive and readable so the examples in this manual use the arrow Data objects in your working directory are permanent They remain even if you quit Spotfire S and start Spotfire S again later You can also change the directory location where S PLUS objects are stored by using the attach function or by using the Object Explorer See the attach help file for further information To display a list of the names of the data objects in your working directory use the objects function as follows gt objects If you created the vectors x and y as described on page 429 you see these listed in your working directory The S PLUS objects function also searches for objects whose names match a character string given to it as an argument The pattern may include wildcard characters For instance the following expression displays all of your objects that start with the letter d gt objects d See the help file for grep for information on wildcards and how they work Because S PLUS objects are permanent from time to time you should remove objects you no longer need Use the rm function to remove objects The rm function takes any number of objects as its arguments and removes each
366. mple of Rows dialog 51 read table function 440 read only mode 403 Recode dialog 50 redraw auto plot 549 regression 270 linear 271 local loess 286 nonlinear 288 regression line 279 rejection regions calculating 370 relational operators 187 Remove Block dialog 45 Remove Column button 46 Remove Columns dialog 46 Remove Current Link button 495 Remove Excel to Spotfire S Link button 495 Remove Row button 47 Remove Rows dialog 47 removing columns 46 rows 47 reports document object type of 381 Report window 461 480 creating 481 saving 481 resampling bootstrap 351 jackknife 353 residuals definition of 271 normal plots 279 plotting in linear models 279 resources 5 Restore Data Objects button 20 Restore Data Objects dialog 20 restoring data sets to initial state 20 to previous state 20 right braces automatic insertion of 472 rm function 430 routing text output 537 row lists 36 rows clearing 46 copying by dragging 40 using the clipboard 40 using the Data menu 40 inserting 43 lists of 36 moving by dragging 40 using the clipboard 40 using the Data menu 40 names of 36 adding 36 changing 36 numbers of 36 removing 47 selecting 29 contiguous 29 in a special order 29 noncontiguous 29 Run button 466 S S init file 551 S_FIRST environment variable 551 sample data sets 14 SAS files hints for importing and exporting 190 Save As field 201 Save In field 201 SBATCH 459 scan function 438 439
367. mpt dialog is a handy reminder that in order to permanently store a Graph Sheet you must save it to an external sgr file Note that if you clear this check box you can still save your Graph Sheets by choosing File gt Save from the main menu before closing the Graph Sheet window 524 Changing Defaults and Settings Prompt to Save Data Files When selected Spotfire S issues a dialog prompt whenever you close a Data window displaying a new or modified data set Note Unlike Graph Sheets data sets are automatically and permanently stored in a special internal database called the working data Letting Spotfire S store your data for you is the easiest way to manage your data However if you prefer you can save your data sets as external sdd files the prompt dialog simply reminds you to do so Note that if you clear this check box you can still save your data sets as files by choosing File Save from the main menu before closing the Data window Remove Data from Database Select an option to control how Spotfire S handles the data objects in your working data that you have created or modified during the current session when you end the session Note The default for this field is Never Remove Data However if you elect to store your data sets in external files you should probably select Always Remove Data to prevent conflicts with database objects when you open these external files Show
368. n If Spotfire S encounters problems during the creation of a graph in SPSS any error messages will appear in a modeless dialog box in SPSS If errors occur it might mean that invalid data were specified for the plot created It might also indicate another problem related to the range or data type of the data specified The graph may not be created if errors occur Using Spotfire S With MathSoft Mathcad USING SPOTFIRE S WITH MATHSOFT MATHCAD The Spotfire S component for Mathcad makes it easy to create and modify Spotfire S graphs from within the MathSoft Mathcad application The component includes the ability to create Spotfire S graphs from selected Mathcad variables and to modify the layout or properties of a Spotfire S graph embedded in a Mathcad worksheet The graph wizard guides you through the process of choosing a Spotfire S graph and plot type and creating the graph in Mathcad You can also use the S PLUS script language to create and manipulate data within Spotfire S and return the information to Mathcad The script wizard lets you enter S PLUS language commands and specify the number of inputs to be passed from Mathcad and the number of outputs to be returned to Mathcad Installing the During a Typical installation of Spotfire S Setup examines your Spotfire S system for the necessary version of Mathcad Version 8 02 or higher Component for and if detected automatically installs the Spotfire S component for Math
369. n examining whether the child s age the number of vertebrae operated on or the starting vertebra influence the likelihood of the child having a deformity As an exploratory tool we test whether the distributions of Age Number and Start are the same for the children with and without kyphosis 1 Open the Two sample Kolmogorov Smirnov Goodness of Fit Test dialog 2 Type kyphosis in the Data Set field We perform separate tests for each of the three covariates in each case grouping by Kyphosis Select Kyphosis as Variable 2 Select the Variable 2 is a Grouping Variable check box 4 Select Age as Variable 1 Click Apply Select Number as Variable 1 Click Apply 6 Select Start as Variable 1 Click OK 233 Chapter 6 Statistics K Sample Tests One Way Analysis of Variance 234 A Report window appears with three goodness of fit summaries The pvalues for Age Number and Start are 0 076 0 028 and 0 0002 respectively This suggests that the children with and without kyphosis do not differ significantly in the distribution of their ages but do differ significantly in the distributions of how many vertebrae were involved in the operation as well as which vertebra was the starting vertebra This is consistent with the logistic regression model fit to these data later in Logistic Regression on page 295 Spotfire S supports a variety of techniques to analyze group mean differences in designed experiments e One way an
370. n quickly become tedious 457 Chapter 8 Using the Commands Window 458 The following function supposes you will supply as the location argument a data set containing one variable for each location When run this function returns the mean temperature for each location temp means lt function location apply location 2 mean na rm T The apply function here applies the function mean to the columns of the data set provided as the argument location If we had used a 1 instead of a 2 in the call the function would be applied to the rows instead The last argument to apply is the argument we want to supply to mean namely na rm Using Spotfire S in Batch Mode USING SPOTFIRE S IN BATCH MODE Once you ve created a function to do a complicated analysis and verified that it works you may want to set it to work on a very large data set Complicated analyses on very large data sets can take a long time however Detailed information on using the batch commands for Windows and Unix can be found in Chapter 13 Verbose Logging of the Application Developer s Guide Batch mode provides a way to perform intensive computations without your constant attention The SBATCH command has the following form from a Command Prompt sometimes called a DOS Prompt Splus SBATCH args inputfile where inputfile is the input file you create for use in the batch job Note that you must specify the command in uppercase le
371. n deviance to warrant the inclusion of the additional terms That is the extra complexity is justified by an improvement in goodness of fit In our example the p value of 0 035 suggests that Age and or Number add extra information useful for predicting the outcome Analysis of Deviance Table Response Kyphosis Terms Resid Df Resid Dev Test 1 Age Number Start 77 61 37993 2 Start 79 68 07218 Age Number Df Deviance Pr chn 1 2 Z 6 692253 0 03522052 Cluster Analysis CLUSTER ANALYSIS Compute Dissimilarities In cluster analysis we search for groups clusters in the data in such a way that objects belonging to the same cluster resemble each other whereas objects in different clusters are dissimilar A data set for clustering can consist of either rows of observations or a dissimilarity object storing measures of dissimilarities between observations K means partitioning around medoids and monothetic clustering are all algorithms that operate on a data set Partitioning around medoids fuzzy clustering and the hierarchical methods take either a data set or a dissimilarity object The clustering routines themselves do not accept nonnumeric variables If a data set contains nonnumeric variables such as factors they must either be converted to numeric variables or dissimilarities must be used How we compute the dissimilarity between two objects depends on the data type of the original variables By default num
372. n outputting date data The available choices mirror those in your Windows Regional Options the default value for this field is the current Windows default 529 Chapter 11 Customizing Your Spotfire S Session Time format Select the format you prefer to use when outputting time data The available choices mirror those in your Windows Regional Options the default value for this field is the current Windows default Note The selections you make in the Date format and Time format fields persist only for the current session To preserve your settings from session to session clear the Use Regional Options for output check box in the Date Time Formats group on the Startup page of the dialog Startup The Startup page of the General Settings dialog is shown in Figure 11 4 ixi General Data Startup Computations Open at Startup J Prompt for project folder I Select Data Dialog IV Show splash screen MV Command Line T Update project prefs I Object Explorer IV Prompt to update project prefs I Set S_PROJ to Working Directory eA EEE Register all OLE objects ed EEOSE Cancel Figure 11 4 The Startup page of the General Settings dialog Open at Startup group Choose whether to have Spotfire S automatically open at startup the Select Data dialog the Commands window and or the default Object Explorer 530 Changing Defaults and Settings Other Set S_PROJ to Working Dire
373. n the Cox Proportional Hazards dialog 2 Type leukemia in the Data Set field 3 Enter the Formula Surv time status group or click the Create Formula button to construct the formula The Surv function creates a survival object which is the appropriate response variable for a survival formula To use the Formula builder click the Create Formula button In the dialog that appears highlight the time variable and click the Time 1 button Highlight the status variable click the Censor Codes button and then click Add Response Finally highlight the group variable and click Main Effect This generates the formula Surv time status type right group By default the censoring argument type is set to right when only one time variable is given Thus this formula is equivalent to Surv time status group 316 Parametric Survival Survival 4 Select the Survival Curves check box on the Plot page 5 Click OK A summary of the fitted model appears in the Report window and a plot of the survival curve with confidence intervals appears in a Graph Sheet Parametric regression models for censored data are used in a variety of contexts ranging from manufacturing to studies of environmental contaminants Because of their frequent use for modeling failure time or survival data they are often referred to as parametric survival models In this context they are used throughout engineering to discover reasons why engineered produc
374. n use the Create Formula button to create complicated linear models and learn the notation for model specifications The online help discusses formula creation in detail 4 Click OK to fit the robust LTS regression model A summary of the model appears in the Report window Stepwise Linear Regression Regression One step in the modeling process is determining what variables to include in the regression model Stepwise linear regression is an automated procedure for selecting which variables to include in a regression model Forward stepwise regression adds terms to the model until additional terms no longer improve the goodness of fit At each step the term is added that most improves the fit Backward stepwise regression drops terms from the model so long as dropping terms does not significantly decrease the goodness of fit At each step the term is dropped whose removal least degrades the fit Stepwise regression also has the option of alternating between adding and dropping terms This is the default method used Performing stepwise linear regression From the main menu choose Statistics gt Regression gt Stepwise The Stepwise Linear Regression dialog opens as shown in Figure 6 39 Stepwise Linear Regression _ p x Model Results m Data r Stepping Options Data Set air 7 Stepping Direction both x Weights v Print a Trace of All Fits Subset Rows with r Save Model Object M Omit R
375. nbinned equivalent to our contingency table This is the format expected by the Mantel Haenszel Chi Square Test dialog We use the mantel raw data in the example analysis below Statistical inference We use the Mantel Haenszel Chi Square Test dialog to test the independence between cancer status and passive smoking status 1 Open the Mantel Haenszel s Chi Square Test dialog 2 Type mantel raw in the Data Set field Select Group as Variable 1 Passive as Variable 2 and Smoker as the Stratification Variable 4 Click OK A summary of the test appears in the Report window The p value of 0 0002 indicates that we reject the null hypothesis of independence between cancer status and passive smoking The chi square test performs a Pearson s chi square test on a two dimensional contingency table This test is relevant to several types of null hypotheses statistical independence of the rows and columns homogeneity of groups etc The appropriateness of the test to a particular null hypothesis and the interpretation of the results depend on the nature of the data at hand In particular the sampling scheme is important in determining the appropriate of a chi square test 255 Chapter 6 Statistics 256 The pvalue returned by a chi square test should be interpreted carefully Its validity depends heavily on the assumption that the expected cell counts are at least moderately large a minimum size of five is often quoted as a rule
376. ncluded in the new data set but their order is randomly permuted The Distribution Functions dialog computes density values cumulative probabilities or quantiles from a specified distribution The purpose of this dialog is to generate distributional information from data sets or sequences of numbers We can use it to calculate p values and rejection regions for our tests or to plot and visualize a variety of distributions Computing distribution functions From the main menu choose Data Distribution Functions The Distribution Functions dialog opens as shown in Figure 6 86 367 Chapter 6 Statistics 368 Distribution Functions Anan snapeik snape 2i Honan Semple SIZE Figure 6 86 The Distribution Functions dialog Example l Calculating p values The Distribution Functions dialog can be used to generate rejection regions and p values for statistical tests For example suppose we conduct a two sided pooled test Our null and alternative hypotheses are as follows Ay Wy Ho Ay M Ho Our statistic is 2 10 with 12 degrees of freedom What is the p value for this test 1 Open an empty data set by clicking the New Data Set button E on the Standard toolbar Random Numbers and Distributions 2 Enter the values 2 10 and 2 10 in the first column Since the t distribution is symmetric about zero we calculate probabilities at both 2 10 and 2 10 to obtain values in both tails Chang
377. ncontiguous 29 rows a single 29 contiguous 29 in a special order 29 noncontiguous 29 Data window toolbar 15 dBase files hints for importing and exporting 190 Decrease Precision button 35 Decrease Width button 33 defaults setting object 523 degrees of freedom 218 Delete button 389 405 deleting columns 46 data all the data in a Data window 44 in a block of cells 44 in a cell 44 in a column 45 in a row 46 rows 47 delimiter matching automatic 472 delimiters character string 431 Density Cumulative Probability or Quantile dialog 368 371 density values 367 Details button 387 dialog fields automatically filled in 201 distribution functions 368 371 Distribution Functions dialog 51 divisive hierarchical method 336 document background colors 537 document objects 380 dot plots 127 drag and drop in the Object Explorer 404 E Edit data function 440 editing data 19 438 engine objects 379 environment variable S_FIRST 551 error bar plots 148 error messages 419 ethanol data 97 evaluation error messages during 419 interrupting 419 exact binomial test 244 examples ANOVA of coagulation data 235 one sample speed of light data 55 214 two sample weight gain data 225 Index exbarley data 211 Excel files hints for importing and exporting 193 Excel add in creating graphs 498 disabling during Setup 496 disabling in Excel 497 enabling in Excel 496 error handling 502 installing automatically 496 with Setup 496 me
378. ncy Financial Date Date amp Time Time and Elapsed_H M S To change the format type of a column do the following Open the column properties dialog as discussed on page 31 Select a different format type from the Format Type dropdown list and click OK A column s display precision affects only the way numbers are displayed it has no effect on internal computations which always use the maximum precision available To change the display precision of a column do one of the following To increase or decrease the display precision click in the column and then click the Increase Precision button 60 or ne 00 l the Decrease Precision button 0 respectively on the Data window toolbar Open the column properties dialog as discussed on page 31 In the Precision text box type the desired number of digits to be displayed after the decimal the maximum number allowed is 17 and click OK You can change your column default settings for justification precision width etc to reflect your formatting preferences For example you might prefer to have a different default width for character columns than for numeric columns 35 Chapter 2 Working With Data Formatting Rows Adding or Changing a Row Name Creating a Row List 36 To set your preferred column defaults do the following 1 Open the column properties dialog as discussed on page 31 2 Make any changes that you want to retain as your new default sett
379. ndow By selecting your columns in the right pane you can then graph them using the plot palettes Double clicking objects of different classes yields different behavior in the Object Explorer For example double clicking an object of class data frame or matrix launches the Data window while double clicking an object of class 1m an object constructed by the 1m function displays a summary of the object in a Report window Note shaded Only certain column objects of class numeric integer character logical single complex factor and timeDate may be opened for editing in a Data window Attempting to open objects of certain other classes in a Data window causes Spotfire S to issue a warning that the object will be opened in read only mode In read only mode columns in the Data window are slightly To convert such an object into an editable data frame first open it in read only mode and then convert it to a data frame by clicking the Convert to Data Frame button A on the Data window toolbar or choosing Data gt Convert to Data Frame from the main menu 403 Chapter 7 Working With Objects and Databases Copying and Moving Objects You can also launch the Data window for objects such as data frames and matrices by right clicking the object and selecting Edit from the shortcut menu To edit other types of objects use the object s properties dialog You can open the dialog by right clicking the object and select
380. ndow or between Copying Data different Data windows by using a variety of techniques discussed below Moving and To move or copy a cell or block of cells by dragging do the Copying Cells and following Blocks 1 Select the cell or block of cells you want to move or copy 2 Position the cursor within the selected cell or block The cursor becomes an arrow as shown in Figure 2 17 p ethanol Ioj x Figure 2 17 Selecting a block of cells in a Data window 37 Chapter 2 Working With Data 3 Drag the selected cell or block to the new location To move the cell or block simply release the mouse button To copy the cell or block press and hold down the CTRL key while releasing the mouse button See Figure 2 18 Note Moving or copying data to a target location that already contains data overwrites the existing data Also note that when you move a block of cells Spotfire S fills the empty cells in the old location with NAs which denote missing values H ethanol ioj xj P ethanol iol x 12 00 12 00 a7 a 12 00 PET l 12 00 1 02 12 00 1 19 3 12 9 00 1 00 0 64 9 00 1 23 Figure 2 18 Moving above lefi and copying above right a block of cells in a Data window 0 76 Hint When you use drag and drop to move or copy data between Data windows be sure to arrange your windows so that you can see both the source and the
381. nds with 6 because you entered 5 values on the first line Reading an ASCII File Importing and Editing Data When you finish entering data press RETURN in response to the prompt and Spotfire S returns to the Spotfire S command prompt gt The complete example appears on your screen as follows gt diftf is lt scant e068 ald ot ot 05 Gr 31 12 23 05 03 Tis 282 223 saoe 30 158 gt Entering data from the keyboard is a relatively uncommon task in Spotfire S More typically you have a vector data set stored as an ASCII file which you want to read into Spotfire S An ASCII file usually consists of numbers separated by spaces tabs newlines or other delimiters Let s say you have a file called vec dat in your working directory containing the following data 62 60 63 59 63 67 71 64 65 66 88 66 71 67 68 68 56 62 60 61 63 64 63 59 You read the file vec dat into Spotfire S by using the scan command with vec dat as an argument gt x lt Scant Vec dat The quotation marks around the vec dat argument to scan are required You can now type x to display the data object named x that you have read into Spotfire S from the file vec dat If the file you want to read is not in your working directory you must use the entire path name So if the file vec dat is in a directory with path name c mabel test vec dat then type gt vec data lt scan c mabel test vec dat Note that you
382. ng data When importing most types of files typically you only need to specify the file s name and type By default Spotfire S imports the data into a new data set and displays it in a Data window Note If you prefer not to have your imported data automatically displayed in a Data window choose Options gt General Settings from the main menu click the Data tab and clear the Show Imported Data in View check box Importing From a Data File 168 When exporting to most types of files usually all you need to do is specify the data set you want to export and the new file s name and type By default Spotfire S exports the data into a new data file using default settings The Import From File dialog shown in Figure 5 1 below consists of three tabbed pages labeled Data Specs Options and Filter To open the dialog do one of the following e From the main menu choose File gt Import Data gt From File e From the main menu choose Data gt Select Data In the Source group of the Select Data dialog click the Import File radio button and then click OK Importing From and Exporting to Data Files Import From File o x Data Specs Options Filter gt From File Name Browse File Format Unspecified file format z To Data set sDF1 X Create new data set Add to existing data set Start col END z Cancel Appt current Figure 5 1 The Da
383. ngle set of plots Different types of plots scatter plots box plots histograms provide different types of information but deciding which plots to use is fairly straightforward With multidimensional data visualization is more involved In addition to one and two variable relationships variables may have interactions such that the relationship between two variables changes depending on the values of the other variables In this section we discuss both standard multidimensional visualization techniques and novel techniques such as Trellis graphics We begin with scatter plots and scatterplot matrices for multidimensional data Next we describe Trellis graphics which are particularly useful for discovering higher dimensional relationships in data and then we present plots designed for three dimensional data Finally we conclude with the brush and spin interactive dynamic graphics tools We can use 2D scatter plots to view multidimensional data by varying symbol attributes such as color style and size Scatterplot matrices are powerful graphical tools that enable you to quickly visualize multidimensional data Often when faced with the task of analyzing data the first step is to get familiar with the data Generating a scatterplot matrix greatly facilitates this process The fuel frame data set contains information on 60 makes of cars taken from the April 1990 issue of Consumer Reports The variables are e Weight Automobi
384. ngside their equivalents in S PLUS Table 10 1 Excel data types and S PLUS equivalents Excel Data Type S PLUS Equivalent General Character Number Complex Currency Currency Accounting Date Date Date amp Time Time Decimal Percent Factor Fraction Financial Scientific Logical Text Mixed Special Number Custom Scientific Time 489 Chapter 10 Using Spotfire S With Other Applications 7 Click Next 8 As shown in Figure 10 6 the final page of the wizard asks you to specify a name for the new S PLUS data frame Type the name of your choice in the text box and click Finish to close the wizard and create the link from Excel to Spotfire S Excel to S PLUS Link Wizard x Enter the name of an S PLUS data frame below The data from Excel will be copied to this data frame overwriting any existing contents trial data If you select the option below a comment will be added to the top left cell of the data range allowing S PLUS to automatically recreate this link the next time the worksheet is opened in S PLUS I Allow S PLUS to store link information in Excel Cancel Help Figure 10 6 Naming the new data frame in Spotfire S Hint If you are working with the same Excel worksheet regularly and do not want to go through the process of creating a link using the wizard each time you can allow Spotfire S to store information in Excel that will
385. nostic plots You can access these plots by clicking the seven page tabs at the bottom of the Graph Sheet The plots do not reveal any significant problems in our model The Report window displays the results of the ANOVA Random effects ANOVA is used in balanced designed experiments where the treatment effects are taken to be random The model must be balanced and the model must be fully random Only single strata designs are allowed For mixed effect models use the Linear Mixed Effects dialog Fitting a random effects ANOVA model From the main menu choose Statistics gt ANOVA gt Random Effects The Random Effects Analysis of Variance dialog opens as shown in Figure 6 50 301 Chapter 6 Statistics Random Effects Analysis of Yariance Bee Model Options Results Plot m Data Data Set pigment X Weights b Subat Hows wae r Save Model Object M Omit Rows with Missing Values Save As Variables _ Dependent Independent Formula Moisture Batch Sample in Batch Create Formula Cancel Apply current Help Figure 6 50 The Random Effects Analysis of Variance dialog Example The pigment data set has 60 rows and 4 columns The rows represent 15 batches of pigment for which 2 samples were drawn from each batch and 2 analyses were made on each sample These data are from a designed experiment of moisture content where samples are nested within batch We
386. nsion slot Dim You can use the dim function to display the dimensions of an object gt my mat lt matrix 1 8 4 2 gt dim my mat ij 42 This shows that the dimension of the matrix my mat is 4 rows by 2 columns Matrix objects also have length and mode which correspond to the length and mode of the vector in the Data slot You can use the length and mode functions to view these characteristics of a matrix Like vectors a matrix object has a single mode This means that you cannot create for example a two column matrix with one column of numeric data and one column of character data For that you must use a data frame S PLUS contains an object called a data frame which is very similar to a matrix object A data frame object consists of rows and columns of data just like a matrix object except that the columns can be of different modes The following object baseball df is a data frame 427 Chapter 8 Using the Commands Window List Objects 428 consisting of some baseball data from the 1988 season The first two columns are factor objects codes for names of players the next two columns are numeric and the last column is logical gt baseball df bat ID pitch ID event typ outs play err play rl pettg001 clemr001 2 1 r2 whitl001 clemr001 14 r3 evand001 clemr001 3 r4 trama001 clemr001 2 r5 andeb001 morrj001 3 r6 barrm001 morrj00l 2 r7 boggw001 morrj001 21 r8 ricej001 morrj00l 3 FPOrRrFrFrHFO i ie a ad is
387. nu for 498 modifying layout properties of graphs 499 modifying plot properties of graphs 499 overview of 495 removing automatically 497 with Setup 497 selecting data 500 for conditioning graphs 502 toolbar for 498 Excel Link Wizards 484 Excel worksheet creating in Spotfire S 484 opening in Spotfire S 484 Exchange Columns dialog 50 Exchange Rows dialog 50 Expand Grid dialog 51 Expand Item button 386 exploratory analysis speed of light data 216 227 Explorer Pages deleting 389 inserting 388 exporting data to data files 168 174 to ODBC tables 183 Export To File dialog 174 Export to ODBC dialog 184 expressions multiple line 418 Extra Plots palette 119 557 Index 558 F factor analysis 341 Factorial Design dialog 264 FASCII files hints for importing and exporting 191 Fill Numeric Columns dialog 50 filtering using folders 397 saving as the default 402 Find button 469 finding objects 396 Find Objects button 396 Find Objects dialog 396 Fisher s exact test 248 Folder dialog Advanced page of 400 Folder page of 398 Objects page of 399 folders collapsing 386 deleting 389 expanding 386 filtering with 397 saving as the default 402 inserting 389 formulas 201 freedom degrees of 218 Friedman rank test 241 fuel frame data 87 functions arguments to abbreviating 437 defaults for 436 formal 437 optional 436 required 436 supplying by name 437 supplying by value 437 attach 430 c 431 calling 417 431 definit
388. o display object details in the right pane see page 391 that include more than just the data class and search path position an object that is masked by another object existing earlier in the search path will be displayed with a red X painted through its icon For example if you create a new data set named air it is automatically stored in the working data which occupies position one in the search path However there is a built in data object named air that is stored in the data database in position 383 Chapter 7 Working With Objects and Databases 384 four of the search path Therefore your data object air will mask the system data object of the same name and the system data object air will show a red X painted through its icon as shown in Figure 7 3 Contents of database data aL Data Object Pos Data Class Dimensior a E Graphs AT 4 character 227 E3 Reports Z min script id 4 integer 2 El Scripts Al Copyright 4 character 1 E EN SearchPath mal Original 4 list 2 w E D Program Files Insight ma PostScript Options 4 list 24 H E splus 2s Program 4 expression H E stat Bair 4 data frame 11184 H E TR akima x 4 numeric 50 wE trellis R akima y 4 numeric 50 w E nlme3 IR akima z 4 numeric 50 H E menu Di animals 4 data frame 20x6 H E sgui EJ author count 4 matrix 12x26 E winspi E auto stats 4 matrix 74x12 H E main m axis break table 4 list 4 E axis
389. o plot in the Rug Tile Variable field A high density plot that shows the average value of the specified variable for observations in each leaf is plotted beneath the tree plot Specify a name in the Save As field to save a vector of the average values This tool is not interactive Snip use this tool to create a new tree with some splits removed Select a node on the tree plot to print the total tree deviance and what the total tree deviance would be if the subtree rooted at the node were removed Click a second time on the same node to snip that subtree off and visually erase the subtree This process may be repeated any number of times Right click to leave the selection mode Specify a name in the Save As field to save the snipped tree Tile specify a variable to plot in the Rug Tile Variable field A vertical bar plot of the variable is plotted beneath the tree plot Factor variables have one bar per level and numeric variables are quantized into four equi sized ordered levels Tree Specify a name in the Save As field to save a matrix of frequency counts for the observations in each leaf This tool is not interactive Using the tree tools From the main menu choose Statistics Tree gt Tree Tools The Tree Tools dialog opens as shown in Figure 6 61 Tree Tools ME E Model Selection gt r Tree Tool Model Object myttee Tool Type C Browse Name String Match C Bul C Histogram Variables to Plot Iden
390. o select all the data in the data set and then choose Clear from the Edit or shortcut menu To remove a cell or block of cells do one of the following 44 Manipulating Data Select the cell or block of cells and then press the DELETE key or choose Cut from the Edit or shortcut menu From the main menu choose Data gt Remove gt Block The Remove Block dialog opens as shown in Figure 2 26 Remove Block o x From Data Set ethanol ba Columns NOx X Bows fes Cancel Appt current Figure 2 26 The Remove Block dialog In the Columns and Rows fields specify by column and row positions the cell or block of cells you want to remove and click OK Clearing and Clearing a column deletes the data in the column but otherwise Removing leaves the column s position name and formatting information Columns intact To clear a column do one of the following e Click in the column and then click the Clear Column button in on the Data window toolbar Select the column and choose Clear from the Edit or shortcut menu e From the main menu choose Data gt Clear gt Column The Clear Columns dialog opens as shown in Figure 2 27 Clear Columns Mm x From Data Set ethanol aa Columns NOx X ia eT aco f aA Help Figure 2 27 The Clear Columns dialog Select the column you want to clear from the Columns dropdown list and click OK 45 Chapter 2 Workin
391. o use the Insert Rows dialog to insert multiple rows Simply type the number of rows you want to insert in the Count text box 43 Chapter 2 Working With Data Deleting Data When deleting data in a Data window you can either clear the data values leaving the cells intact or you can remove both the cells and their contents and shrink the size of the data set Note that when you clear data Spotfire S replaces the values in the cells with NAs which denote missing values Note When you clear a cell block column or row by pressing the DELETE key or by choosing Clear from the Edit or shortcut menu the data are not placed in the clipboard To erase the data and place them in the clipboard choose Cut instead Clearing and To clear a cell or block of cells do one of the following Removing Cells e Select the cell or block of cells and choose Clear from the and Blocks Edit or shortcut menu e From the main menu choose Data gt Clear gt Block The Clear Block dialog opens as shown in Figure 2 25 Clear Block Mm x From Data Set ethanol X Columns NOx X Rows faa By Cancel Apply K current Figure 2 25 The Clear Block dialog In the Columns and Rows fields specify by column and row positions the cell or block of cells you want to clear and click OK Hint To clear all the data in a Data window click in the empty shaded area in the upper left hand corner of the Data window t
392. o your most recent action or restore the data set to its original state at the beginning of the session To undo your most recent action do one of the following e Press CTRL Z or click the Undo button on the Standard toolbar From the main menu choose Edit Undo 19 Chapter 2 Working With Data To restore a data set to its initial state do the following 1 Click the Restore Data Objects button on the Standard toolbar or choose Edit gt Restore Data Objects from the main menu The Restore Data Objects dialog opens as shown in Figure 2 7 Restore Data Objects x Select objects to restore Choose whether to restore to the previous state or to the initial state Click OK to accept changes or CANCEL to do nothing ema created removed Deselect All Select All For selected objects Restore to Previous State C Restore to Initial State Figure 2 7 The Restore Data Objects dialog 2 Select the data set from the list of objects displayed in the dialog 3 Click the Restore to Initial State radio button and then click OK Note You can also perform a single undo using the Restore Data Objects dialog Simply select the data set click the Restore to Previous State radio button and click OK To redo an undo just perform one of the above procedures again Saving Data By saving your data in a special internal database Spotfire S safeguards your data with no intervention
393. oduction to 198 normal distributions 371 p values 368 Index random numbers generating 372 random numbers and distributions 366 random sample generation 366 regression 270 rejection regions calculating 370 savings results from an analysis 203 Statistics menu for 199 200 summary 204 451 common functions for 451 tabular summaries 211 Statistics menu 199 200 Student s t confidence intervals 217 Student s t significance test p values 217 Student s t tests 215 217 228 Subset dialog 51 subsetting 442 from matrices 443 from vectors 442 summary statistics 204 451 common functions for 451 Summary Statistics dialog 204 216 supersmoother 357 surface plots 110 surface plots 3D 153 survival analysis Cox proportional hazards 315 SYBASE files hints for importing and exporting 190 194 system databases 381 382 T tabular summaries 211 Tabulate dialog 51 211 testing hypothesis 452 text as symbols plots 138 text output routing 537 TIBCO Web site 9 time series 567 Index 568 autocovariance correlation 359 autoregressive integrated moving average 361 time series plots 76 85 Tip of the Day 9 ToolTips 382 387 in the Object Explorer 382 387 394 training courses 9 Transform dialog 50 350 Transpose Block dialog 50 Transpose Columns dialog 50 Transpose Rows dialog 50 treatment 236 ANOVA models 238 tree based models 322 Trellis graphics 159 introduction to 97 two sample tests 224 t test 224 Two s
394. of soybean is in the plot Variety the year the plot was planted Year the time each sample was taken time and the average leaf weight per plant weight We are interested in modeling weight as a function of Time in a logistic model with parameters Asym xmid and scal These parameters have both fixed and random effects The grouping variable is Plot 1 Open the Nonlinear Mixed Effects Models dialog 2 Type Soybean in the Data Set field Mixed Effects 3 Type the following Formula weight SSlogis Time Asym xmid scal This specifies that we want to predict weight by a function SSlogis of the variables Time Asym xmid and scal The SSlogis function is a sel f starting function used to specify the nonlinear model as well as provide initial estimates to the solver 4 Specify starting fixed effect parameter estimates in the Parameters name value field fixed c 18 52 7 5 5 Specify that Asym xmid and scal are the fixed effects variables by typing the following formula in the Fixed field under Effects Asym xmid scal 1 6 Specify that Asym xmid and scal are the random effects variables and that Plot is the grouping variable by typing the following formula in the Random field under Effects Asym xmid scal 1 Plot 7 Click OK A summary of the fitted model appears in the Report window 309 Chapter 6 Statistics GENERALIZED LEAST SQUARES Linear 310 Generalized least squares mode
395. of thumb Even when cell counts are adequate the chi square is only a large sample approximation to the true distribution of chi square under the null hypothesis If the data set is smaller than is appropriate for a chi square test then Fisher s exact test may be preferable Performing Pearson s chi square test From the main menu choose Statistics gt Compare Samples gt Counts and Proportions Chi square Test The Pearson s Chi Square Test dialog opens as shown in Figure 6 26 5 x Pearson s Chi Square Test Data Data Set vaccine h l i Results M y Save As IV Print Results r Options MV Apply Yates Continuity Correction m vanrabei vananez IV Data Set is a Contingency Table Cancel Appt current Figure 6 26 The Pearson s Chi Square Test dialog Example The data set shown in Table 6 8 contains a contingency table with results from Salk vaccine trials in the early 1950s There are two categorical variables for the Salk trials vaccination status which has the two levels vaccinated and placebo and polio status which has the three levels no polio non paralytic polio and paralytic polio Of 200 745 individuals who were vaccinated 24 contracted non paralytic polio 33 contracted paralytic polio and the remaining 200 688 did not contract any kind of polio Of 201 229 individuals Compare Samples who received the placebo 27 contracted non
396. of univariate observations or simply a batch of data In this section we examine a number of basic plot types useful for exploring the shape of the distribution of a one dimensional data object These visualization plots are simple but powerful exploratory data analysis tools that can help you quickly grasp the nature of the distribution of your data Such an understanding can help you avoid the misuse of statistical inference methods such as using a method appropriate only for a normal Gaussian distribution when the distribution is strongly non normal The first step in creating a plot is creating or locating the data of interest For large data sets you may prefer to store the data in a database or a spreadsheet such as Microsoft Excel For smaller data sets it is convenient to directly enter the data into a Data window We begin this section by creating an example data set the Michelson data exmichel In 1876 the French physicist Cornu reported a value of 299 990 km sec for c the speed of light In 1879 the American physicist A A Michelson carried out several experiments to verify and improve on Cornuw s value Michelson obtained the following 20 measurements of the speed of light 850 740 900 1070 930 850 950 980 980 880 1000 980 930 650 760 810 1000 1000 960 960 To obtain Michelson s actual measurements in km sec add 299 000 km sec to each of the above values The 20 observations can be thought of as observed va
397. ol Charts 346 Resample 351 Smoothing 355 Time Series 359 viii Contents Random Numbers and Distributions 366 References 376 Chapter 7 Working With Objects and Databases 377 Introduction 378 Understanding Object Types and Databases 379 Introducing the Object Explorer 385 Working With Objects 396 Organizing Your Work 406 Chapter 8 Using the Commands Window 413 Introduction 415 Commands Window Basics 416 S PLUS Language Basics 424 Importing and Editing Data 438 Extracting Subsets of Data 442 Graphics in Spotfire S 446 Statistics 451 Defining Functions 457 Using Spotfire S in Batch Mode 459 Chapter 9 Using the Script and Report Windows 461 Introduction 462 The Script Window 464 Script Window Features 472 Time Saving Tips for Using Scripts 475 The Report Window 480 Printing a Script or Report 482 Contents Chapter 10 Using Spotfire S With Other Applications Using Spotfire S With Microsoft Excel Using Spotfire S With SPSS Using Spotfire S With MathSoft Mathcad Using Spotfire S With Microsoft PowerPoint Chapter 11 Customizing Your Spotfire S Session Introduction Changing Defaults and Settings Customizing Your Session at Startup and Closing Index 483 484 503 509 516 521 522 523 550 553 INTRODUCTION Welcome to Spotfire S System Requirements Running Spotfire S Help Support and Learning Resources Online Help Online Manuals Tip of the Day Spotfire S on the Web Training Cou
398. olx m Data r Options Data Set catalyst x I Both Orderings for Each Pair Subset Rows with Eunction mean M Omit Rows with Missing Values r Layout ae Variables Rows 2 Dependent Yieid v Columns eo o Independent Temp Conc Cancel Appt current Figure 6 33 The Interaction Plot dialog Example We create interaction plots for the catalyst data set as follows 1 2 3 4 268 Open the Interaction Plot dialog Type catalyst in the Data Set field Select Yield as the Dependent variable CTRL click to select Temp Conc and Cat as the Independent variables Experimental Design 5 Change the number of Rows and number of Columns to 2 This specifies a 2 x 2 grid of plots 6 Click OK An interaction plot appears in a Graph Sheet For each pair of factors a set of lines is created showing the mean of Yield for each level of the second factor at each level of the first factor If the lines in a plot cross it suggests that an interaction is present between the two factors 269 Chapter 6 Statistics REGRESSION 270 Regression is the standard technique for assessing how various predictors relate to a response This section discusses the regression techniques available from the Statistics gt Regression menu Linear regression Predicting a continuous response as a linear function of predictors using a least squares fitting criterion
399. on page 220 2 Open the One sample Chi Square Goodness of Fit Test dialog The Distribution is normal by default Select qcc process as the Data Set 4 Select X as the Variable 5 For the chi square test we must specify parameter estimates for the mean and standard deviation of the distribution Enter 10 as the Mean and 1 as the Std Deviation If you do not know good parameter estimates for your data you can use the Summary Statistics dialog to compute them 223 Chapter 6 Statistics Two Sample Tests Two Sample t Test 224 6 Since we are estimating the mean and standard deviation of our data we should adjust for these parameter estimates when performing the goodness of fit test Enter 2 as the Number of Parameters Estimated 7 Click OK A summary of the goodness of fit test appears in the Report window Spotfire S supports a variety of statistical tests for comparing two population parameters That is we test the null hypothesis that Hy 0 where and are the two population parameters e Two sample test a test to compare two population means ll and Ho For small data sets we require that both populations have a normal distribution Variations of the two sample test such as the paired rf test and the two sample t test with unequal variances are also supported e Two sample Wilcoxon test a nonparametric test to compare two population means uy and uo As with the t test we test
400. ong and you don t want to look at all of it To interrupt Spotfire S simply press the ESC key Don t be afraid of making mistakes when typing commands in the Commands window you will not break anything by making a mistake Usually you get some sort of error message after which you can try again Here is an example of a mistake made by typing an improper expression gt LZ Ad Problem Invalid object supplied as function Use traceback to see the call stack Dumped In this example we typed something that Spotfire S tried to interpret as a function because of the parentheses However there is no function named 5 419 Chapter 8 Using the Commands Window Quitting Spotfire S Command Line Editing 420 To quit Spotfire S from the Commands window use the q function gt qQ The are required with the q command to quit Spotfire S because q is an S PLUS function and parentheses are required with all S PLUS functions The Commands window allows you to recall and edit previously issued S PLUS commands The up and down arrows can be used to scroll backward and forward through the list of commands typed during the session Typing errors can be easily corrected using standard Windows editing commands and new commands can be constructed based on previously issued commands For example type the following expression and press ENTER gt Im Mileage Weight data fuel frame If you now decide t
401. ools palette by clicking the Graph Tools button ET on the Graph toolbar 2 Click the Select Data Points button on the Graph Tools palette The mouse cursor becomes a cross hair with a little rectangle annotation t 63 Chapter 3 Exploring Data 3 Drag a rectangle around the two outliers to select them They now appear highlighted in red by default You can highlight additional points by pressing the CTRL key while releasing the mouse button Note When you select points in a scatter plot they are also selected in any Data window in which the data are displayed 4 Change the cross hair mouse pointer back to the regular mouse pointer by clicking the Select Tool button X on the Graph Tools palette 5 Close the Graph Tools palette when you are done Click a cell in the Data window to deselect all points Scatter Plots You can fit a straight line to your scatter plot data and superimpose With Line and the fit with the data Such a fit helps you visually assess how well the data conform to a linear relationship between two variables When the linear fit seems adequate the fitted straight line plot provides a good visual indication of both the slope of bivariate data and the variation of the data about the straight line fit Curve Fits 64 Least Squares Straight Line Fits Robust Line Fits Visualizing Two Dimensional Data You can fit a straight line to the exmain bivariate data by the m
402. oose Paste from the Edit menu CTRL V Using Undo in a Script Window Using Find and Replace The Script Window The Script window has its own Undo capability which is separate from the Undo used when working with Graph Sheets and Data windows While you edit a script you cannot undo or redo any actions for Graph Sheets and data objects While editing scripts you can undo your typing changes by choosing Edit Undo from the menus As soon as you leave the Script window your Undo queue for Graph Sheets or Data windows is restored To undo the last change made in a Script window click the Undo button on the Standard toolbar or choose Undo from the Edit menu or type CTRL Z The last change you made will be undone If you need to restore the Script window to its previous state before your last undo you can Undo again and the change you just undid will be restored To review or change text in a Script window use the Find or Replace options You can use Find to locate specific occurrences of text in your script You can use Replace to locate the text and replace it throughout your script Find and Replace can be used for certain words phrases or sequences of characters such as whole commands Spotfire S will replace specified text throughout a script unless you select a part of the script It is a good idea to save your script before you use Replace so that if you do not like the results you can close the Script window
403. or Precedence Hierarchy S PLUS Language Basics The evaluation of S PLUS expressions follows a precedence hierarchy shown below in Table 8 3 Operators appearing higher in the table have higher precedence than those appearing lower operators on the same line have equal precedence Table 8 3 Precedence of operators Operator Use Component selection CC Subscripts elements Exponentiation Unary minus Sequence operator Me DID ae Modulus integer divide matrix multiply Multiply divide o Add subtract a aa Comparison Not amp amp amp And or i Formulas SSS FE 1 SEIS Assignments 435 Chapter 8 Using the Commands Window Note Optional Arguments to Functions 436 When using the operator if the base is a negative number the exponent must be an integer Among operators of equal precedence evaluation proceeds from left to right within an expression Whenever you are uncertain about the precedence hierarchy for evaluating an expression you should use parentheses to make the hierarchy explicit S PLUS shares a common feature with many computer languages in that the innermost parentheses are evaluated first and so on until the outermost parentheses are evaluated For example let s assign the value 5 to a vector of length 1 called x FE gt 5 and use the sequence operator to show the difference between how the expression
404. or all the factor variables created from character strings Otherwise the levels are defined in the order in which they are read from the data file Labels as numbers When selected SAS and SPSS variables that have labels are imported as numbers Otherwise the value labels are imported Century cutoff When importing an ASCII text file this field specifies the origin for two digit dates Dates with two digit years are assigned to the 100 year span that starts with this numeric value The default value of 1930 thus reads the date 6 15 30 as June 15 1930 while the date 12 29 29 is interpreted as December 29 2029 Options page ASCII group Format string This field is required when importing a formatted ASCII FASCII text file A format string specifies the data types and formats of the imported columns For more information on the syntax accepted by this field see Format Strings on page 191 Delimiter Specify all characters used to separate elements in an ASCII file By default Spotfire S uses the comma Separate Delimiters When selected the separator is strictly a single character otherwise repeated consecutive separator characters are treated as one separator Importing From and Exporting to Data Files Date format Select the format you prefer to use when importing date data The available choices mirror those in your Windows Regional Options the default value for this field is the current Windows default Time
405. ored in your working data To create an object do the following 1 Right click the folder s icon and select Insert from the shortcut menu The Create Object dialog opens as shown in Figure 7 15 Create Object x Object Classes Cancel character 3l ClassInfo Ei factor FunctionInfo GraphSheet integer list Menultem numeric ali H Iom ENR Figure 7 15 The Create Object dialog Working With Objects 2 Select the type of object you want to create and click OK Note Because the working data is the first database in the search path if you create an object with the same name as an S PLUS system object your object will mask the system object An object that is masked by another object of the same name earlier in the search path is displayed with a red X over its icon To eliminate the conflict simply rename the masking object Selecting Objects Viewing and Editing Objects Selecting an object in the Object Explorer is easy simply click its icon The Object Explorer is especially useful for selecting objects that are difficult to select in other views such as overlaid graphical elements in a Graph Sheet When you select graph element objects in the right pane of the Object Explorer they are also selected in the Graph Sheet in which they reside Similarly if a data set is open in a Data window selecting columns in the right pane also selects them in the Data wi
406. oring Data 98 4 Close all windows other than the current Graph Sheet and the ethanol Data window From the main menu choose Window P Tile Vertical to display the two windows side by side Adjusting the smoothing parameter Right click any symbol of the plotted loess line to display the shortcut menu for the plot and select Smooth Sort In the Loess Friedman Specs group type 1 in the Span box and click OK An increase in the span parameter results in a smoother line as shown in Figure 3 38 ow o o 00 ow o oo o o o0 o o0oo0ojoo o o o oo w o o ojoo o o oo 0000 ooo o 000000 o o Figure 3 38 Compression ratio versus nitrogen oxide in the ethanol data set span of 1 Trellis Graphs Visualizing Multidimensional Data There appears to be little dependence of the oxides of nitrogen on the compression ratio However hidden from this scatter plot is the fact that E is varying as we move from point to point The next step is to condition the plot on E to further examine the relationship 1 Select the E column in the Data window Move the cursor to the middle of the column Press and hold the mouse button While holding down the button drag the cursor to the top of the graph As the cursor passes over the top of the graph a rectangular drop target appears w ow o 2 Release the mouse button when the cursor is within the rectangle The graph is redrawn in panels represent
407. ormation on programming editable graphics see Chapter 6 Editable Graphics Commands in the Guide to Graphics Dragging If you drag an S PLUS function object from an Object Explorer Function window onto a Script window the function definition is expanded in the Script window This is a very convenient shortcut to edit a function s body If you want to test your changes select Run in the Objects Into a Script Window Script menu or click the Run button and the new function definition is automatically sent to the Spotfire S interpreter 479 Chapter 9 Using the Script and Report Windows THE REPORT WINDOW 480 The Report window is similar to the Script window They both are primarily text windows that can be opened and saved via the File menu and both are editable Unlike the Script window the Report window does not deal with programs or scripts The Report window is a placeholder for the text output resulting from any operation in Spotfire S You must select the Report window as your preference for text output See the sections below on redirecting text output for how to set this option Text in the Report window is editable The Report window toolbar shown in Figure 9 9 allows you to modify the size font and style of text that appears in a Spotfire S report Reportindow Courier New Figure 9 9 The Report window toolbar In addition to basic editing features such as cut copy and paste the Rep
408. ort window supports the following operations e Typing from the keyboard point and click highlight drag and drop etc Undo Cut Copy Paste Find Replace and so on are supported via the Edit menu and context sensitive menu right click Pasting from the clipboard including graphics and other OLE objects is supported for RTF files only In particular if you attempt to save the Report window as a text file and have graphical images pasted in it the graphics will not be saved and you will not get an error message e Fonts are supported via the context sensitive menu right click and the Format menu Fonts are supported in RTF mode only e User input via the keyboard may go through the Report window trickling input The Report Window The Report window is saved by default in Rich Text Format rtf Reports may also be saved in plain text txt format or with a srp extension the extension that was used in earlier versions of Spotfire S Plain text can be used with the widest variety of programs and is relatively fast RTF has more capability but RTF files are bigger and slower than their plain text counterparts To save a Report window choose File gt Save or Save As from the main menu To create a new Report window choose File gt New from the main menu select Report File and click OK A new Report window will appear e To open an RTF file in a Report window choose File gt Open from the
409. ot me A 0 48 7 B 0 46 7 Cc F D gt 0 44 7 E 0 42 5 H JK M O 0 40 7 L N P QR A Y series plot is just a special kind of line plot that plots multiple series on the same graph The data are plotted along the vertical axis against a common automatically generated integer sequence along the horizontal You can replace the integer sequence with more appropriate labels such as times or dates by using the X Axis Labels dialog For details see the online help To create a Y series plot iii y series Of x 1 1 Select the y1 and y2 columns 2 Click the amp button on the Plots 2D palette XY Pairs Line Plots Plotting Two Dimensional Data Y series plot as 1993 1994 1995 1996 1997 1998 1999 2000 An XY pairs line plot lets you plot multiple sets of x and y pairs on a common set of axes To create an XY pairs line plot EE Peles 1 Select the x1 yl x2 and y2 columns 0 49 0 48 0 46 2 Click the amp button on the Plots 2D palette XY pairs line plot Ea 0 47 7 0 43 4 0 39 4 0 35 T T T T T T 5 10 15 20 25 30 35 139 Chapter 4 Creating Plots Grouped Box A grouped box plot consists of side by side box plots describing Plots multiple columns of data The number of rows in the x column determines the number of boxes and the number of rows in the y column must be evenly divisible by the number of rows
410. ota ten varieties of barley were grown for each of two years 1931 and 1932 The data are the yields for all combinations of site variety and year so there are a total of 6x 10x2 120 observations Chapter 3 Exploring Data examines these data in a Trellis plot In this example we tabulate the data in a variety of ways To tabulate the barley data 1 Open the Tabulate dialog 2 Type barley in the Data Set field 3 Click the variable yield and then CTRL click to select year the order of selection is important 4 Click OK 211 Chapter 6 Statistics The table below appears in a Report window xxx Table of yield year in barley 1932 1931 13 92000 thru 22 64667 13 2 22 64667 thru 31 37334 18 20 31 37334 thru 40 10002 17 17 40 10002 thru 48 82669 10 13 48 82669 thru 57 55336 1 4 57 55336 thru 66 28003 1 4 The yield has been divided into six bins Note how the yield tends to be higher in year 1931 than in year 1932 More than two variables can be selected in the Tabulate dialog For example try selecting yield year and variety in that order to further subdivide the table into the ten varieties of barley 212 Compare Samples COMPARE SAMPLES One Sample Tests One Sample t Test Spotfire S supports a variety of statistical tests for testing a hypothesis about a single population Most of these tests involve testing a parameter against a hypothesized value That is the null hyp
411. oted that the Kolmogorov Smirnov Goodness of Fit Compare Samples data may not be normal so the results of the test are suspect We now conduct a Wilcoxon signed rank test to see if the two values for the speed of light differ significantly from each other 1 Open the One sample Wilcoxon Test dialog 2 Type exmichel in the Data Set field 3 Select speed as the Variable 4 Enter 990 as the Mean Under Null Hypothesis 5 Click OK The Report window shows Wilcoxon signed rank test data speed in exmichel signed rank normal statistic with correction Z 3 0715 p value 0 0021 alternative hypothesis true mu is not equal to 990 You may also receive a warning message that there are duplicate values in the variable speed You can ignore this message The p value of 0 0021 is close to the test p value of 0 0027 for testing the same null hypothesis with a two sided alternative Thus the Wilcoxon signed rank test confirms that Michelson s average value for the speed of light of 299 909 km sec is significantly different from Cornu s value of 299 990 km sec The Kolmogorov Smirnov goodness of fit test is used to test whether the empirical distribution of a set of observations is consistent with a random sample drawn from a specific theoretical distribution It is generally more powerful than the chi square goodness of fit test for continuous variables For discrete variables the chi square test is generally preferable
412. otfire S ships with a number of sample data sets stored in internal databases These data sets are provided for your convenience while you are familiarizing yourself with Spotfire S To see these sample data objects do the following 1 Open the Object Explorer by clicking the Object Explorer button E on the Standard toolbar 2 In the left pane of the Object Explorer click the sign to the left of the SearchPath object to display the names of the databases in the search path 3 Click the icon to the left of a database name for example data to display all the objects contained in that database in the right pane For a complete discussion of the Object Explorer see Chapter 7 Working With Objects and Databases 14 Introduction You can open any number of Data windows simultaneously to display different data sets or to create concurrent views of a single data set When you open a Data window the Data window toolbar is automatically displayed The toolbar shown in Figure 2 2 contains buttons for quickly performing many frequently used editing commands Remove Remove Increase current link Column Width Spotfire S Align Align Decrease Insert Insert Clear Sort to Excel Left Right Precision Column Row Row Descending link wizard 38 58 1h tall Ipi MM 22 8 3 BG Cx amp SplusBook1 Shi 7 Convert Center Increase Change Clear Remove Sort Decrease Active Link to D
413. othesis has the form Hj where is the parameter of interest and Oo is the hypothesized value of our parameter One sample test a test for the population mean u We test if the population mean is a certain value For small data sets we require that the population have a normal distribution e One sample Wilcoxon signed rank test a nonparametric test for the population mean u As with the test we test if the population mean is a certain value but we make no distributional assumptions e One sample Kolmogorov Smirnov goodness of fit test a test to determine if the data come from a hypothesized distribution This is the preferred goodness of fit test for a continuous variable e One sample chi square goodness of fit test a test to see if the data come from a hypothesized distribution This is the preferred goodness of fit test for a discrete variable A one sample t testis used to test whether the mean for a variable has a particular value The main assumption in a f test is that the data come from a Gaussian normal distribution If this is not the case then a nonparametric test such as the Wilcoxon signed rank test may be a more appropriate test of location Performing a one sample t test From the main menu choose Statistics gt Compare Samples gt One Sample P t Test The One sample t Test dialog opens as shown in Figure 6 8 213 Chapter 6 Statistics 214 One sample t Test m Data Data
414. ots the results of a spectrum estimation We use these techniques to examine the structure in an environmental data set The autocovariance function is an important tool for describing the serial or temporal dependence structure of a univariate time series It reflects how much correlation is present between lagged observations Plotting autocorrelations From the main menu choose Statistics gt Time Series gt Autocorrelations The Autocorrelations and Autocovariances dialog opens as shown in Figure 6 79 359 Chapter 6 Statistics Autocorrelations and Autocovariances op x Data r Options Object flyn af x Estimate Type autocorrelation x Variable in x T Change Maximum Lag Default Meyimur eg r Results Save As I Plot Results Cancel Apply 4 current Figure 6 79 The Autocorrelations and Autocovariances dialog Example The example data set lynx df located in the example5 library contains the annual number of lynx trappings in the Mackenzie River District of northwest Canada for the period 1821 to 1934 Figure 6 80 displays the data 4000 6000 lynx trappings 2000 Figure 6 80 Lynx trappings in the Mackenzie River District of northwest Canada 360 ARIMA Time Series A definite cycle is present in the data We can use autocorrelations to explore the length of the cycle 1 2 3 4 5 Choose File Load Library to load the example5 l
415. ou want to plot or analyze in Spotfire S This region can contain data only or can include column and or row labels 2 Click the Excel to Spotfire S Link Wizard button E on the Excel Sheet toolbar or choose Links Link Wizard from the main menu The Excel to Spotfire S Link Wizard opens as shown in Figure 10 3 Excel to S PLUS Link Wizard x Welcome to the Excel to S PLUS Link Wizard This wizard walks you through the steps necessary to copy data from an Excel spreadsheet to an S PLUS data frame You will have the opportunity to specify the data range in Excel and row and column labels and data types for the data frame When you have completed the wizard the data will be copied from the Excel range to the S PLUS data frame Once the wizard is completed a link is created with which you control the transfer of data from Excel to S PLUS If the data in Excel are changed you can easily update the data frame in S PLUS by selecting Update Current Link from the Link menu To continue click Next Figure 10 3 The Excel to Spotfire S Link Wizard 3 Click Next Using Spotfire S With Microsoft Excel 4 As you can see in Figure 10 4 the range you selected in the Excel worksheet before starting the wizard automatically appears in the Data Range field Excel to S PLUS Link Wizard x Please choose the ranges for the data column and row names Only the data range is required D
416. out of the shaded row names column in a Data window by using the Copy Columns dialog simply select the special key word CROWNAMES gt S from the Columns dropdown list in either the From or To group Inserting Data Inserting Cells and Blocks When you insert a cell block column or row in a Data window Spotfire S shifts existing cells down and or to the right as appropriate to make room for the new cells To insert a cell or block of cells do the following From the main menu choose Insert gt Block The Insert Block dialog opens as shown in Figure 2 21 Insert Block ioli To Data Set ethanol X Columns NOx X Rows faa Cancel Apply current Figure 2 21 The Insert Block dialog In the Columns and Rows fields specify by column and row positions the cell or block of cells you want to insert and click OK 41 Chapter 2 Working With Data Inserting To insert a column do one of the following Columns e Click in the column you want to have shifted to the right to make room for the new column To insert a new column of the default type or of the same type as the last new column inserted click the Insert Column button Wl on the Data window toolbar To insert a new column of a specific type click the column type selector arrow located to the right of the Insert Column button see Figure 2 22 and select the type of column you want to insert DataSet E B SB 8 8 ile SS g a a a
417. ows with Missing Values Save As Models Scope Upper Formula ozone radiation temperature wind Lower Formula ozone rA Create Formula Cancel Appt current Help Figure 6 39 The Stepwise Linear Regression dialog Example We apply stepwise regression to the air data 1 Open the Stepwise Linear Regression dialog 2 Type air in the Data Set field 3 We must supply a formula representing the most complex model to consider Specify ozone radiation temperature wind as the Upper Formula 283 Chapter 6 Statistics 284 4 We must also supply a formula representing the simplest model to consider Specify ozone 1 as the Lower Formula The 1 indicates inclusion of just an intercept term 5 Click OK Stepwise regression uses the Cp statistic as a measure of goodness of fit This is a statistic which rewards accuracy while penalizing model complexity In this example dropping any term yields a model with a Cp statistic that is smaller than that for the full model Hence the full model is selected as the best model The summary of the steps appears in the Report window xxw Stepwise Regression Stepwise Model Comparisons Start Alc 29 9302 ozone radiation temperature wind Single term deletions Model ozone radiation temperature wind scale 0 2602624 Df Sum of Sq RSS Cp lt none gt 27 84808 29 93018 radiation 1 4 05928 31 90736 33 46893 tempera
418. p of the USA with state boundaries graphsheet gt usa Create a different map of the USA compare gt b gt 7 The USA map created by map is superior to that created by usa EXPLORING DATA Introduction Visualizing One Dimensional Data Exploratory Plots Visualizing Two Dimensional Data Scatter Plots Scatter Plots With Line and Curve Fits Scatter Plots With Nonparametric Curve Fits Line Plots and Time Series Plots Visualizing Multidimensional Data Scatter Plots and Scatterplot Matrices Trellis Graphs Three Dimensional Plots Dynamic Graphics 54 55 56 60 62 64 69 76 87 87 96 105 113 53 Chapter 3 Exploring Data INTRODUCTION 54 In this chapter we discuss the concept of exploratory data analysis and introduce you to a variety of plot types for examining the structure of your data Our discussion here is devoted exclusively to the use of plotting techniques as a means of examining your data However Spotfire S also offers a wide assortment of options for fully customizing your plots and transforming them into presentation quality graphics These procedures are documented in the Guide to Graphics available from the Help gt Online Manuals menu in Windows or as graphics pdf in your SHOME help directory Visualizing One Dimensional Data VISUALIZING ONE DIMENSIONAL DATA The Michelson Data A one dimensional data object is sometimes referred to as a single data sample a set
419. pear in a modeless dialog box in Excel If errors occur it might mean that invalid data were specified for the plot created It might also indicate another problem related to the range or data type of the data specified The graph may not be created if errors occur Using Spotfire S With SPSS USING SPOTFIRE S WITH SPSS Installing the SPSS Add In The SPSS add in application works with SPSS to make it easy to create and modify Spotfire S graphs from within SPSS This add in includes the ability to create Spotfire S graphs from selected variables in the SPSS data editor to modify the layout of a Spotfire S graph embedded in an SPSS output document and to modify the properties of a plot in an embedded Spotfire S graph in SPSS A helpful wizard guides you through the process of selecting variables choosing a Spotfire S graph and plot type and creating the graph in SPSS If you try to create a plot based on data that contains more than 100 000 rows using the SPSS add in to Spotfire S you get an error There is a limit of 100 000 rows using the SPSS add in During a Typical installation of Spotfire S Setup examines your system for the necessary version of SPSS Version 8 0 or higher and if detected automatically installs the SPSS add in Note To disable the automatic installation of the SPSS add in during Setup choose the Custom install option and clear SPSS Add in from the list of components to install
420. pecify the maximum number of undos to be saved in the undo queue for graph objects The higher the number of undos the more memory is used History Entries Specify the maximum number of entries to be saved in the history log The higher the number of entries the more memory is used History Type Select Condensed for a brief history or Full for a more detailed history Text Output Routing Document Background Colors Graph Options Changing Defaults and Settings To specify text output settings choose Options gt Text Output Routing from the main menu The Text Output Routing dialog is shown in Figure 11 10 Text Output Routing gt Window for Normal Text Output m Window for Errors and Warnings Default Default Commands Commands Report Report Script Program C Script Program Script Output C Script Output Invisible C Invisible Dialog Dialog Cancel Figure 11 10 The Text Output Routing dialog Use the options on this page to specify your output window preferences for normal text and for errors and warnings To specify document background colors choose Options gt Document Background Colors from the main menu The Document Background Colors dialog is shown in Figure 11 11 Document Background Colors P x Command Linge Data Grid eae Color C Bright wr x Background Color __ Bright Wr x Object Explorer Script Background Color C B
421. ph Sheet is created containing a plot of ozone versus radiation with a loess smooth Choose Statistics Smoothing gt Spline Smoother Type air as the Data Set Select radiation as the x Columns ozone as the y Columns and then click OK A Graph Sheet is created containing a plot of ozone versus radiation with a smoothing spline smooth Choose Statistics Smoothing gt Supersmoother Type air as the Data Set Select radiation as the x Columns ozone as the y Columns and then click OK A Graph Sheet is created containing a plot of ozone versus radiation with a supersmoother smooth TIME SERIES Autocorrela tions Time Series Time series techniques are applied to sequential observations such as daily measurements In most statistical techniques such as linear regression the organization of observations rows in the data is irrelevant In contrast time series techniques look for correlations between neighboring observations This section discusses the time series available from the Statistics gt Time Series menu e Autocorrelations calculates autocorrelations autocovari ances or partial autocorrelations for sequential observations e ARIMA fits autoregressive integrated moving average models to sequential observations These are very general models that allow inclusion of autoregressive moving average and seasonal components e Lag plot plots a time series versus lags of the time series Spectrum plot pl
422. plemented by exploratory graphics to provide confirmation that the model assumptions are sufficiently correct to validate the formal ANOVA conclusion Spotfire S provides tools for you to do both the data exploration and the formal ANOVA Setting up the data We have one factor variable diet and one response variable time The data are appropriately described in Spotfire S as a data set with two columns The steps below create a data frame named blood containing the data from Table 6 2 1 Open an empty data set by clicking the New Data Set button B on the Standard toolbar 2 Enter the four columns of observations listed in Table 6 2 Change the column names by double clicking on V1 and typing in A double clicking on V2 and typing in B and so on Press ENTER or click elsewhere in the Data window to accept the changes Compare Samples 3 Select Data Restructure Stack from the main menu In the From group of fields verify that the name of the data set appears in the Data Set field Select lt ALL gt in the Stack Columns list 4 Inthe To group of fields type blood as the Data Set time for the Stack Column and diet for the Group Column Click OK This step creates a data set named blood containing the data points from Table 6 2 in a two column format 5 Select Data gt Restructure Pack from the main menu Verify that blood appears in the Data Set field and select lt ALL gt in the Columns list This step removes al
423. potfire S link To remove the link from an Excel region to its corresponding S PLUS data frame do the following 1 Select the name of the S PLUS data frame from the Active Link dropdown list on the Excel Sheet toolbar 2 Click the Remove Excel to Spotfire S Link button E on the Excel Sheet toolbar Note that removing a link also removes the corresponding comment in Excel Removing a Spotfire S to Excel link To remove the link from an S PLUS data frame to its corresponding Excel worksheet do the following 1 Select the name of the Excel link from the Active Link dropdown list on the Data window toolbar 2 Click the Remove Current Link button amp on the Data window toolbar Note that removing a link also removes the corresponding attribute in the S PLUS data frame The Microsoft Excel add in application makes it easy to create and modify Spotfire S graphs from within Excel This add in includes the ability to create Spotfire S graphs from selected data to modify the layout of a Spotfire S graph embedded in Excel and to modify 495 Chapter 10 Using Spotfire S With Other Applications Installing the Excel Add In the properties of a plot in an embedded Spotfire S graph in Excel A helpful wizard guides you through the process of selecting data choosing a Spotfire S graph and plot type and creating the graph in Excel much like Excel s Chart Wizard During a Typical installation of Spotfire S Setup examin
424. ppear with a hyphen between them For example the key combination of SHIFT and F1 appears as SHIFT F1 Menu selections are shown in an abbreviated form using the arrow symbol P to indicate a selection within a menu as in File gt New WORKING WITH DATA Introduction Entering Editing and Saving Data Creating a Data Set Entering and Editing Data Saving Data Viewing and Formatting Data Displaying a Data Set Selecting Data Formatting Columns Formatting Rows Manipulating Data Moving and Copying Data Inserting Data Deleting Data Sorting Data Other Data Manipulation Options Libraries Included With Spotfire S 14 16 16 18 20 24 24 28 30 36 37 37 41 44 47 49 52 13 Chapter 2 Working With Data INTRODUCTION In Spotfire the primary tool for viewing editing formatting and manipulating data is the Data window It is similar to a spreadsheet except that it is column oriented rather than cell oriented Figure 2 1 below shows the sample data set air displayed in a Data window P air iol x ozone radiation temperature wind 190 00 67 00 7 40 3 30 118 00 72 00 8 00 2 29 149 00 74 00 12 60 2 62 313 00 62 00 11 50 2 84 299 00 65 00 8 60 2 67 99 00 59 00 13 80 2 00 19 00 61 00 20 10 2 52 o0 69 00 9 70 2 22 66 00 9 20 DAI sonn annon 5 0 s o a awn Figure 2 1 Sample data displayed in a Data window Note Sp
425. pt Warnings are not considered as serious as errors Typically warnings will not stop script execution whereas errors will You can select all the text in the Script window by choosing Select All from the Edit menu or by pressing CTRL A You can move text within a Script window using the Cut Copy and Paste commands in the Edit menu or the Cut Copy and Paste buttons on the Standard toolbar or CTRL X CTRL C and CTRL V You can use these commands to move and copy text within the same Script window to another open Script window or between Spotfire S and other applications Text that you cut or copy is placed on the clipboard An item placed on the clipboard will remain there until either the Cut or Copy command is chosen You can paste text from the clipboard into a Script window as many times as you want The same techniques used to move and copy text are used to move and copy any item or character You can use the Clear command in the Edit menu or the DELETE key to delete text from the Script window without keeping a copy of the text on the clipboard To move or copy text in a script do the following 1 Select the text 2 Click the Cut button or the Copy button on the Standard toolbar or choose Cut CTRL X or Copy CTRL C from the Edit menu This places the text on the clipboard 3 Position the insertion point in a new location in the Script window Click the Paste button fal on the Standard toolbar or ch
426. pter 11 Customizing Your Spotfire S Session Interactive 540 Create Editable Graphics When selected a plot created using one of the statistics dialogs is an editable graphic Resize with Graph group Resize Fonts with Graph When selected titles comments and other text in a graph are resized when you resize the graph Resize Symbols with Graph When selected symbols in a graph are resized when you resize the graph Note that shapes such as the open rectangle filled rectangle and oval are always resized regardless of this setting The Interactive page of the Graphs dialog is shown in Figure 11 13 Graphs BBE Options Interactive Brush and Spin IV Display Selected Points p Panning Selected Ports Vertical Overlap 15 Style Noe f Horiz Overlap 5 o Color Lre z l Height Multiplier fe Line Weight Incr Reo Cancel Figure 11 13 The Interactive page of the Graphs dialog Display Selected Points When selected data points that you select are highlighted in the Graph Sheet You can select data points either by selecting the desired rows in the Data window or by using the Select Data Points tool on the Graph Tools palette Use the options in the next group to specify the appearance of selected points Selected Points group Style Select a style from the dropdown list If you select None the selected points are highlighted but not replaced by another style Color Select a color from th
427. qual to Less than or equal to amp Vectorized And Vectorized Or 8 amp 8 Control And Control Or Not 433 Chapter 8 Using the Commands Window Expressions 434 An expression is any combination of functions operators and data objects For example KX Obes 3 221 is an expression that involves an operator the assignment operator and a function the combine function Here are a few more examples to give you an indication of the variety of expressions you will be using in S PLUS gt 3 runif 10 1 1 6006757 2 2312820 0 8554818 2 4478138 2 3561580 6 1 1359854 2 4615688 1 0220507 2 8043721 2 5683608 gt 3 2 11 1 1 5 32 gt e 2 runif 5 10 20 1 0 6010921 0 3322045 1 0886723 0 3510106 5 0 9838003 10 0000000 20 0000000 gt B c 2 x 5 1 1 23 17 21 5 14 The last two examples above illustrate a general feature of Spotfire S functions arguments to functions can themselves be S PLUS expressions Here are three examples of expressions that are important because they show how arithmetic works in Spotfire S when you use expressions involving both vectors and numbers If x consists of the numbers 4 3 2 1 then the following operations work on each element of x gt x1 ois 2 Lo gt 2 x1 DIRETTA PRA 1 16941 Any time you use an operator with a vector as one argument and a number as the other argument the operation is performed on each component of the vect
428. r in addition to the data type the precision of numeric values can also be specified For example the format string 03 27 2 PAs 25 2 exports the first and third columns as whole numbers of three and four digits respectively The second and fourth columns each have two decimal digits of precision If a precision value is not specified it is assumed to be zero if you supply a precision value for a character column it is ignored Note that when exporting row names the first entry in the format string is reserved for the row names Specifying a format string can potentially speed up the export of data sets that have many character columns If you do not include a format string Spotfire S must check the width of every entry in a character or factor column and determine a width large enough for all values in Informix Files Lotus Files Microsoft Access Files Microsoft Excel Files Oracle Files Notes on Importing and Exporting Files of Certain Types the column Since many of the supported file types use fixed widths considerable space can be saved by specifying a narrow width for character columns that have many short values and only a few long values With this approach the few long values are truncated All imports from and exports to Informix files are done using ODBC so the various ODBC components must be properly installed If your Lotus type worksheet contains numeric data only in a rectangular block starting
429. r into two subsets one for which a given condition is true and one for which the condition is false When used as a subscript the expression returns the subset for which the condition is true For instance the following expression selects all elements with values greater than 8 gt x x gt 8 ij 14 4 In this case the second and fourth elements of x with values 14 and 9 meet the requirements of the logical expression x gt 8 and so are displayed As usual in Spotfire S you can assign the result of the operation to another object For example you could assign the above selected subset to an object named y and then display y or use y in subsequent calculations gt y lt x x gt 8 gt y 1 14 9 In the next section you will see that the same principles also apply to matrix data objects although the syntax is a little more complicated because there are two dimensions from which selections may be made A single element of a matrix can be selected by typing its coordinates inside the square brackets as an ordered pair separated by commas We use the built in dataset state x77 to illustrate The first index inside the operator is the row index and the second index is the column index The following command displays the value in the third row eighth column of state x77 gt state x77 3 8 1 113417 443 Chapter 8 Using the Commands Window You can also display an element using row and column dimnames if
430. r of folders which themselves are used to contain references or shortcuts to objects of various types Note Unlike in Windows Explorer Object Explorer folders do not reflect the locations where your objects are actually stored Rather folders display shortcuts to objects giving you a way to visually organize them To open the Object Explorer click the Object Explorer button 5 on the Standard toolbar To close the Object Explorer simply click the button again 385 Chapter 7 Working With Objects and Databases The Object Explorer Toolbar The Left Pane and Right Pane Displays 386 When you open the Object Explorer window the Object Explorer toolbar is automatically displayed as shown in Figure 7 5 The toolbar provides buttons for quickly performing many common tasks as well as buttons for changing the right pane display Expand Find Large Item Objects Properties Icons List ObjectE xplorer Insert Collapse New Delete Small Details Page Item Folder Icons Figure 7 5 The Object Explorer toolbar As you can see in Figure 7 4 the left pane of the Object Explorer maps out folders and objects in a hierarchical display By expanding a folder or an object you can drill down to any level of detail to view its underlying structure To expand or collapse a folder or an object do one of the following e Click the plus or minus symbol to the left of the folder or object icon e Select the
431. raph is selected 2 Select the variable diff hstart in the Data window Visualizing Two Dimensional Data While pressing the SHIFT key click the Isolated Points button E on the Plots 2D palette The resulting graph is shown in Figure 3 23 o a o ITERS o o mag o ea Figure 3 23 Line with isolated points plots of tel gain and diff hstart Using the Y Series Lines button The previous example showed how to add a second line plot to an existing graph To create multiple line plots at once we can use the Y Series Lines button kA on the Plots 2D palette Select tel gain and diff hstart in any order in the Data window 81 Chapter 3 Exploring Data 82 2 Click the Y Series Lines button amp on the Plots 2D palette The resulting graph shown in Figure 3 24 below is very similar to Figure 3 23 Figure 3 24 Y series lines plot of te gain and diff hstart Adding titles and legends To add a main title Main Gain an x axis title Year and a legend to the last graph 1 From the main menu choose Insert gt Titles gt Main Type Main Gain where Auto appears in selected form and then click outside the edit box 2 Select the x axis by clicking on a tick mark not a tick label From the main menu choose Insert gt Titles gt Axis Type Year where Auto appears in selected form and then click outside the edit box Visualizing Two Dimensional Data 4 Now
432. rder specified on the Lines page of this dialog Changing Defaults and Settings Symbol Auto Change group Select Symbol Style and or Symbol Color to have these properties change each time you add a plot with symbols to the same graph Symbol styles and colors rotate in the order specified on the Symbols page of this dialog If no rotation is specified the first symbol style and color are used by default Pie and Area Auto Change group Select Fill Pattern Pattern Color and or Fill Color to have these properties change for each pie slice or area in a newly created pie or area chart Fill patterns pattern colors and fill colors rotate in the order specified on the Pattern Color and Fill Color pages of this dialog If no rotation is specified the first fill pattern pattern color and fill color are used by default Standard Bars Auto Change group Select Fill Pattern Pattern Color and or Fill Color to have these properties change for each bar in a newly created standard bar chart Fill patterns pattern colors and fill colors rotate in the order specified on the Pattern Color and Fill Color pages of this dialog If no rotation is specified the first fill pattern pattern color and fill color are used by default Grouped Bars Auto Change group Select Fill Pattern Pattern Color and or Fill Color to have these properties change for each bar in each group in a newly created grouped bar chart Fill patterns pattern colors and fi
433. rer toolbar or by choosing View from the main menu and selecting the desired view View Insert Format Data v Status Bar Toolbars Messages Expand Selected Large Icons Small Icons List Refresh Right Pane page Object Details group If you select List Details for your right pane display this group of options is enabled Here you can choose the type of detail you want to view For each check box you select a column of information is displayed in the right pane Listed below is a brief description of each option For more information see the Programmer s Guide Position Depends on what types of objects are listed in the column For an engine object Pos shows the position in the search path of the database where the object is stored For an element of a list or data frame Pos displays its position within the parent object For a toolbar button or menu item Pos shows its relative position on the toolbar or menu respectively Data Class The class of a data object such as data frame design or Im Inheritance Refers to the data class of an object and any classes from which it inherits Storage Mode The mode of a data object Dimensions The dimensions of an object in the case of a vector its length for a data frame or matrix the numbers of rows and columns Date The date an object was last modified Formatting Explorer Pages Introducing the Object Explorer Advanced page The Advanced
434. riate techniques time series analysis survival analysis resampling techniques and mathematical computing in Spotfire S Guide to Statistics Vol 2 CONTENTS Chapter 1 Introduction Welcome to Spotfire S Help Support and Learning Resources Typographic Conventions Chapter 2 Working With Data Introduction Entering Editing and Saving Data Viewing and Formatting Data Manipulating Data Libraries Included With Spotfire S Chapter 3 Exploring Data Introduction Visualizing One Dimensional Data Visualizing Two Dimensional Data Visualizing Multidimensional Data Chapter 4 Creating Plots Introduction Plotting One Dimensional Data Plotting Two Dimensional Data Plotting Multidimensional Data 12 13 14 16 24 37 52 53 54 55 60 87 117 119 122 132 144 vii Contents Trellis Graphs 159 Chapter 5 Importing and Exporting Data 161 Introduction 162 Supported File Types for Importing and Exporting 163 Importing From and Exporting to Data Files 168 Importing From and Exporting to ODBC Tables 179 Filter Expressions 187 Notes on Importing and Exporting Files of Certain Types 190 Chapter 6 Statistics 195 Introduction 198 Summary Statistics 204 Compare Samples 213 Power and Sample Size 259 Experimental Design 264 Regression 270 Analysis of Variance 300 Mixed Effects 306 Generalized Least Squares 310 Survival 314 Tree 322 Compare Models 326 Cluster Analysis 329 Multivariate 340 Quality Contr
435. rics S NuOpt S SeqTrial S SpatialStats S Wavelets S PLUS Graphlets Graphlet Spotfire S FlexBayes Spotfire S Resample TIBCO Spotfire Miner TIBCO Spotfire S Server TIBCO Spotfire Statistics Services and TIBCO Spotfire Clinical Graphics are either registered trademarks or trademarks of TIBCO Software Inc and or subsidiaries of TIBCO Software Inc in the United States and or other countries All other product and company names and marks mentioned in this document are the property of their respective owners and are mentioned for identification purposes only This Reference Technical Support Important Information software may be available on multiple operating systems However not all operating system platforms for a specific software version are released at the same time Please see the readme txt file for the availability of this software version on a specific operating system platform THIS DOCUMENT IS PROVIDED AS IS WITHOUT WARRANTY OF ANY KIND EITHER EXPRESS OR IMPLIED INCLUDING BUT NOT LIMITED TO THE IMPLIED WARRANTIES OF MERCHANTABILITY FITNESS FOR A PARTICULAR PURPOSE OR NON INFRINGEMENT THIS DOCUMENT COULD INCLUDE TECHNICAL INACCURACIES OR TYPOGRAPHICAL ERRORS CHANGES ARE PERIODICALLY ADDED TO THE INFORMATION HEREIN THESE CHANGES WILL BE INCORPORATED IN NEW EDITIONS OF THIS DOCUMENT TIBCO SOFTWARE INC MAY MAKE IMPROVEMENTS AND OR CHANGES IN THE PRODUCT S AND OR THE PROGRAM S DESCRIBED IN THIS DOCU
436. right Wr x Background Color Bright Wr x r Report Background Color Bright Wr x Cancel Figure 11 11 The Document Background Colors dialog Help Make selections on this page to specify your background color preferences for the Commands Object Explorer Data Script and Report windows To specify graph options choose Options Graph Options from the main menu The Graphs dialog consists of three tabbed pages of options discussed below 537 Chapter 11 Customizing Your Spotfire S Session Options 538 The Options page of the Graphs dialog is shown in Figure 11 12 Graphs oy x Options Interactive Brush and Spin m Graph Sheet Initialization r Traditional Graphics Graph Style Color x Auto Pages Within Expressi x 1 I Default to Draft Mode T Create Editable Graphics Default 2D Axes Linear x I Suppress Warnings Conditioning Options Use Printer Page Defaults T Condition Mode On Statistics Dialogs Graphics Condition Vars fi x IV Create New Graph Sheet Snap to Grid T Create Editable Graphics Grids per Inch fi 2 r Resize with Graph T Resize Fonts with Graph I Resize Symbols with Graph Cancel Help Figure 11 12 The Options page of the Graphs dialog Graph Sheet Initialization group Graph Style Select Color or Black and White for your graphs Default to
437. rning messages and reloading and may help in tracking down mysterious bugs such as when Spotfire S terminates abnormally Note that evaluation is substantially slower with this option turned on and at times it may introduce strange behavior Error Action Select the function with no arguments to be called when an error or interrupt occurs Spotfire S provides dump calls and dump frames to dump the outstanding function calls or the entire associated frames For details on these functions consult the online help Setting the function to NULL eliminates all error actions Warning Action Select the level of warnings that you would like reported 533 Chapter 11 Customizing Your Spotfire S Session 534 Max Recursion Specify the maximum depth to which expressions can be nested This option exists primarily to catch runaway recursive calls of a function to itself directly or indirectly Output Page Size group Values for these two settings apply to all output windows and persist between Spotfire S sessions for the current project By default Width and Length are automatically set to the dimensions of the current output window which includes Script and Report windows as well as the Commands window but you can override this default behavior by entering your own values in these fields Miscellaneous group Print Digits Specify the number of significant digits to use in print and therefore in automatic printing Setting this
438. rows California Michigan and Utah Notice that the closing bracket appears immediately after the comma meaning that all columns are selected gt state x77 c California Michigan Utah Population Income Illiteracy Life Exp Murder California 21198 5114 Vel 7 71 10 3 Michigan 9111 4751 0 9 70 63 11 1 Utah 1203 4022 0 6 72 90 4 5 HS Grad Frost Area California 62 6 20 156361 Michigan 520 125 56817 Utah 67 3 137 82096 445 Chapter 8 Using the Commands Window GRAPHICS IN Spotfire S Making Plots 446 Graphics are central to the Spotfire S philosophy of looking at your data visually as a first and last step in any data analysis With its broad range of built in graphics functions and its programmability Spotfire S lets you look at your data from many angles This section describes how to use Spotfire S to create simple plots To put Spotfire S to work creating the many other types of plots see the Guide to Graphics available from the Help Online Manuals menu item Plotting engineering scientific financial or marketing data including the preparation of camera ready copy on a laser printer is one of the most powerful and frequently used features of Spotfire S Spotfire S has a wide variety of plotting and graphics functions for you to use The most frequently used S PLUS plotting function is plot When you call a plotting function a Spotfire S graphics window displays the requested plot gt plot car
439. rses Books Using Spotfire S Typographic Conventions Scocca Geom Loan m N Chapter 1 Introduction WELCOME TO SPOTFIRE S Spotfire S is based on the latest version of the powerful object oriented S language originally developed at Lucent Technologies S is a rich environment designed for interactive data discovery and is the only language created specifically for data visualization and exploration statistical modeling and programming with data Spotfire S continues to be the premier solution for your data analysis and technical graphing needs The Microsoft Office compatible user interface gives you point and click access to data manipulation graphing and statistics With Spotfire S you can program interactively using the S PLUS programming language In a typical Spotfire S session you can e Import data from virtually any source e View and edit your data in a convenient Data window e Create plots with the click of a button e Control every detail of your graphics and produce stunning professional looking output for export to your report document e Perform statistical analyses from convenient dialogs in the menu system e Run analysis functions one at a time at the command line or in batches using the Script window e Create your own functions e Completely customize your user interface System For a list of system requirements see the file INSTALL TXT in the Requirements installation directory
440. rt dialogs you can subset the data you import or export The filter expression must be written in terms of the original column names in the file and not in terms of the variable names specified by the Col names row field Note also that the filter is not evaluated by Spotfire S meaning that expressions containing built in Spotfire S functions such as mean are not allowed One special exception to this rule deals with missing values while you can use NA to denote missing values in logical expressions you cannot use NA specific functions such as is na and na exclude To construct a variable expression specify a single variable or an expression involving several variables In addition to the usual arithmetic operators that are available Table 5 2 lists the relational operators that are accepted by the Filter expression field Table 5 2 Relational operators Operator Description Equal to Not equal to lt Less than gt Greater than lt Less than or equal to gt Greater than or equal to amp And 187 Chapter 5 Importing and Exporting Data Table 5 2 Relational operators Continued Operator Description Or Not For example to select all rows that do not have missing values in the id column type id NA To import all rows corresponding to 10 year old children who weigh less than 150 pounds type Age 10 amp Weig
441. rtial residual plot A partial residual plot is a plot of r b x versus x where r is the ordinary residual for the ith observation x is the ih observation of the kth predictor and b is the regression coefficient estimate for the Ath predictor Partial residual plots are useful for detecting nonlinearities and identifying possible causes of unduly large residuals The line y is shown as a dashed line in the third plot of the top row in Figure 6 36 In the case of simple regression this line is visually equivalent to the regression line The regression line appears to model the trend of the data reasonably well The residuals plots left two plots in the top row of Figure 6 36 show no obvious pattern although five observations appear to be outliers By default the three most extreme values are identified in each of the residuals plots and in the Cook s distance plot Another useful diagnostic plot is the normal plot of residuals right plot in the top row of Figure 6 36 The normal plot gives no reason to doubt that the residuals are normally distributed The r f plot on the other hand left plot in the bottom row of Figure 6 36 shows a weakness in this model the spread of the residuals is actually greater than the spread in the original data However if we ignore the five outlying residuals the residuals are more tightly grouped than the original data The Cook s distance plot shows four or five heavily influen
442. s SAS7UX32 sas7bdat sd7 SAS version 7 or later data files Solaris SPARC HP UX IBM AIX SAS7UX64 sas7bdat sd7 SAS version 7 or later data files Digital Compaq UNIX SAS Transport File SAS_TPT xpt tpt Version 6 x Some special export options may need to be specified in your SAS program We suggest using the SAS Xport engine not PROC CPORT to read and write these files SAS_CPORT stc cpt CPORT files created by SAS ver sions 7 01 9 01 can be imported SigmaPlot File STGMAPLOT jnb Import only SPSS Data File SPSS Sav OS 2 Windows HP IBM Sun DEC UNIX Supported File Types for Importing and Exporting Table 5 1 Supported file types for importing and exporting data Continued Default Exten Format Type sion Notes SPSS Portable File SPSSP _por Stata Data File STATA dta Versions 2 0 and higher Sybase SYBASE Same as DIRECT SYBASE Sybase database connection No file argument should be specified SYSTAT File SYSTAT syd sys Double or single precision sys files Note All imports from and exports to Informix Oracle and SYBASE databases are done using ODEC so the various ODBC components must be properly installed See Importing From and Exporting to ODBC Tables on page 179 167 Chapter 5 Importing and Exporting Data IMPORTING FROM AND EXPORTING TO DATA FILES Spotfire S provides convenient dialogs for importing and exporti
443. s Help is available for all the financial related functions from the Commands window and you can also access help from the main menu by navigating to Help gt Available Help gt financedb In this chapter we discuss each of these import export options in turn Supported File Types for Importing and Exporting SUPPORTED FILE TYPES FOR IMPORTING AND EXPORTING Table 5 1 lists all the supported file formats for importing and exporting data Note that Spotfire S both imports from and exports to all the listed types with two exceptions SigmaPlot jnb files are import only and HTML htm tables are export only Note As of Spotfire S version 8 2 native database drivers are deprecated In lieu of these drivers you should use JOBC ODBC drivers for all supported database vendors Table 5 1 Supported file types for importing and exporting data Default Exten Format Type sion Notes ASCII File ASCII CSV Comma delimited asc csv txt Delimited prn Whitespace delimited space asc dat txt delimited tab delimited user prn defined delimiter dBASE File DBASE dbf II I III IV files DIRECT DB2 DIRECT DB2 DB2 database connection No file argument should be specified DIRECT ORACLE DIRECT ORA ora Oracle database connection No Ft file argument should be specified DIRECT SQL DIRECT SQL Microsoft SQL Server database connection No file argument sho
444. s are done using ODBC so the various ODBC components must be properly installed 193 Chapter 5 Importing and Exporting Data SYBASE Files 194 When exporting to Oracle table names and column names must be in UPPERCASE All imports from and exports to SYBASE files are done using ODBC so the various ODBC components must be properly installed STATISTICS Introduction 198 Overview 198 Basic Procedure 200 Dialogs 200 Dialog Fields 201 Plotting From the Statistics Dialogs 202 Saving Results From an Analysis 203 Summary Statistics 204 Summary Statistics 204 Crosstabulations 207 Correlations 209 Tabulate 211 Compare Samples 213 One Sample Tests 213 Two Sample Tests 224 K Sample Tests 234 Counts and Proportions 244 Power and Sample Size 259 Normal Mean 259 Binomial Proportion 261 Experimental Design 264 Factorial 264 Orthogonal Array 265 Design Plot 266 Factor Plot 267 Interaction Plot 268 Regression 270 Linear Regression 271 Robust MM Regression 280 Robust LTS Regression 281 195 Chapter 6 Statistics 196 Stepwise Linear Regression Generalized Additive Models Local Loess Regression Nonlinear Regression Generalized Linear Models Log Linear Poisson Regression Logistic Regression Probit Regression Analysis of Variance Fixed Effects ANOVA Random Effects ANOVA Multiple Comparisons Mixed Effects Linear Nonlinear Generalized Least Squares Linear Nonlinear Survival Nonparametric Survival Co
445. s missing values in data objects as variant type VT_ERROR This option is selected by default and is important because programs such as Visual Basic interpret missing values as error values DDE Server Support group Respond to DDE Requests When selected Spotfire S responds to Dynamic Data Exchange queries For more information see Chapter 7 Calling Spotfire S Using DDE in the Application Developer s Guide Old Format for DDE Request When selected Spotfire S uses the text formatting style from earlier versions of Spotfire S when responding to Dynamic Data Exchange queries Other Enable ToolTips When selected button labels appear in small pop up windows when you hover the mouse over toolbar and palette buttons 526 Data Changing Defaults and Settings Color Toolbar When selected toolbars are displayed in color Otherwise they appear in black and white Large Buttons When selected large toolbar buttons are displayed By default Spotfire S displays small toolbar buttons Enable Graph DataTips When selected data information appears in small pop up windows when you hover the mouse over data points on a Graph Sheet The Data page of the General Settings dialog is shown in Figure 11 3 General Settings oi x General Data Startup Computations m Data Options ASCII Import Export Options M Show Auto Created Data in View Import Delimiter IV Autoload Data Sets Export Delimiter M Enabl
446. s our conclusion that the mean weight gain is not significantly different at level 0 05 in the high and low protein diets The two sample Kolmogorov Smirnov goodness of fit test is used to test whether two sets of observations could reasonably have come from the same distribution This test assumes that the two samples are random and mutually independent and that the data are measured on at least an ordinal scale In addition the test gives exact results only if the underlying distributions are continuous Compare Samples Perform a two sample Kolmogorov Smirnov goodness of fit test From the main menu choose Statistics gt Compare Samples gt Two Samples gt Kolmogorov Smirnov GOF The Two sample Kolmogorov Smirnov Goodness of Fit Test dialog opens as shown in Figure 6 16 Two sample Kolmogoroy Smirnoy Goodness of Fit Test Data rm Results Data Set kyphosis X Save s Variable 1 Age yx Print Results Variable 2 kyphosis X I Variable 2 is a Grouping Variable Dee Cancel Apply current Figure 6 16 The Two sample Kolmogorov Smirnov Goodness of Fit Test dialog Example The kyphosis data set has 81 rows representing data on 81 children who have had corrective spinal surgery The outcome Kyphosis is a binary variable and the other three columns Age Number and Start are numeric Kyphosis is a post operative deformity which is present in some children receiving spinal surgery We are interested i
447. s variable click the Censor Codes button and then click Add Response This generates the formula Surv time status type right 1 By default the censoring argument type is set to right when only one time variable is given Thus this formula is equivalent to Surv time status 1 4 Click OK A summary of the fitted model appears in the Report window and a plot of the survival curve with confidence intervals appears in a Graph Sheet The Cox proportional hazards model is the most commonly used regression model for survival data It allows the estimation of nonparametric survival curves such as Kaplan Meier curves in the presence of covariates The effect of the covariates upon survival is usually of primary interest Fitting a Cox proportional hazards model From the main menu choose Statistics Survival gt Cox Proportional Hazards The Cox Proportional Hazards dialog opens as shown in Figure 6 57 315 Chapter 6 Statistics Cox Proportional Hazards x Model Options Results Plot Predict Data r Save Model Object Data Set fleukemia fe Save As Weights he Subset Rows with IV Omit Rows with Missing Values Formula Formula Surv time status group Create Formula Cancel Apply current Figure 6 57 The Cox Proportional Hazards dialog Example We fit a Cox proportional hazards model to the leukemia data set with group used as a covariate 1 Ope
448. save it in a file you can type a new name in this field Note When you click the Object Explorer button on the Standard toolbar Spotfire S looks in the Prefs folder of your Spotfire S project folder for a file named Object Explorer sbf In general therefore we advise not changing the name Description Enter a description for the Object Explorer if desired File The pathname of the file is displayed if the Object Explorer has been saved to a file Bitmap Tab Bar When selected bitmap images are displayed on the tabs for Explorer Pages in the lower left hand corner of the Object Explorer window Clear the check box to label the tabs with the names of the Explorer Pages instead See page 394 for instructions on how to specify bitmaps and names for Explorer Pages Right Pane page The Right Pane page of the Object Explorer dialog is shown in Figure 7 8 Object Explorer 1 lolx Explorer Right Pane Advanced View C Large Icons paper Dotat Smalllcons I Boston List Icons M Data Class List Details T Inheritance I Storage Mode M Dimensions T Date Cancel pp of eaten Figure 7 8 The Right Pane page of the Object Explorer dialog 391 Chapter 7 Working With Objects and Databases 392 View Click one of the radio buttons to select your preferred right pane display refer to Figure 7 6 You can also change the view in the right pane by clicking a button on the Object Explo
449. sed to specify which data summaries to calculate Data menu not shown The Data menu located to the left of the Statistics menu is used to access functions that generate and manipulate data Data Window The open window on the left in Figure 6 1 is a Data window which you can use to create or edit a data set See Chapter 2 Working With Data for a detailed introduction to the Data window Report Window The Report window displays the results of a statistical analysis In the example in Figure 6 1 a Report window shows the results of the summary statistics Graph Sheet not shown A Graph Sheet displays the graphics created from the statistics menus Message Window not shown A Message window appears only if an error warning or informational message is generated by a statistics procedure If you close a Message window it reappears the next time an informational message is generated You should regularly examine the contents of the Message window to ensure that nothing out of the ordinary occurs during your statistical analysis 199 Chapter 6 Statistics Basic The basic procedure for analyzing data is the same regardless of the P i yzng 8 Procedure type of analysis 1 Select the data you want to work with 2 Choose the statistical procedure summary statistics linear regression ANOVA etc you want to perform from the Statistics menu The dialog corresponding to that procedure opens 3 Select the data set var
450. see anything unusual 113 Chapter 3 Exploring Data Selecting Data Points Spinning Data 114 3 Create a scatterplot matrix of the data Again do you see anything unusual any clear patterns or other distinguishing features Most people will see nothing unusual the data seem to be a cloud of random data points Now create a 3D scatter plot of the data Click the Select Data Points button on the Graph Tools palette Click and drag the pointer over some of the points in the 3D scatter plot A rectangular lasso appears as you drag When you release the mouse button all the points inside the lasso are highlighted Now click on the title bar of the scatterplot matrix Graph Sheet to bring it into focus Notice that the points you just highlighted in the 3D scatter plot are also highlighted in each of the matrix panels Click and drag the pointer to lasso an empty section in a scatterplot matrix panel The highlighting for the previously selected points is turned off Experiment with the Select Data Points tool by selecting and deselecting points It will be hard to find any kind of meaningful pattern in these data using brushing For some other data sets brushing can be a very useful exploratory data analysis tool Now let s examine the 3D scatter plot Here we can rotate the data interactively This allows us to view the data from a variety of angles and thus may help us to understand the data better
451. significantly different from the product of the total row percentage and total column percentage Thus there is an interaction between the car age and type which influences the number of claims That is the effect of car age on the number of claims varies by car type The Crosstabulations dialog is most useful for examining row and column percentages and performing a test for independence To create a simpler table of data use the Tabulate dialog as discussed on page 211 The Correlations and Covariances dialog produces the basic bivariate summaries of correlations and covariances 209 Chapter 6 Statistics 210 Computing correlations and covariances From the main menu choose Statistics Data Summaries gt Correlations The Correlations and Covariances dialog opens as shown in Figure 6 6 Correlations and Covariances olx r Data r Statistic Data Set air ii Type Correlations Variables C Covariances Fraction to Trim 10 Results Method to Handle Missing Values Save As Fai H IV Print Results Cancel Apply current Help Figure 6 6 The Correlations and Covariances dialog Example In Summary Statistics on page 204 we looked at univariate summaries of the data set air We now generate the correlations between all four variables of the data set Here are the basic steps 1 Open the Correlations and Covariances dialog 2 Type air in the Data Set field 3 Choose
452. sing Values Save As Variables Dependent Mileage z Independent Formula Mileage w eight Disp Create Formula Cancel Appt f current Figure 6 37 The Robust MM Linear Regression dialog Help Robust LTS Regression Regression Example The data set fuel frame is taken from the April 1990 issue of Consumer Reports It contains 60 observations rows and 5 variables columns Observations of weight engine displacement mileage type and fuel were taken for each of sixty cars In the fuel frame data we predict Mileage by Weight and Disp using robust MM regression 1 Open the Robust MM Linear Regression dialog 2 Type fuel frame in the Data Set field 3 Type Mileage Weight Disp in the Formula field Alternatively select Mileage as the Dependent variable and CTRL click to select Weight and Disp as the Independent variables As a third way of generating a formula click the Create Formula button select Mileage as the Response variable and CTRL click to select Weight and Disp as the Main Effects You can use the Create Formula button to create complicated linear models and learn the notation for model specifications The online help discusses formula creation in detail 4 Click OK to fit the robust MM regression model A summary of the model appears in the Report window A warning regarding initial and final estimates may also appear in the Message window for details about
453. sis two sided Na Results Save As IV Print Results Cancel Bew id current Help Figure 6 21 The Exact Binomial Test dialog Example When you play roulette and bet on red you expect your probability of winning to be close to but slightly less than 0 5 You expect this because in the United States a roulette wheel has 18 red slots 18 black slots and two additional slots labeled 0 and 00 This gives a total of 38 slots into which the ball can fall Thus for a fair or perfectly balanced wheel you expect the probability of red to be Po 18738 0 474 You hope that the house is not cheating you by altering the roulette wheel so that the probability of red is less than 0 474 For example suppose you bet on red 100 times and red comes up 42 times You wish to ascertain whether these results are reasonable with a fair roulette wheel 1 Open the Exact Binomial Test dialog 2 Enter 42 as the No of Successes Enter 100 as the No of Trials 3 Enter 0 474 as the Hypothesized Proportion 245 Chapter 6 Statistics Proportions Parameters 246 4 Click OK A summary of the test appears in the Report window The p value of 0 3168 indicates that our sample is consistent with data drawn from a binomial distribution with a proportions parameter of 0 474 Hence the roulette wheel seems to be fair The proportions parameters test uses a Pearson s chi square statistic to assess whether
454. st squares line for the exmain data appears to be influenced by the two outliers it would be nice to see what the effect is of making the least squares fit with these two points removed This is very easy to do 1 Make a scatter plot with a least squares line of tel gain versus diff hstart and select the two outliers as you did before see Figure 3 9 Remember to change the cursor back 66 Visualizing Two Dimensional Data to its regular form by clicking on the Select Tool button k on the Graph Tools palette after you have selected the outlier data points 2 0 7 T F T T 0 85 0 60 0 35 0 10 0 15 0 40 0 65 diff hstart Figure 3 9 Scatter plot with least squares line outlier points selected 2 From the main menu choose Format gt Exclude Selected Points This results in a new least squares line which fits the data without outliers quite well as shown in Figure 3 10 Notice that the vertical axis scale has changed and the two removed outliers do not appear in the plot 2174 0 85 0 60 0 35 0 10 0 15 0 40 0 65 diff hstart Figure 3 10 Scatter plot with least squares line outlier points removed 67 Chapter 3 Exploring Data A hallmark of a good robust line fitting method is that it gives a straight line fit that is close to that obtained with least squares when the data do not contain outliers You can check this out for the least trimmed squares LTS robust line fit relative to
455. t Excel EXCEL xi Versions 2 1 through 2010 Note Worksheet EXCELX xlsx that EXCELX and the new file extension xlsx are for files imported from or exported to Excel 2007 or 2010 ODBC ODBC Not applicable For Informix ifx Oracle ora and Sybase syb databases This file type is available only in Spot fire S for Windows Oracle Oracle ora Same as DIRECT ORACLE Oracle database connection No file argument should be specified Paradox Data File PARADOX db QuattroPro Work QUATTRO wq wb sheet S PLUS File SPLUS dd Windows DEC UNIX Uses data restore to import file STATA STATA dta Portable across platforms UNIX Windows and Mac STATASE Can import STATA files and ee export STATA or STATASE only When exporting a STATA dataset you are limited to 2 047 characters For larger STATA datasets up to 32 767 variables specify type STATASE 165 Chapter 5 Importing and Exporting Data Table 5 1 Supported file types for importing and exporting data Continued 166 Default Exten Format Type sion Notes SAS File SAS sd2 SAS version 6 files Windows SASV6 SAST ssd01 SAS version 6 files HP IBM SAS6UX32 Sun UNIX SAS4 ssd04 SAS version 6 files Digital SAS6UX64 UNIX SAS7 sas7bdat sd7 SAS version 7 or 8 files current platform SAS7WIN sas7bdat sd7 SAS version 7 or later data files Window
456. t Smooth 91 Chapter 3 Exploring Data 2 On the Smooth page of the Scatter Plot Matrix dialog select Least Squares from the Smoothing Type pull down list and click Apply Scatter Plot Matrix 1 _ oy x Data to Plot Line Histogram Symbol Vary Symbols Smooth Smoothing Type None z R EE EPEL is Wutput Points Loess Friedman Box Span eating Degree Family J Smoothing Spline Spe Deg of Freedar E ad Cancel Appt K current Figure 3 32 The Smooth page of the Scatter Plot Matrix dialog 3 Try experimenting with different types of lines and smoothing parameters Click OK to close the dialog when you are finished and then close all windows The Sensors Data As you will recall the sensors data contain the responses of eight different semiconductors designed to be responsive in different ways to gases in the air for example for pollution monitoring The data contain 80 observations The question is whether additional sensors beyond one really improve pollutant detectability Bubble Color We begin by creating a bubble color plot in which we use x value y Plots value symbol size and symbol color to each display a variable This allows us to represent four variables in a single scatter plot Use the Select Data dialog to display the sensors data ina Data window 2 Select the first four columns of the data 92 Multiple Plots in a Graph Visualizing Multidim
457. t also with a Degree box value of 2 instead of the default value of 1 This number determines whether local linear or local quadratic fits are used Scatter plots are useful tools for visualizing the relationship between any two variables one against the other regardless of whether there is any particular ordering of the horizontal axis variable On the other hand often one of the two variables you want to visualize in a scatter plot is ordered from smallest to largest according to the row numbers of the data set in which the data are stored Line Plots Visualizing Two Dimensional Data Line plots are helpful visualizations of the relationship between two variables as we have seen when overlaying the values of a straight line fit or a nonparametric curve fit on a scatter plot In these cases an ordering of the x axis values with corresponding y axis variables is automatically carried out behind the scenes in overlaying the line or curve fit Another situation in which the ordering of your data is important is time series data Here successive values are measured at successive instants of time With ordered data it is useful to make a line plot in which the successive values of your data are connected by straight lines rather than just making a scatter plot of the data By using straight line segments to connect the points you can see more clearly the overall trend or shape of your ordered data values We return to the variables te
458. t does not include the Spotfire S GUI Works only on computers running a 64 bit Windows operating systems It does not work on computers running 32 bit Windows Help Support and Learning Resources HELP SUPPORT AND LEARNING RESOURCES Online Help HTML Help There are a variety of ways to accelerate your progress with Spotfire S This section describes the learning and support resources available to Spotfire S users Spotfire S offers an online HTML Help system to make learning and using Spotfire S easier Under the Help menu you will find help on how to use the Spotfire S graphical user interface In addition an extensive Language Reference provides detailed help on each function in the S PLUS language The Language Reference help can also be accessed through the Commands window by typing help at the S PLUS language prompt Context sensitive help is available by clicking the Help button in dialogs or the context sensitive Help button on toolbars as well as by pressing the F1 key while Spotfire S is active HTML Help in Spotfire is based on Microsoft Internet Explorer and uses an HTML window to display the help files To access HTML Help do one of the following From the main menu choose Help gt Available Help gt TIBCO Spotfire S Help for help on the GUI e From the main menu choose Help gt Available Help gt Language Reference for help on the S PLUS programming language Chapter 1 Introduct
459. t imported row Row name col If the file you are importing contains row names type the number corresponding to the column in the file that contains the row names Note Because the underscore character is a reserved character in S PLUS Spotfire S replaces the underscore _ with a period when importing column or row names Start col Type the number corresponding to the first data column to be imported from the file By default Spotfire S begins reading from the first column in the file End col Type the number corresponding to the last data column to be imported from the file The default is the last column in the file 171 Chapter 5 Importing and Exporting Data 172 Start row Type the number corresponding to the first data row to be imported from the file By default Spotfire S begins reading from the first row in the file End row Type the number corresponding to the last data row to be imported from the file The default is the last row in the file Options page Additional group Worksheet number When importing data from a spreadsheet such as Excel or Lotus specify the number of the worksheet you want to import Strings as factors When selected Spotfire S converts all character strings to factor variables when the data file is imported Otherwise character strings are imported with class character Sort factor levels When selected Spotfire S alphabetically sorts the levels f
460. t in the list function its own name For instance you can create the same list as above but name the components a and b and save the list data object with the name xyz gt x Z lt Listlea 1017119 b et char string 1 char string 2773 To take advantage of the component names from the 1ist command use the name of the list followed by a sign followed by the name of the component For example the following two commands display components a and b respectively of the list xyz gt xyz a 1 101 102 103 104 105 106 107 108 109 110 111 112 113 14 114 115 116 117 118 119 gt xyz b i ehar string 1 char string 2 In S PLUS any object you create at the command line is permanently stored on disk until you remove it This section describes how to name store list and remove your data objects To name and store data in S PLUS use one of the assignment operators lt or _ Do not use the _ character in names For example to create a vector consisting of the numbers 4 3 2 1 and store it with the name x use the c function and type gt xX lt 4 3 2 1 You type lt by typing two keys on your keyboard the less than key lt followed by the minus character with no intervening space To store the vector containing the integers 1 through 10 in y type gt y lt 13710 The following assignment expressions use the operator _ and are identical to the two previous assign
461. t in the left pane of the Object Explorer by clicking the to the left of its icon 382 Understanding Object Types and Databases 2 In the left pane select a database for example data by clicking its icon The contents of that database are displayed in the right pane as shown in Figure 7 2 Object Explorer iof x Contents of database data LJ Data I Graphs A T character Reports zZ min script id integer Scripts A Copyright character EN SearchPath 4 Original list E D Program Files Insight 4 PostScript Options list E E splus Program expression fH Ea stat air data frame w G TR akima x numeric fH Ea trellis IR akimay numeric Et nlme3 R numeric H E menu I animals data frame H E sgui E author count matrix E winspi LE auto stats matrix J main list Figure 7 2 Displaying the contents of a database in the right pane When you request an object for example by using the Select Data dialog Spotfire S first searches the working data for an object of that name because it is the first database in the search path If Spotfire S cannot find the object in your working data it next searches the database in position two and so on What this means is that if you create an object with the same name as a built in S PLUS object stored in a system database your object will mask the system object until you delete or rename it When you have the Object Explorer set t
462. t the errors in INDEX Symbols Data folders 406 First function 550 551 Last function 552 Prefs folders 406 Numerics 2D plots scatter 87 least squares straight line fits for 65 robust line fits for 65 selecting and highlighting points in 63 3D line plots 144 3D plots 105 rotating 108 scatter 105 3D scatter plots 144 A Access files hints for importing and exporting 190 193 add in applications Excel 495 Mathcad 509 SPSS 503 agglomerative hierarchical method 335 analysis of variance ANOVA 234 300 one way 234 238 random effects 301 Append Columns dialog 50 area charts 150 arguments abbreviating 437 defaults for 436 formal 437 optional 436 required 436 supplying by name 437 supplying by value 437 ARIMA 361 arithmetic operators 187 ASCII files 439 hints for importing and exporting 190 Attach Create Chapter dialog 408 attach function 430 autocovariance correlation 359 automatically filled in dialog fields 201 automatic delimiter matching 472 automatic indention 472 auto plot redraw 549 autoregressive integrated moving average ARIMA 361 B background colors document 537 553 Index 554 bandwidth 69 355 barley data 101 bar plots 128 grouped 140 stacked 142 bar plots 3D 153 batch command processing 459 binomial histogram 153 binomial power and sample size 259 261 Binomial Power and Sample Size dialog 259 261 blood data 235 bootstrap 351 boxcar kernel smoothers 70
463. t the plot as you like by double clicking in the graph area of the component bringing up the Spotfire S toolbars Figure 10 18 shows such a graph In this example we used the fuel frame sample data set where x is the Weight variable y1 is Mileage y2 is Fuel and cond are the codes of the factor variable Type The value 1 corresponds to the Compact type 2 to Large 3 to Medium 4 to Small 5 to Sporty and 6 to Van Note If you change the number of inputs to a graph component your existing plot will be deleted and a new plot will be created but all your other settings will be saved If you change the graph type however everything will be deleted before a graph of the requested type is created that is all your other modifications will be lost 512 Using Spotfire S With MathSoft Mathcad 2100 3200 2100 3200 vals 1 x yl y con Figure 10 18 A Tiellis graph in a Mathcad worksheet Creating an S PLUS script To create a Spotfire S language script in Mathcad do the following 1 Click in a blank space in your Mathcad worksheet A red crosshair indicates the insertion point 2 From the Mathcad main menu choose Insert P Component to open the Component Wizard dialog 3 Select Spotfire S Script and then click Next to start the Spotfire S Script Setup Wizard as shown in Figure 10 19 513 Chapter 10 Using Spotfire S With Other Applications S PLUS Script Setup Wizard x S PLUS
464. t window for editing To use a more full featured text editor use the fix function which uses the editor specified in your Spotfire S session options by default notepad With fix you create a copy of the original data object edit it and then reassign the result under its original name If you already have a favorite editor you can use it by specifying it with the options function For example if you prefer to use Microsoft Word as your editor you can set this up easily as follows gt options editor c Program Files Microsoft Office Built In Data Sets Importing and Editing Data Office winword Spotfire S comes with a large number of built in data sets These data sets provide examples for illustrating the use of Spotfire S without forcing you to take the time to enter your own data When Spotfire S is used as a teaching aid the built in data sets provide a useful basis for problem assignments in data analysis To get Spotfire S to display a built in data set just type its name at the gt prompt The built in data sets in Spotfire S include data objects of various types To find these built in data sets use the search function It will return a list of currently attached object databases gt search 1 C DOCUME 1 MYDOCU 1 Spotfi 1 Project1 2 splus 3 stat 4 data 5 trellis 6 nlme3 7 menu 8 sgui 9 winjava 10 SPXML Lil marn 44
465. ta Specs page of the Import From File dialog Data Specs page From group File Name Specify the file you want to import by doing one of the following e Click Browse and navigate to the file e If the file is in your current project folder simply type the file name including its extension e If the file is not in your current project folder type the full pathname of the file File Format Select the format of the file from the dropdown list in this field Hint In most cases if you use Browse to specify the file to import Spotfire S automatically selects the correct format type 169 Chapter 5 Importing and Exporting Data Data Specs page To group Data set By default Spotfire S creates a new data set with the same name as the file being imported To override the default name type a different name in this field To import the data into an existing data set type or select its name Note If the name you specify in the Data set field is the name of an existing object by default Spotfire S prompts you for an overwrite confirmation To turn off this feature choose Options gt General Settings from the main menu click the Data tab and clear the Prompt on import overwrite check box Create new data set When selected Spotfire S creates a new data set with the name specified in the Data set field Add to existing data set When selected Spotfire S imports the data into the existing dat
466. te button corresponding to the desired plot 3D scatter plot B 3D line plot 3D line with scatter plot 144 3D regression plot 4 Bubble and Color Plots Plotting Multidimensional Data 3D regression with scatter plot ae Bubble and color plots are scatter plots that let you represent an additional dimension by varying the size or color of the plotting symbol To create a scatter plot of y against x with the z data represented as bubbles of varying size bubble plot or bubbles of varying color color plot 3 bubble and color iol x 1 Select the x y and z columns 2 For a bubble plot click the E button on the Plots 2D palette For a color plot click the E button Bubble plot Ca we e e eoo e e EJ Color plot E 145 Chapter 4 Creating Plots Bubble Color A bubble color plot is just a combination of a bubble plot and a color Plots plot To produce a scatter plot of y against x with the z data represented as bubbles of varying size and the w data represented as bubbles of varying color ii bubble color ioj xj 1 Select the x y z and w columns 2 Click the E button on the Plots 2D palette Bubble color plot E High Low Plots A high low plot typically displays the daily monthly or yearly high and low values of a series together with average or closing values and perhaps opening
467. ted to linear regression are Stepwise Linear Regression Compare Models and Multiple Comparisons The Stepwise Linear Regression dialog uses a stepwise procedure to suggest which variables to include in a model The Compare Models dialog provides tests for determining which of several models is most appropriate The Multiple Comparisons calculates effects for categorical predictors in linear regression or ANOVA Fitting a linear regression model From the main menu choose Statistics Regression Linear The Linear Regression dialog opens as shown in Figure 6 34 Linear Regression ol x Model Resuts Pot Predict m Data Data Set air fad Weights x Subset Rows with r Save Model Object IV Omit Rows with Missing Values Save As Variables Dependent ozone X Independent Formula Jozone temperature Create Formula Cancel Apply current Help Figure 6 34 The Linear Regression dialog Example We examine the air pollution data in the example data set air This is a data set with 111 observations rows and 4 variables columns It is taken from an environmental study that measured the four variables ozone solar radiation temperature and wind speed for 111 consecutive days We first create a scatter plot of the temperature and ozone variables in air as shown in Figure 6 35 Regression Oo 000 0 0 o o T T T T T 50 60 70 80 90 100 temperature Figure 6 3
468. ter 7 Working With Objects and Databases Setting Your Preferred Defaults 394 Doing any of the foregoing opens the Explorer Page dialog as shown in Figure 7 10 Explorer Page 1 iol x Name Pace IV Display Search Path ToolTip Pae Image FileName P Browse Cancel Apply current Help Figure 7 10 The Explorer Page dialog Name By default a new Explorer Page is named Page x where xis a sequential number If you prefer you can enter a new name in this field Names are displayed on Explorer Page tabs if the Object Explorer is set to display names rather than bitmaps or icons For more information on this option see page 391 ToolTip If you pause your mouse cursor over an Explorer Page tab Spotfire S displays a ToolTip for that page By default the ToolTip text is the name of the page but if you prefer you can specify a different ToolTip Image FileName If the Object Explorer is set to display bitmaps or icons on Explorer Page tabs see page 391 specify the complete file name and path of the bitmap file or icon you want to use To navigate to the file click Browse If no file is specified Spotfire S uses a default image Display Search Path When selected a SearchPath object is automatically inserted in the Explorer Page When you click the Object Explorer button I on the Standard toolbar the default Object Explorer is opened After customizing the Object Explorer you can save your changes as t
469. that data The Random Sample of Rows dialog does this It can also be used to randomize permute the rows in a data set Taking a random sample of rows Choose Data gt Random Sample The Random Sample of Rows dialog opens as shown in Figure 6 85 BE x Random Sample of Rows Data r Options Data Set ffuel frame 7 I Sample with Replacement Sample Size feo Sampling Prob Si Set Seed with Results Save In Eq Cancel Apply HA aren i Figure 6 85 The Random Sample of Rows dialog exfuel20 Help Distribution Functions Random Numbers and Distributions Example The data set fuel frame is taken from the April 1990 issue of Consumer Reports It contains 60 observations rows and 5 variables columns Observations of weight engine displacement mileage type and fuel were taken for each of sixty cars In the fuel frame data we randomly sample 20 of these 60 cars and place them in a new data set named exfuel20 1 Open the Random Sample of Rows dialog 2 Enter fuel frame in the Data Set field Enter a Sample Size of 20 and type exfue120 in the Save In field 4 Click OK The data set exfuel20 containing the new sample is created and displayed in a Data window The Random Sample of Rows dialog can also be used to randomize permute rows To do this set the Sample Size to be the number of observations in the data set and check the Sample with Replacement option All rows are i
470. the Covariance Struct 7 Click OK A summary of the fitted model appears in the Report window In many scientific fields notably psychology and other social sciences you are often interested in quantities like intelligence or social status which are not directly measurable However it is often possible to measure other quantities that reflect the underlying variable of interest Factor analysis is an attempt to explain the correlations between observable variables in terms of underlying factors which are themselves not directly observable For example measurable quantities such as performance on a series of tests can be explained in terms of an underlying factor such as intelligence Performing factor analysis From the main menu choose Statistics Multivariate gt Factor Analysis The Factor Analysis dialog opens as shown in Figure 6 71 341 Chapter 6 Statistics Principal Components 342 Factor Analysis ox Model Options Results Plot Predict m Data r Model Data Set ftestscores df x Number of Factors 2 Subset Rows with Method mle Es M Omit Rows with Missing Values Rotation varimax iv T Use Covariance List as Input r Save Model Object avanance List Save As I Include Scores Formula Variables Figure 6 71 The Factor Analysis dialog Example The data set testscores df located in the examp1e5 library contains five test scores for eac
471. the K Means Clustering dialog Type state df in the Data Set field en Gen IS CTRL click to select the Variables Population through Area 5 Click OK A summary of the clustering appears in the Report window The partitioning around medoids algorithm is similar to k means but it uses medoids rather than centroids Partitioning around medoids has the following advantages it accepts a dissimilarity matrix it is more 331 Chapter 6 Statistics 332 robust because it minimizes a sum of dissimilarities instead of a sum of squared Euclidean distances and it provides novel graphical displays silhouette plots and clusplots Performing partitioning around medoids From the main menu choose Statistics Cluster Analysis gt Partitioning Around Medoids The Partitioning Around Medoids dialog opens as shown in Figure 6 65 Partitioning Around Medoids Model l Results Plot r Data Dissimilarity Measure Data Set state df F Metric euclidean X Variables iteracy I Standardize Variables Options os Num of Clusters 2 Subat Bows oOo T Use Large Data Algorithm 3 M Omit Rows with Missing Values r Dissimilarity Object T Use Dissimilarity Object I z Save Model Object Saved Object Save As M Save Data Save Dissimilatities Cancel Apply K current Figure 6 65 The Partitioning Around Medoids dialog Example
472. the row names column Changing Defaults and Settings ASCII Import Export Options group Import Delimiter Specify a delimiter for importing ASCII text The default delimiter is a comma Export Delimiter Specify a delimiter for exporting to ASCII text The default delimiter is a comma Other Show Imported Data in View When selected Spotfire S automatically opens a Data window to display imported data Prompt on import overwrite When selected Spotfire S prompts you for overwrite confirmation when you are attempting to import a data file that is named the same as a data set already stored in the database Date Time Input group Date format Select the format you prefer to use when creating and displaying date data The available choices mirror those in your Windows Regional Options the default value for this field is the current Windows default Time format Select the format you prefer to use when creating and displaying time data The available choices mirror those in your Windows Regional Options the default value for this field is the current Windows default Note The selections you make in the Date format and Time format fields persist only for the current session To preserve your settings from session to session clear the Use Regional Options for input check box in the Date Time Formats group on the Startup page of the dialog Date Time Output group Date format Select the format you prefer to use whe
473. the same plot without the distribution line click the e button on the Extra Plots palette Probability normal with line plot Ke Probability plot e Pad 0 8 e 0 6 4 P 0 44 Po Ss 024 uo fice ned deS R ae Tei bes we b85 i Te Normal Probabilities Normal Probabilities Histogram A histogram displays a set of rectangular bars The width of each bar Density Plots represents a range of values and the height of the bar represents the counts of observations that fall within a given range A nonparametric density estimate is an estimate of the probability density function or density for short of your data that does not assume any parametric form for the density such as a normal density with mean parameter p and variance parameter o2 125 Chapter 4 Creating Plots 126 You can think of a nonparametric density estimate as a smooth alternative to a histogram with the shape of the density estimate being similar to that of the histogram Histogram density plots are powerful visualization tools without the considerable data reduction produced by a box plot To create any of the histogram density plots for a single set of data select the x column Then e To create a histogram click the ie button on the Plots 2D palette H histogram density Of x e To create a density plot click the lA button on the Plots 2D palette e To create a histogram density plot c
474. this right click the appropriate script in the right pane and select Properties from the pop up menu To save the desired settings as defaults for future Script window sessions select Options gt Save Window Size Properties as Default Time Saving Tips for Using Scripts TIME SAVING TIPS FOR USING SCRIPTS History Log Spotfire S provides several methods for writing scripts The easiest is to open a new Script window and type in commands and execute them Other ways to generate scripts include using the History Log and the menus or dragging objects into a Script window to record the commands that create or modify these objects This section discusses how to view the History Log how to generate a given plot using S PLUS language commands and how to edit an S PLUS function s definition using a Script window You can drag and drop other object types from the Object Explorer into a Script window including toolbars menu items ClassInfo objects and FunctionInfo objects Spotfire S keeps a continuous record or history of menu toolbar and dialog operations Visual edits such as changing cells in Data windows or repositioning an object on a Graph Sheet are also recorded as are commands issued in the Commands window The S PLUS programming language equivalents of these operations are recorded in the History Log You can view the History Log in a Script window In order to record dialog operations in the History Log you must
475. this dialog see Random Numbers and Distributions on page 366 1 Open an empty data set by clicking the New Data Set button B on the Standard toolbar 220 Compare Samples 2 Select Data gt Random Numbers from the main menu Verify that the name of the blank data set is in the Data Set field Note that the Distribution is normal by default Type 10 for the Mean and leave the Std Deviation as 1 3 Type X for the Target Column and 200 for the Sample Size For reproducibility we want to set the random number generator seed type 21 in the Set Seed with field Click OK This step creates a column named X containing 200 elements that are randomly sampled from a Gaussian distribution 4 Select Data Fill from the main menu Verify that the name of the data set appears in the Data Set field Type Day for the Columns 20 as the Length and 10 as the number of Replications Select Grouped Sequence from the Content list and click OK This step creates a column named Day containing the integers 1 through 20 each repeated 10 times The Day column represents the day on which the simulated measurements were taken 5 Rename the data set by double clicking in the upper left corner of the Data window In the dialog that appears type qcc process in the Name field and click OK We can use the Kolmogorov Smirnov goodness of fit test to confirm that qcc process is Gaussian 1 Open the One sample Kolmogorov Smirnov Goodness of Fit Test d
476. tial observations Because the regression line fits the data reasonably well the regression is significant and the residuals appear normally distributed we feel justified in using the regression line as a way to estimate the ozone concentration for a given temperature One important issue remains however the regression line explains only 279 Chapter 6 Statistics Robust MM Regression 280 57 of the variation in the data We may be able to do somewhat better by considering the effect of other variables on the ozone concentration Robust regression models are useful for fitting linear relationships when the random variation in the data is not Gaussian normal or when the data contain significant outliers In such situations standard linear regression may return inaccurate estimates The robust MM regression method returns a model that is almost identical in structure to a standard linear regression model This allows the production of familiar plots and summaries with a robust model The MM method is the robust regression procedure currently recommended by TIBCO Performing robust MM regression From the main menu choose Statistics Regression gt Robust MM The Robust MM Linear Regression dialog opens as shown in Figure 6 37 Robust MM Linear Regression BEI Model Options Results Plot Predict m Data Data Set ffuel rame X Weights E Subset Rows with gt Save Model Object I Omit Rows with Mis
477. tify C Rug C Snip Tile Bug Tile Variable age z gt Save Model Object Save As Hist Varaules Figure 6 61 The Tree Tools dialog Example In Tree Models on page 322 we fit a classification tree to the kyphosis data We can use a tree tile plot to see histograms of Age within each group 1 If you have not done so already fit the classification tree and save the results in an object named my tree This process is outlined on page 322 2 Open the Tree Tools dialog 3 Select my tree as the Model Object 4 Select Tile as the Tool Type 5 Select Age as the Rug Tile Variable 6 Click OK A tree tile plot is displayed in a Graph Sheet The top portion of the graph contains a plot of the tree The bottom portion contains histograms of Age for each terminal node in the tree 325 Chapter 6 Statistics COMPARE MODELS 326 In regression and ANOVA the data analyst often has a variety of candidate models of interest From these models the data analyst usually chooses one which is thought to best describe the relationship between the predictors and the response Model selection typically involves making a trade off between complexity and goodness of fit A more complex model one involving more variables or interactions of variables is guaranteed to fit the observed data more closely than a simpler model For example a model with as many parameters as observations would fit the
478. tion parameter Fisher s Exact Test a test for independence between the rows and columns of a contingency table e McNemar s Test a test for independence in a contingency table when matched variables are present e Mantel Haenszel Test a chi square test of independence for a three dimensional contingency table e Chi square Test a chi square test for independence for a two dimensional contingency table Binomial data are data representing a certain number k of successes out of n trials where observations occur independently with probability p of a success Contingency tables contain counts of the number of occurrences of each combination of two or more categorical factor variables The exact binomial testis used with binomial data to assess whether the data are likely to have come from a distribution with a specified proportion parameter p Binomial data are data representing a Compare Samples certain number k of successes out of n trials where observations occur independently with probability p of a success Examples include coin toss data Performing an exact binomial test From the main menu choose Statistics gt Compare Samples gt Counts and Proportions gt Binomial Test The Exact Binomial Test dialog opens as shown in Figure 6 21 Exact Binomial Test 5 x Data m Test Hypotheses i No of Successes fa2 Hypothesized Proportion No of Trials 100 0 474 Alternative Hypothe
479. tion for row and column numbers is derived from mathematical matrix notation In the above example the vector 1 12 fills the first column first then the second column and so on This is called filling the matrix by columns If you want to fill the matrix by rows use the optional argument byrow T to matrix For a vector of given length used to fill the matrix the number of rows determines the number of columns and vice versa Thus you need not provide both the number of rows and the number of Data Frame Objects S PLUS Language Basics columns as arguments to matrix it is sufficient that you provide only one or the other The following command produces the same matrix as above gt tatrixdi 12 3 You can also create this matrix by specifying the number of columns only To do this type gt matrix 1 12 ncol 4 You have to provide the optional argument ncol 4 in name value form because by default the second argument is taken to be the number of rows When you use the by name form ncol 4 as the second argument you override the default See Optional Arguments to Functions on page 436 for further information on using optional arguments in function calls The array classes generally have three slots a Data slot to hold the actual values a Dim slot to hold the dimensions vector and an optional Dimnames slot to hold the row and column names The most important slot for a matrix data object is the dime
480. to display the Insert Columns dialog 3 Type Year in the Name s text box and 1971 1984 in the Fill Expression text box Select integer as the Column Type and click OK See Figure 3 27 Insert Columns o x Name s rer Fill Expression fanas Start Column jemo zj Count pooo Data Set ferman Column Type E Cancel Appt current R Help Figure 3 27 The Insert Columns dialog 85 Chapter 3 Exploring Data The third column now contains integers specifying years as shown below p exmain of x 1 diff hstart 0 0600 0 1300 0 1400 0 0700 0 0500 n 2100 Select the Year column Press the CTRL key and select tel gain and diff hstart in either order Click the Isolated Points button on the Plots 2D palette The resulting plot is shown in Figure 3 28 Main Gain diff nstart i tel gain R Pe pen et SN N al Sa ou sal a e o o ee os 7 o 1 T T T T T 1970 1972 1974 1976 1978 1980 1982 1984 Year Figure 3 28 Yearly time series plot of tel gain and diff hstart Visualizing Multidimensional Data VISUALIZING MULTIDIMENSIONAL DATA Scatter Plots and Scatterplot Matrices The Fuel Data In the previous sections we discussed visualizing simple one and two dimensional data sets With one and two dimensional data all of the basic information in the data may be easily viewed in a si
481. tomatic indentation is enabled the editor automatically indents the bodies of function definitions if statements for statements and while statements By default the amount of indentation is 4 spaces but this value can be changed Script Window Features The following sample function illustrates the indentation style that is supported testi function x ITR gt 0Y for i in 1 x catty An else Le Fo while i gt 0 estii a 1 hed 2 Modifying The default settings of the Script window can be changed by means Script Window of the Script dialog accessed by right clicking in a Script window Settings and selecting Properties from the pop up menu To disable any of the following properties deselect the appropriate check box Output Pane Word Wrap Auto Match and Auto Indent Auto Insert Right Brace To change the Tab Size enter the number of spaces desired in the corresponding text box To change the amount of time that matching delimiters are highlighted change the value shown in milliseconds in the Match Time msec text box To change the number of characters searched for an automatic match enter a value in the Match CharLimit text box The default value 1 means to search from the cursor to the top of the file 473 Chapter 9 Using the Script and Report Windows 474 The properties of a Script window can also be accessed from the Object Explorer To do
482. tput pane and not below each command The Commands window is preferable for doing interactive exploratory data analysis at the prompt while the Script window is useful for writing longer functions 463 Chapter 9 Using the Script and Report Windows THE SCRIPT WINDOW Any executable statements or commands can be entered into a Script window and executed For example you could enter the following S PLUS language expression in a Script window objects All Script windows have an output pane which contains output from print statements and information about warnings and error messages that occur when running your script The Script window program pane contains a line and column number indicator in the upper left of the window that helps you locate lines in your script when you are editing Seri pt Figure 9 2 The Script window toolbar Working With Scripts can be created or opened edited run saved and printed Scripts To create a new script do the following 1 From the main menu choose File gt New or click the New button L on the Standard toolbar A list of window types pops up 2 Select Script File and click OK New Data Set OK Graph Sheet Microsoft Excel Worksheet ae Object Explorer Report File Help Figure 9 3 The New dialog can be used to create many file types including scripts A new script is created and displayed in a window New scripts are given temporary default names 464 The
483. ts fail They are called accelerated failure time models or accelerated testing models when the product is tested under more extreme conditions than normal to accelerate its failure time The Parametric Survival and Life Testing dialogs fit the same type of model The difference between the two dialogs is in the options available The Life Testing dialog supports threshold estimation truncated distributions and offsets In addition it provides a variety of diagnostic plots and the ability to obtain predicted values This functionality is not available in the Parametric Survival dialog In contrast the Parametric Survival dialog supports frailty and penalized likelihood models which is not available in the Life Testing dialog Fitting a parametric survival model From the main menu choose Statistics gt Survival gt Parametric Survival The Parametric Survival dialog opens as shown in Figure 6 58 317 Chapter 6 Statistics 318 Parametric Survival Mm E Model Options Results m Data r Model Data Set capacitor X Distribution weibull hd Subset Rows with Scale 0 IV Omit Rows with Missing Values Fixed Parameters r Save Model Object Save As Formula Formula Survidays event voltage Create Formula Cancel Apply f current Help Figure 6 58 The Parametric Survival dialog Example The capacitor data set contains measurements from a simulated accelerated life test
484. ts in a graph for the sensors data Plots in separate panels We can also view the three plots in separate panels with a shared X axis 1 Click inside the graph but not on a point to select the graph 2 Open the Graph Tools palette by clicking the Graph Tools button T on the Graph toolbar 3 Click the Separate Panels with Varying Y Axes button on the Graph Tools palette Three scatter plots are created in separate panels Scatterplot Matrices Visualizing Multidimensional Data 4 Remove the legend by selecting it and pressing the DELETE key The resulting graph is shown in Figure 3 35 0 8 4 By a 0 4 7 ay a a PA EN a Bao 6 E 1 0 7 2 o m oo o 0 5 4 o Rp B o T S gr 0 0 T T T T 0 2 0 4 0 6 0 8 V1 Figure 3 35 Multiple plots in separate panels for the sensors data Notice that the relationship between V1 and V4 is very close The points fall almost exactly along a straight line The relationships between V1 and the other two variables v2 and v3 show more variation Now we will create a scatterplot matrix to view all of the variables 1 Select all of the columns in the sensors data by clicking in the upper left hand corner of the Data window 95 Chapter 3 Exploring Data 2 Click the Scatter Matrix button on the Plots 2D palette The resulting graph is shown in Figure 3 36 HPO sO FORO 1018 0 0 oe ot a8 a8 aa a v4 06 og ie i oA ue a8
485. tter plot and superimpose a moving average smooth with a default bandwidth choice Boxcar smoother 1 From the main menu choose Data gt Select Data 2 In the Source group ensure that Existing Data is selected Visualizing Two Dimensional Data 3 In the Existing Data group type sensors in the Name field and click OK 4 Select columns V5 and V6 5 From the main menu choose Graph gt 2D Plot to open the Insert Graph dialog 6 In the Plot Type list box select Smoothing Kernel Plot and click OK as shown in Figure 3 13 Insert Graph x Axes Type Plot Type Robust Least Trimmed Sq x y1 y2 Robust MM x y1 y2 Scatter Plot x y1 y2 Scatter Plots of XY Pairs x1 y1 x2 2 Smoothing Friedman Super x y1 y2 Smoothing Kernel Plot x y1 y2 Smoothing Loess Plot x y1 92 J Smoothing Spline Plot x y1 y2 Step Plot Horiz x y1 2 Step Plot Vert x y1 y2 Y Series Lines y1 y2 Vector Plot xstart ystart xend pend or x y gt Graph Sheet fast x Cancel Figure 3 13 The Insert Graph dialog This results in the plot shown in Figure 3 14 where a not so smooth curve is produced that fits the data rather poorly This is because the smoothing bandwidth is too small for these data 0 8 47 V6 0 474 0 0 T T T T 0 3 0 5 0 7 0 9 V5 Figure 3 14 Sensor 5 versus sensor 6
486. tters To run theSBATCH command do one of the following e From within Windows choose Run from the Start menu type your S PLUS SBATCH command line in the dialog and click OK From a DOS prompt type your S PLUS SBATCH command line and press ENTER For example suppose you are studying the effects of a cancer treatment on white blood count over time and each month you receive a report in the form of an ASCII file from each of 14 hospitals The ASCII file contains for each patient in the study the name age gender treatment type white blood count and other information You ve developed a function update data to read in one of the reports and update the data for each patient listed You use this function exactly 14 times each month when you read in each of the monthly reports It will save you time in the long run and be more convenient to use if you create an input file update dat containing the following update data hospital 1 update data hospital 2 update data hospital 14 459 Chapter 8 Using the Commands Window 460 Then each month copy each hospital s report to the appropriately numbered file hospital x When all the reports are in type Splus SBATCH logfile update slg update dat The command reads input from the file update dat the last argument entered and writes output and errors to the logfile update slg If you want to save error messages in a separate file you can specify an
487. ture 1 17 48174 45 32982 46 89140 wind 1 6 05985 33 90793 35 46950 eee Linger Mogel Call Im formula ozone radiation temperature wind data air na action na exclude Residuals Min 1G Median 30 Max 1 122 0 3704 0 02535 0 3361 1 495 Coefficients Value Std Error t value Pr gt t Generalized Additive Models Regression Intercept 0 2973 0 5552 0 5355 0 5934 radiation 0 0022 0 0006 3 9493 0 0001 temperature 0 0500 0 0061 8 1957 0 0000 wind 0 0760 0 0158 4 8253 0 0000 Residual standard error 0 5102 on 107 degrees of freedom Multiple R Squared 0 6807 F statistic 76 03 on 3 and 107 degrees of freedom the p value is 0 Generalized additive models extend linear models by flexibly modeling additive nonlinear relationships between the predictors and the response Whereas linear models assume that the response is linear in each predictor additive models assume only that the response is affected by each predictor in a smooth way The response is modeled as a sum of smooth functions in the predictors where the smooth functions are estimated automatically using smoothers Additive models may be useful for obtaining a final fit or for exploring what types of variable transformations might be appropriate for use in a standard linear model Fitting an additive model From the main menu choose Statistics Regression gt Generalized Additive The Generalized Additive Models dialog opens as shown in
488. u 505 Chapter 10 Using Spotfire S With Other Applications 506 3 A dialog opens showing you a list of the graph areas in this graph you can have multiple graph areas in a graph that is one graph area might be 2D and another might be 3D in the same graph and for each graph area a list showing all the plots in this graph area 4 Select the graph area and the plot in this area that you want to edit and then click Next 5 A Spotfire S plot properties dialog opens in SPSS allowing you to modify the properties of this plot Selecting data for graphs Before you can create a graph you must first select some data in the data editor You can select variables in the SPSS data editor by clicking on the column header where the variable name appears for each variable you want to include in the graph Spotfire S plots accept data in a variety of formats Some plots require at least three columns of data with the three columns being interpreted as x y and z data values Other plots require at least four columns of data with the four columns being interpreted as x y z and w data values For example consider the following variables in SPSS If you want to create two line plots in a graph you can select the variables xdata ydatal and ydata2 xdata ydatal ydata2 Using Spotfire S With SPSS The Create Spotfire S Graph wizard treats the xdata variable in this selection as the x data and the ydatal and
489. u can also choose Format gt Apply Style from the main menu to modify a Graph Sheet to match a particular style specification Because the Color Style and Black and White Style dialogs are identical except for the colors used in the latter case black white and shades of gray in this section we describe the various options available using the Color Style dialog The Color Style dialog consists of five tabbed pages of options discussed below The Options page of the Color Style dialog is shown in Figure 11 15 Color Style 15 x Options Lines Symbols Pattemn Color FillColor Basic Colors _ y Standard Bars Auto Change l User Colors Standard bad T Fill Pattern Image Colors Standard bd I Patter Color Line Auto Change I Fill Color T Line Style Grouped Bars Auto Change Line Color T Fill Pattern Symbol Auto Change 4 I Pattern Color A Spice T Fil Color I Symbol Color m Pie and Area Auto Change Tl Fill Pattern Tl Pattern Color M Fill Color f Cancel Figure 11 15 The Options page of the Color Style dialog Basic Colors group Select the User Colors and Image Colors schemes to be used for the style For information on editing the color palettes used in these color schemes see page 546 Line Auto Change group Select Line Style and or Line Color to have these properties change each time you add a line plot to the same graph Line styles and colors rotate in the o
490. ues logical values etc Vectors must be of a single mode you cannot have a vector consisting of the values T 2 3 If you try to create such a vector S PLUS coerces the elements to a common mode For example gt COLT 2 3 LI tah 2 3 425 Chapter 8 Using the Commands Window Matrix Data Objects 426 Vectors are characterized by their length and mode Length can be displayed with the length function and mode can be displayed with the mode function An important data object type in Spotfire S is the two way array or matrix object For example 30 Zal Tab 2 249 2220 7 0 10 0 16 1 5 3 21 0 2655 Matrices and their higher dimensional analogues arrays are related to vectors but have an extra structure imposed on them Spotfire S treats these objects similarly by having the matrix and array classes inherit from another virtual class the structure class To create a matrix use the matrix function The matrix function takes as arguments a vector and two numbers which specify the number of rows and columns For example gt matrix 1 12 nrow 3 ncol 4 Delt Es Let Lyt 1 1 4 Fi 10 2 2 5 8 11 3 3 6 9 12 In this example the first argument to matrix is a vector of integers from 1 through 12 The second and third arguments are the number of rows and columns respectively Each row and column is labeled the row labels are 1 1 2 3 and the column labels are 1 2 3 4 This nota
491. uld be detected for various sample sizes That is how much would the proportion of Democratic voters in the sample have to differ from the historical proportion of 40 to claim that the proportion is significantly different from the historical norm 1 Open the Binomial Power and Sample Size dialog 2 Select Min Difference as the value to Compute Enter 0 4 as the Proportion and 100 500 1000 5000 as the sample sizes N1 to consider 3 Click OK A power table is displayed in the Report window The table indicates the detectable differences delta for each sample size For example with 1000 observations the pollster could determine whether the proportion varies from 40 by at least 4 34 wae Power Table p null p alt delta alpha power nl FWP Fe Power and Sample Size 0 4 0 5372491 0 1372491 0 05 0 8 100 0 4 0 4613797 0 0613797 0 05 0 8 500 0 4 0 4434020 0 0434020 0 05 0 8 1000 0 4 0 4194100 0 0194100 0 05 0 8 5000 263 Chapter 6 Statistics EXPERIMENTAL DESIGN Factorial 264 Typically a researcher begins an experiment by generating a design which is a data set indicating the combinations of experimental variables at which to take observations The researcher then measures some outcome for the indicated combinations and records this by adding a new column to the design data set Once the outcome is recorded exploratory plots may be used to examine the relationship between the outcome and the experimental vari
492. uld be specified This option is available only in Spotfire S for Windows 163 Chapter 5 Importing and Exporting Data Table 5 1 Supported file types for importing and exporting data Continued Default Exten Format Type sion Notes DIRECT SYBASE DIRECT Sybase database connection No eee file argument should be specified Epi Info File EPI rec Fixed Format ASCII FASCII fix fsc File FoxPro File FOXPRO dbf Gauss Data File GAUSS dat Automatically reads the related GAUSS96 DHT file if any as GAUSS 89 If no DHT file is found reads the DAT file as GAUSS96 GAUSS96 is available in Unix only HTML Table HTML htm Export only Lotus 1 2 3 Work LOTUS wk wr sheet Matlab Matrix MATLAB mat File must contain a single matrix Spotfire S recognizes the file s platform of origin on import On export specify type MATLAB to create a pre MATLAB 7 version file otherwise specify type MATLAB7 to export the MATLAB 7 file format Minitab Workbook MINTTAB mtw Versions 8 through 12 Microsoft Access File ACCESS mdb Microsoft Access file This file type is available only in Spotfire S for Windows 164 Supported File Types for Importing and Exporting Table 5 1 Supported file types for importing and exporting data Continued Default Exten Format Type sion Notes Microsof
493. ults Data Set daims z Save As sz Variables MV Print Results Counts Variable number X Subset Rows with LO o Method to Handle Missing Values j Fail g Cancel Appt K current Figure 6 5 The Crosstabulations dialog Example Consider the data set claims which has the components age car age type cost and number The original data were taken from 8 942 insurance claims The 128 rows of the claims data set represent all possible combinations of the three predictor variables columns 207 Chapter 6 Statistics 208 age car age and type An additional variable number gives the number of claims in each cell The outcome variable cost is the average cost of the claims We can use a contingency table to examine the distribution of the number of claims by car age and type The corresponding test for independence tells us whether the effect of age upon the likelihood of a claim occurring varies by car type or whether the effects of car age and type are independent To construct a contingency table for the claims data 1 2 3 5 Open the Crosstabulations dialog Type claims in the Data Set field In the Variables field click on car age and then CTRL click type This selects both variables for the analysis In the Counts Variable field scroll through the list of variables and select number Click OK The table below appears in the Report window Call kk Crosst
494. umn and choose Format gt Selected Object from the main menu The column properties dialog opens as shown in Figure 2 13 Double Precision Column 1 oy x Name vil Format Type Decimal fd Width fi 2 Precision 2 Justification Right hd Description eg Cancel Apply of cent Figure 2 13 The Double Precision Column dialog 2 In the Name text box type a new column name or edit the existing name and click OK 31 Chapter 2 Working With Data Note The name of a properties dialog as it appears in the dialog s title bar is determined by the type of object selected when you open the dialog For example the Double Precision Column dialog opens for double precision columns the Character Column dialog opens for character columns etc Adding or Editing In addition to numbers and names columns can also have a Column Description Creating a Column List 32 descriptions If you specify a description for a column the description is used as the default axis title and legend text in graphs If no description is specified the column name is used instead Tips for specifying column descriptions e Column descriptions can contain up to 75 characters e Column descriptions can be any combination of letters numbers symbols and spaces To add or edit a column description do the following Open the column properties dialog as discussed on page 31 In the Description text box typ
495. umns either through the Data window or the Object Explorer and then click a palette button ots3D ix ots2D x g 2 L LA Le Le be Le Le pats Ta e etl cal Pea u E B8 P P le e Hoy k ii ius z a a ExtraPlots x A K ee ket E e 59 Eu OSEE ener ooo Haea V K bie l La A ded m SE eB a td L22 Bee OB Figure 4 1 The Plots 2D Plots 3D and Extra Plots palettes The distinction between the 2D and 3D palettes is whether plots are created with two axes or three Many 2D plots such as scatterplot matrices bubble color plots and contour plots can show data representing more than two dimensions In this chapter we organize the plots primarily by the dimensionality of the data 119 Chapter 4 Creating Plots The Insert Graph Dialog Plot Properties Dialogs Structuring Your Data to Plot 120 You can also create any plot type by selecting it in the Insert Graph dialog as shown in Figure 4 2 Insert Graph x Axes Type Plot Type Line with Isolated Points x y1 y2 a Line with Scatter x y1 y2 Line with Text as Symbols x y text Pareto Plot x y Pareto Plot Horiz x y Probability Plot x y PP Normal with Line x Quantile Quantile Plot x y QQ Normal with Line x Robust Least Trimmed Sq x y1 y2 Li bi ia Robust MM x i 2 wae 12 15 22 28 m 1S 48 LnX n Ln Ln i Scatter Plots of XY Pairs x1 v1 x2 2 1 na Graph
496. urce 180 Data Source Administrator 179 driver 180 ODBC tables exporting 183 importing 181 one sample tests 213 t test 213 One sample t Test dialog 213 One sample t Test dialog 217 One sample Wilcoxon Test dialog 219 One way Analysis of Variance dialog 239 online help 8 Open Spotfire S Project dialog 406 407 operators 432 arithmetic 187 432 comparison 433 exponentiation 433 logical 433 precedence hierarchy of 435 relational 187 sequence 433 unary 433 Oracle files hints for importing and exporting 190 193 Orthogonal Array Design dialog 265 P packages finding 52 Pack Columns dialog 50 237 palettes Extra Plots 119 Plots 2D 119 Plots 3D 119 par command 449 Pareto plots 130 partitioning around medoids 331 parzen kernel smoothers 72 pie charts 127 plot function 446 plot palettes Extra Plots 119 Plots 2D 119 Plots 3D 119 plots 3D line 144 3D scatter 144 3D surface 153 area 150 bar 128 bar 3D 153 box 122 box plot 122 bubble 145 bubble color 92 146 candlestick 147 color 88 145 comment 155 contour 111 152 contour 3D 152 creating 119 curve fitting 134 diagnostic for linear models 274 dot 127 error bar 148 for linear models 279 grouped bar 140 grouped box 140 high level functions for 447 high low 146 high low open close 147 histogram 125 in separate panels 94 levels 152 line 76 77 132 low level functions for 448 multipanel 109 multiple 449 NLS 135 par command for 449 Pareto 130 pie ch
497. urvives while the other member dies is called a discordant pair There are 16 discordant pairs in which the individual who received treatment A survived and the individual who received treatment B died There are five discordant pairs with the reverse situation in which the individual who received treatment A died and the individual who 251 Chapter 6 Statistics 252 received treatment B survived If both treatments are equally effective then we expect these two types of discordant pairs to occur with nearly equal frequency Put in terms of probabilities the null hypothesis is that p p where p is the probability that the first type of discordancy occurs and po is the probability that the second type of discordancy occurs Setting up the data We create a mcnemar trial data set containing the information in Table 6 6 1 Open an empty data set by clicking the New Data Set button B on the Standard toolbar 2 Enter the two columns of observations listed in Table 6 5 3 Change the column names by first double clicking on V1 and typing in B Survive and then double clicking on V2 and typing in B Die 4 Change the row names by double clicking in the gray box next to the first entry in each row Type A Survive for the first row name and A Die for the second row name You can resize the gray column to better view the row names to do this drag and drop the right border of the column to its desired width 5 Rename the d
498. use the OK or Apply buttons in the dialog to accept your changes If you choose Cancel or press ESC from the dialog the command that corresponds to the dialog is not recorded in the History Log You can edit lines in the History Log just as you would any other script These edits do not modify the History Log itself they only modify the copy in this Script window You can cut and paste parts of the script into other scripts execute portions of the script execute the entire script and save the script to a file The maximum size of the History Log the total number of operations recorded can be specified in the History Entries field of the Undo amp History dialog available through the Options menu 475 Chapter 9 Using the Script and Report Windows To view the current History Log in a Script window do the following 1 Click the History Log button amp on the Standard toolbar to display the History Log with default settings or from the Window menu choose History then Display The Display History Log dialog appears Display History Log of x Range Display Start with Entry fi Tl in Reverse Order End with Entry fs T for Selected Object Only Script Name History Cancel Apply current Help Figure 9 8 The Display History Log dialog 2 Specify any desired display options 3 Click OK to display the History Log Table 9 2 Options in the Display History Log dialog Field Description Start
499. ustrates some steps to fit a regression model to the fuel frame data containing five variables for 60 cars We do not show the output type these commands in your Commands window and you ll get a good feel for doing data analysis with the S PLUS language gt gt gt gt gt Ps gt Bs names fuel frame par mfrow c 3 2 plot fuel frame pairs fuel frame attach fuel frame par mfrow c 2 1 scatter smooth Mileage Weight scatter smooth Fuel Weight Im fitl lt Im Fuel Weight Im fitl names 1m fitl 455 Chapter 8 Using the Commands Window 456 VV VV FVM Vv tv tv Vv summary 1m fitl qqnorm residuals 1m fit1l plot Im influence Im fitl hat type h xlab Case Number ylab Hat Matrix Diagonal o type lt ordered Type c Smal1 Sporty Compact Medium Large Van par mfrow c 1 1 coplot Fuel Weight o type given values sort unique o type Im fit2 lt updateclm fitl Type Im fit3 lt update Im fit2 Weight Type anova Im fitl Im fit2 Im fit3 summary 1m fit3 Defining Functions DEFINING FUNCTIONS S PLUS is a powerful programming language that can be used to design large complex systems As with any programming language the more you learn about the S PLUS language the more of its power yowll be able to harness Unlike most programming languages however Spotfire S lets you use many of its features right away In this chapter
500. ute the interquartile range IQR 3Q 1Q The interquartile range provides a useful criterion for identifying outliers any observation that is more than 1 5 3 JOR above the third quartile or below the first quartile is a suspected outlier Statistical inference Because the Michelson data are probably not normal you should use the Wilcoxon signed rank test for statistical inference rather than the Student s test For illustrative purposes we use both To compute Student s t confidence intervals for the population mean value location parameter u we use the One sample t Test dialog This dialog also computes Student s t significance test p values for the parameter Uy 299 990 1 Open the One sample t Test dialog 2 Type exmichel in the Data Set field 3 Select speed as the Variable 4 Suppose you want to test the null hypothesis value Uy 990 plus 299 000 against a two sided alternative and you want to construct 95 confidence intervals Enter 990 as the Mean Under Null Hypothesis 5 Click OK The results of the one sample t test appear in the Report window One sample t Test data speed in exmichel t 3 4524 df 19 p value 0 0027 alternative hypothesis true mean is not equal to 990 95 percent confidence interval 859 8931 958 1069 sample estimates 217 Chapter 6 Statistics One Sample Wilcoxon Signed Rank Test 218 mean of x 909 The computed mean of the Michelson data is 909
501. value to 17 gives the full length of double precision numbers Time Series Eps Specify the time series comparison tolerance This small number is used throughout the time series functions for comparison of their frequencies Frequencies are considered equal if they differ in absolute value by less than the number specified here Tools group Editor Specify the default text editor command to be used by the edit function Whatever editor you choose is invoked in the style of Notepad that is by a command of the form Notepad filename followed by the reading of editing commands Do not supply editors that expect a different invocation or a different form of user interaction Pager Specify the default pager program to be used by the help and page functions Whatever pager you choose is invoked as pager filename and should read from filename Changing Defaults and Settings Command Line To specify command line options choose Options gt Command Font Options Line from the main menu The Command Line Options dialog consists of two tabbed pages of options discussed below The Font page of the Command Line Options dialog is shown in Figure 11 7 Command Line Options lolx Font Options Font Courier New 7 f Styles T Bold Font Size fg T Italics Color HB Blue X T Underline Cancel Figure 11 7 The Font page of the Command Line Options dialog Use the options on this page to specify the font font si
502. ven fractional membership in multiple clusters Performing fuzzy partitioning From the main menu choose Statistics Cluster Analysis gt Fuzzy Partitioning The Fuzzy Partitioning dialog opens as shown in Figure 6 66 333 Chapter 6 Statistics 334 Fuzzy Partitioning BE x Model Resuts Pit m Data r Dissimilarity Measure Data Set state af bs Metric euclidean ad Variables I Standardize Variables m Options Num of Clusters 2 Genes Harve oar ie Sif p Save Model Object Save As M Omit Rows with Missing Values M Save Data r Dissimilarity Object I Use Dissimilarity Object Sayed Object l z M Save Dissimilarities Cancel Apply k curent Help Sea aw Kf Figure 6 66 The Fuzzy Partitioning dialog Example In K Means Clustering on page 330 we clustered the information in the state df data set using the k means algorithm In this example we use fuzzy partitioning l wb 5 If you have not done so already choose File gt Load Library to load the examp1e5 library This library contains a few example data sets that are not in the main Spotfire S databases including state df Open the Fuzzy Partitioning dialog Type state df in the Data Set field CTRL click to select the Variables Population through Area Click OK A summary of the clustering appears in the Report window Example 2 In Compute Dissimilarities on page
503. were generated by the actions you made in the menus and dialogs To clear the History Log from the Window menu choose History then choose Clear from the submenu By default the History Log is written in a condensed form that shows the main commands with required inputs and only those optional inputs that differ from their default values You can ask Spotfire S to show a full history with each command and all its parameters including defaults The full history is useful for example if you want a record of exactly which options were used to create a specific plot If you are using the History Log to learn how to create Spotfire S graphics from a script or in the Commands window the condensed form will be more useful to you To choose the type of History Log do the following 1 From the main menu choose Options gt Undo amp History 2 Select Condensed or Full from the History Type dropdown list 477 Chapter 9 Using the Script and Report Windows Dragging Another way to create scripts is to drag objects from a graph such as Graph Objects plots or extra symbols into a Script window If you want to know Into a Script which S PLUS language commands are used to create or modify a particular editable plot you can drag it into a Script window and the Window S PLUS command to create it will be written there automatically You can then run the generated script to create or modify the plot This is an alternative to using menu
504. with Entry Specify the starting and ending entry numbers to be End with Entry displayed in the History Log This lets you control the number of History Entries and which ones get placed in the History Log Display in Reverse Order Choose to display the History Entries in the reverse order in which they were generated You will see the command executed most recently at the top of the script Display for Selected Object Only Choose to have the script contain History Entries for the selected object only This is useful when you want to focus on the commands for a specific object For example if you select a symbol the script will contain all the entries related to creating and modifying the symbol 476 Time Saving Tips for Using Scripts Table 9 2 Options in the Display History Log dialog Continued Field Description Script Name Condensed vs Full Specify a name for the script that will contain the History Log This is optional the default script name is History To execute recorded commands in the History Log 1 Use the mouse to highlight the History Entries you want to execute 2 Click the Run button on the Script window toolbar or choose Run from the Script menu You can also cut paste and save these commands to another script file You should clear the History Log before you start recording steps This will save editing time later and make it clearer what commands
505. with a box kernel smoother line 71 Chapter 3 Exploring Data 72 Experimenting with the bandwidth l Now right click one of the points in the scatter plot or the curve fit line and select Smooth Sort from the shortcut menu On the Smooth Sort page of the Line Scatter Plot dialog notice the default value for the smoother bandwidth look in the Bandwidth box of the Kernel Specs group Experiment with various bandwidth choices between 0 1 and 0 6 by entering different numbers in the Bandwidth box and clicking Apply so that the Line Scatter Plot dialog remains open Which bandwidth produces the best by eyeball curve fit The smoother with bandwidth choice 0 3 is shown in Figure 3 15 V5 Figure 3 15 Sensor 5 versus sensor 6 with a box kernel smoother line using bandwidth 0 3 Changing the kernel smoother type to Parzen smoother With the Line Scatter Plot dialog still open to the Smooth Sort page open the dialog again if you closed it select Parzen from the Kernel pull down list in the Kernel Specs group and click Apply The Parzen kernel is a box convolved with a triangle Experiment again with the choice Spline Smoothers Visualizing Two Dimensional Data of bandwidth selection Do you get a nicer smooth curve fit The Parzen kernel smoother with bandwidth 0 15 is shown in Figure 3 16 0 8 7 0 6 7 V6 0 27 0 0 T T T T V5 Figure 3 16 Sensor 5 versus sensor
506. without saving the changes You can also use Undo to undo the last replacement made to the script To find text do the following 1 Click the Find button on the Script window toolbar or choose Find from the Edit menu or press CTRL F The Find dialog will pop up 2 In the Find what box type the text you re searching for Find 12 x Find what a I Match whole word only Cancel I Match case Figure 9 6 The Find dialog will find a string of up to 255 characters the text will scroll horizontally as you type 469 Chapter 9 Using the Script and Report Windows If you used Find or Replace in your current work session the text you last searched for is selected in the Find what box Type over the text to find different text 3 Choose Find Next to begin searching The Find dialog has the options in Table 9 1 Table 9 1 Check box options in the Find and Replace dialogs Option Purpose Match whole word only Choose this option to find whole words not substrings Match case Choose this option to find only words having the specified pattern of uppercase and lowercase letters To find and replace text do the following 1 From the Edit menu choose Replace or type CTRL H The Replace dialog will pop up 2 In the Find what box type the text you re searching for 3 Ifyou used Find or Replace in your current work session the text you last searched for is selected in the Find what
507. ws fields of the From group specify by column and row positions the cell or block of cells you want to move or copy 3 In the Columns and Rows fields of the To group specify the target location by column and row positions and click OK Hint To move or copy the cell or block to another data set select its name from the Data Set dropdown list of the To group To create a target data set type a new name in this field Moving and The procedures for moving and copying columns and rows are the Copying Columns same as those outlined above for moving and copying cells and and Rows blocks with the following additional comments 39 Chapter 2 Working With Data When you move or copy a column or row by dragging note the following To drag a column or row position the cursor within the selected column or row not within the column or row header Spotfire S moves or copies the whole column or row as a unit including the name Names of copied columns and rows are appended with 1 Moving or copying data to a target location that already contains data overwrites the existing data When you move or copy a column or row using Cut Copy and Paste note the following Spotfire S moves or copies only the data values in the column or row to the new location Moving or copying data to a target location that already contains data overwrites the existing data As shown in Figure 2 20 the Data menu dialogs for moving and cop
508. ws for nested random effects Fitting a linear mixed effects model From the main menu choose Statistics Mixed Effects Linear The Linear Mixed Effects Models dialog opens as shown in Figure 6 52 Linear Mixed Effects Models _ x Model Options Results Plot Predict m Data Data Set Orthodont x SUbsot Hows wih r Save Model Object M Omit Rows with Missing Values i Save As r Random Effects Group Variables Subject 7 Advanced Random Terms fage 7 Random Formula E age Subject T Fired Effects Dependent distance X Independent Formula fistance agee tt stsi Ss Create Formula Cancel Apply f curent Help Figure 6 52 The Linear Mixed Effects Models dialog Nonlinear Mixed Effects Example The Orthodont data set has 108 rows and four columns and contains an orthodontic measurement on eleven girls and sixteen boys at four different ages We use a linear mixed effects model to determine the change in distance with age The model includes fixed and random effects of age with Subject indicating the grouping of measurements 1 Open the Linear Mixed Effects Models dialog 2 Type Orthodont in the Data Set field 3 Specify distance age in the Formula field 4 Select Subject as a Group Variable and age as a Random Term The Random Formula field is automatically filled in as age Subject 5 Click OK A summary of the model is
509. ws in a data set leave the Subset Rows field blank Some dialogs have Variables fields These fields are automatically filled in with the column names in the data set you have selected Some dialogs require a Formula which is automatically filled in if you have selected columns of a data set The first selected column is the response and the remaining columns are the predictors If you do not want the formula that automatically appears you can type another one directly in the Formula field or click the Create Formula button to bring up a dialog that builds a formula for you Some dialogs such as the Generalized Additive Models dialog require special formulas in these cases the special terms available are listed in the Formula Builder Most dialogs have a Save As field that corresponds to the name of the object in which the results of the analysis are saved This object may be manipulated in the Object Explorer to obtain additional summaries or plots after the model has been fit Many of the modeling dialogs also have one or more Save In fields The Save In 201 Chapter 6 Statistics Plotting From the Statistics Dialogs 202 field corresponds to the name of a data set in which new columns are saved Examples of new columns include fitted values residuals predictions and standard errors Most of the statistics dialogs produce default plots that are appropriate for the analysis Many have several plot options usually on a s
510. x Proportional Hazards Parametric Survival Life Testing Tree Tree Models Tree Tools Compare Models Cluster Analysis Compute Dissimilarities K Means Clustering Partitioning Around Medoids Fuzzy Partitioning Agglomerative Hierarchical Clustering Divisive Hierarchical Clustering Monothetic Clustering Multivariate Discriminant Analysis Factor Analysis 283 285 286 288 292 294 295 297 300 300 301 303 306 306 307 310 310 311 314 314 315 317 319 322 322 323 326 329 329 330 331 333 335 336 338 340 340 341 Principal Components MANOVA Quality Control Charts Continuous Grouped Continuous Ungrouped Counts and Proportions Resample Bootstrap Inference Jackknife Inference Smoothing Kernel Smoother Local Regression Loess Spline Smoother Supersmoother Examples Time Series Autocorrelations ARIMA Lag Plot Spectrum Plot Random Numbers and Distributions Random Sample of Rows Distribution Functions Random Numbers References 342 344 346 346 347 348 351 351 353 355 356 356 357 357 358 359 359 361 363 364 366 366 367 372 376 197 Chapter 6 Statistics INTRODUCTION Overview 198 The power of Spotfire S comes from the integration of its graphics capabilities with its statistical analysis routines In other chapters throughout this manual we introduce Spotfire S graphics In this chapter we show how statistical procedures ar
511. y are also useful in programming Matrices Another important object in S PLUS is the matrix a rectangular two dimensional array of data values in which all the elements are of the same mode Like vectors matrices are produced by some statistical techniques and they too are useful in programming Data frames In Spotfire S the primary structure for storing two dimensional data is the data frame Note Throughout this User s Guide we have rather loosely referred to two dimensional data as data sets However in Spotfire S every object has a particular class and the class of these objects is data frame Classes and methods play an important part in programming in Spotfire S For a thorough treatment of these concepts see the Programmer s Guide 379 Chapter 7 Working With Objects and Databases Interface Objects Document Objects 380 A data frame can contain columns of differing mode For example in a two column data frame one column can contain numeric data while the second column can contain character data In a data frame each row represents an experimental unit Lists A list is the most general and most flexible object for holding data in Spotfire S A list is an ordered collection of components Each component can be any data object and different components can be of different modes For example a list might have three components consisting of a vector of character strings a matrix
512. ying columns and rows are very similar to those for cells and blocks Copp Columns PEIES m From r To r From rTo Data Set ethanol E Data Set ethanol X Data Set ethanol z Data Set ethanol X Columns NOx bd Columns lt END gt Columns NOx X Columns lt END gt fosi MV Overwrite Overrite OK Cancel Apply f current OK Cancel Apply i current Copy Rows Fie m From flo r From To Data Set ethanol X Data Set ethanol X Data Set ethanol X Data Set ethanol X Bows fi Rows lt END gt Rows fi Rows lt END gt M Overwrite M Overwrite OK Cancel Apply LECE Figure 2 20 The Move Columns Copy Columns Move Rows and Copy Rows dialogs OK Cancel Apply LECE When you move or copy a column or row using the Data menu note the following 40 Manipulating Data Spotfire S moves or copies the whole column or row as a unit including the name Names of copied columns and rows are appended with 1 e By default moving or copying data to a target location that already contains data overwrites the existing data However you can avoid overwriting your existing data by clearing the Overwrite check box at the bottom of the dialogs When you clear this check box Spotfire S shifts existing columns to the right or existing rows down to make room for the moved or copied data Hint You can copy row names into and
513. you leave this option selected Default Text Col By default Spotfire S creates a factor type column when you type character data into an empty column in a Data window To create a character type column by default select character in this field Ragged data frame By default Spotfire S expects all columns in a data set to be of equal length and will pad shorter columns with NAs to even out the columns To prevent this behavior select this option Excel Link group Save link information When this option is selected link information is stored in the source object If the source is an Excel document link information is written as a comment in the upper left cell of the data range If the source is an S PLUS data frame link information is stored as an attribute of the data frame Prompt on overwrite When selected Spotfire S issues a prompt when overwriting data in either an Excel document or an S PLUS data frame whichever is the target for the particular link Remove data on doc close When selected linked data are removed when the current document is closed Remove data on link close When selected linked data are removed when the current link is closed Selection includes col names When selected Spotfire S automatically interprets the first row in the selected data range as the column names row Selection includes row names When selected Spotfire S automatically interprets the first column in the selected data range as
514. z Be Cancel Apply current Help Figure 6 23 The Fisher s Exact Test dialog Compare Samples Example The data set shown in Table 6 5 contains a contingency table summarizing the results of a clinical trial Patients were divided into a treatment group that received an experimental drug and a control group that did not These patients were then monitored for 28 days with their survival status noted at the end of the study Table 6 5 A contingency table summarizing the results of a clinical trial Control Treated Died 17 7 Survived 29 38 Setting up the data We create a fisher trial data set containing the information in Table 6 5 1 Open an empty data set by clicking the New Data Set button E on the Standard toolbar 2 Enter the two columns of observations listed in Table 6 5 3 Change the column names by first double clicking on V1 and typing in Control and then double clicking on V2 and typing in Treated 4 Change the row names by double clicking in the gray box next to the first entry in each row Type Died for the first row name and Survived for the second row name You can resize the gray column to better view the row names to do this drag and drop the right border of the column to its desired width 5 Rename the data set by double clicking in the upper left corner of the Data window In the dialog that appears type fisher trial in the Name field and click OK Statistic
515. ze color and styles to be used in the Commands window To ensure proper alignment of output a fixed width font such as Letter Gothic or Courier is recommended The Options page of the Command Line Options dialog is shown in Figure 11 8 Command Line Options _ oy x Font Options Background Color C Bright wr Main Prompt bt T Echo Continue Prompt fo I Keep Window Focus Key Scroll None Cancel Help Figure 11 8 The Options page of the Command Line Options dialog Background Color Select a background color to be used in the Commands window Echo When selected each complete expression is echoed before it is evaluated Keep Window Focus When selected the Commands window remains in focus when commands are executed 535 Chapter 11 Customizing Your Spotfire S Session Undo and History 536 Main Prompt Specify the string to be used to prompt for an expression The default is gt Continue Prompt Specify the string to be used to prompt for the continuation of an expression The default is Key Scroll Select a method for scrolling through the Commands window To specify undo and history options choose Options Undo amp History from the main menu The Undo and History dialog is shown in Figure 11 9 Undo and History oi x of Graph Undos fi 0 History Entries 1000 History Type Condensed i Cancel Help Figure 11 9 The Undo and History dialog of Graph Undos S

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