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Package `Hmisc` - R

1. mydframe c age sex lt NULL delete 2 variables mydframe Cs age sex lt NULL Same thing The last example uses the Hmisc short cut quoting rj function Cs See also the drop parameter to upData Step 5 Make changes to individual variables within the data frame After importing data the resulting variables are seldom self documenting so we commonly need to change or enhance attributes of individual variables within the data frame If you are only changing a few variables it is efficient to change them directly without attaching the entire data frame EV sex lt factor FEVSsex 0 1 c female male 56 data frame create modify check FEVSsmoke lt factor FEV smoke 0 1 c non current smoker current smoker units FEVSage lt years units FEVS fev lt L label FEVS fev lt Forced Expiratory Volume units FEVSheight lt inches When changing more than one or two variables it is more convenient change the data frame using the Hmisc upData function FEV2 lt upData FEV rename c smoking smoke omit if renamed above drop c varl var2 levels list sex list female 0 male 1 smoke list non current smoker 0 current smoker 1 units list age years fev L height inches labels list fev Forced Expiratory Volume An alternative to level
2. mar list e 4 3 2 I 0 1 e 6 4 42225 2 0 1 3 3 1 2 0 1 5 4 2425 2 0 1 ltypell pch 202 view FALSE pcx FALSE tiff FALSE close view pcx tiff bty 1 type 2 height switch type NULL NULL 5 8 width switch type NULL NULL 7 7 tck if type 3 under unix 0 013 else par tck las if type 3 1 else 0 eps FALSE setps filename w 0 h 3 pointsize 10 sublines 0 toplines 0 type symbol lwd 2 font Helvetica leftlines 0 las 1 trellis missing setTrellis amp missing strip blank amp missing lty dot line amp missing lwd dot line setTrellis TRU Gl F ps slide 197 strip blank TRUE lty dot line 1 lwd dot line 1 seqno NULL color FALSE setpdf filename w 0 h 4 pointsize 10 sublines 0 toplines 0 type symbol lwd 1 5 font if R Helvetica else 1 ratio if R 4 3 else 1 sqrt 5 2 leftlines 0 las 1 bty 1 hor FALSE trellis missing setTrellis amp missing strip blank amp setTrellis TRUE strip blank TRU region c 0 0 h tex string missing lty dot line amp missing lwd dot line r kcd lty dot line 1 lwd dot line 1 W color FALSE seqno NULL lref c psref c scale 1 srt 0 showPsfrag filename Arguments file filename string background foreground
3. Quantile transace 283 quantile 48 63 114 180 186 322 Quantile areg boot transace 283 345 Quantile cph 23 Quantile2 spower 247 rank 247 322 rbind 243 rbinom 226 rcorr 124 183 198 318 rceorr cens 199 203 247 rcorrcens rcorr cens 199 reorrp cens 201 202 rcs 205 208 respline eval 204 206 208 223 303 respline plot 205 respline restate 205 207 resplineFunction rcspline restate 207 read csv 42 312 read dta 254 255 read spss 253 read table 50 read xport 229 redun 209 reLabelled label 138 reorder factor 211 rep 157 177 replace substring wild sedit 239 require 212 requirePackage 212 reShape 213 223 329 reshape 214 legend 216 rlegendg rlegend 216 rm boot 218 rMultinom 225 robcov 60 round 59 307 round POSIXt trunc POSIXt 307 roundN dataRep 57 rug 236 K sample 128 samplesize bin 25 226 sapply 158 sas get 42 50 63 140 229 253 312 sasdsLabels 36 sasdsLabels sasxport get 227 sasxport get 35 227 Save 230 346 save 231 scale 97 scan 50 scatld 1 5 136 180 186 231 score binary 238 search 257 sedit 239 segments 236 sepUnitsTrans Misc 168 setpdf ps slide 192 setps 136 329 setps ps slide 192 setTrellis 196 setTrellis xYplot 324 how col show pch 242 how dvi latex 142 how html html 125 how latex latex 1
4. Arguments x a numeric predictor y a numeric response For binary logistic regression y should be 0 1 model Logistic or cox For cox uses the coxph fit withmethod efron function xrange range for evaluating x default is and 1 f quantiles of x where 10 max n 200 event event censoring indicator if model cox If event is present model is assumed to be cox respline plot 209 nk number of knots knots knot locations default based on quantiles of x by rcspline eval show xbeta or prob what is plotted on y axis adj optional matrix of adjustment variables xlab x axis label default is label attribute of x ylab same for y ylim y axis limits for logit or log hazard plim y axis limits for probability scale plotcl plot confidence limits showknots show knot locations with arrows add add this plot to an already existing plot subset subset of observations to process e g subset sex male ity line type for plotting estimated spline function noprint suppress printing regression coefficients and standard errors m for model logistic plot grouped estimates with triangles Each group contains m ordered observations on x smooth plot nonparametric estimate if model logistic and adj is not specified bass smoothing parameter see supsmu main main title default is e g Estimated Spline Transformation statloc location of summary statistics Default positioning by clicking left m
5. m lt matchCases x case y case id case x case y case id case tol 1 iscase lt m type case Note the first tapply on insures that event indicators are sorted by case id The second actually does something vent cas lt tapply m y iscase m idcase iscase sum event control lt tapply mSy iscase m idcase iscase sum hn control lt tapply iscase mSidcase sum n lt tapply mSy mSidcase length or lt sum event case n control event control n sum event control x 1 event case n or Bootstrap this estimator by sampling with replacement from subjects Assumes id is unique when combine cases controls id was constructed this way above The following algorithms puts all sampled controls back with the cases to whom they were originally matched ids lt unique mSid idgroups lt split l nrow m mSid B lt 50 in practice use many more 102 first word ors lt numeric B Function to order w by ids leaving unassigned elements zero align lt function ids w z lt structure rep 0 length ids names ids z names w lt w Z for i in 1 B j lt sample ids replace TRUE obs lt unlist idgroups j u lt m obs iscase lt u type case n case lt align ids tapply u type u idcase function v sum v case n control lt align ids tapply uStype u idcase function v sum v cont
6. plot w 3 idcol cols par ask FALSE mfrow c 1 1 plot w 1 lattice show n 1 results plot w 3 lattice show n 4 5 results plot w 3 lattice idcol cols same but different color mapping plot w 3 lattice m 1 show a single representative curv Show median 10th and 90th percentiles of supposedly representative curves plot w 3 lattice m quantiles probs c 5 1 9 Same plot but with much less grouping of x variable plot w 3 lattice m quantiles probs c 5 1 9 nx 2 Smooth data before profiling This allows later plotting to plot smoothed representative curves rather than raw curves which specifying smooth TRUE to curveRep would do if curveSmooth was not used lt curveSmooth x y id lt with d curveRep x y id Q Example to show that curveRep can cluster profiles correctly when there is no noise In the data there are four profiles flat flat at a higher mean y linearly increasing then flat and flat at the first height except for a sharp triangular peak JE SR E HE set seed 1 x lt 0 100 50 cut2 m lt length x profile lt matrix NA nrow m ncol 4 profile 1 lt rep 0 m profile 2 lt rep 3 m profile 3 lt c 0 3 rep 3 m 4 profile 4 lt c 0 1 3 1 rep 0 m 4 col lt c black blue green red matplot x profile type 1l col col xeval lt seq 0 100 length out
7. Arguments x a numeric vector may be a character or category or factor vector for wtd table num vector of numerator frequencies denom vector of denominators numbers of trials 324 wtd stats weights a numeric vector of weights normwt specify normwt TRUE to make weights sum to length x after deletion of NAs na rm set to FALSE to suppress checking for NAs probs a vector of quantiles to compute Default is 0 min 25 5 75 1 max type For wtd quantile type defaults to quantile to use the same inter polated order statistic method as quantile Set type to i 1 n 1 i nt 1 or i n to use the inverse of the empirical distribution function using respectively wt 1 T wt T 1 or wt T where wt is the cu mulative weight and T is the total weight usually total sample size These three values of type are the possibilities for wtd Ecdf For wtd table the de fault type is list meaning that the function is to return a list containing two vectors x is the sorted unique values of x and sum of weights is the sum of weights for that x This is the default so that you don t have to convert the names attribute of the result that can be obtained with type table to a numeric variable when x was originally numeric type table for wtd table results in an object that is the same structure as those returned from table For wtd loess noiter the default type is al1 indicat ing that the function is to re
8. type b nNX FALSE means to treat month as a discrete variable To display means and bootstrapped nonparametric confidence intervals use 334 xYplot s lt summarize y llist month year smean cl boot s xYplot Cbind y Lower Upper month year data s type b Can also use Y lt cbind y Lower Upper xYplot Cbind Y Or xYplot y month year nx FALSE method smean cl boot type b This example uses the summarize function in Hmisc to compute the median and outer quartiles The outer quartiles are displayed using filled bands s lt summarize y llist month year smedian hilow conf int 5 filled bands default fill pastel colors matching solid colors in superpose line this works differently in R xYplot Cbind y Lower Upper month groups year method filled bands data s type 1 note colors based on levels of selected subgroups not first two colors xYplot Cbind y Lower Upper month groups year method filled bands data s type 1 subset year 1998 year 2000 label curves FALSE filled bands using black lines with selected solid colors for fill xYplot Cbind y Lower Upper month groups year method filled bands data s label curves FALSE type 1 col 1 col fill 2 3 Key 5 8 col 2 3 use fill colors in key A good way to check for stable variance of residua
9. plot single Kaplan Meier curve from heart data heart surv lt survfit Surv stop event 1 figure 3 traditional Kaplan Meier curve postscript ehgfig3 ps horiz TRUE omi lt par omi c 0 1 25 0 5 1 25 Converting censored events to 2 or 1 0O is censored data heart one as numeric unlist heart heart id i 2 from 0 unlist heart one 3 for one transplant first creating survival object conf int FALSE event history plot heart surv xlab observation time title Figure 3 dev off Kaplan Meier curve now draw event history graph for postscript ehgfig4 ps horiz TRU par omi c 0 1 25 0 5 1 25 event history heart one survtime col heart one 2 97 ylab estimated survival probability in days for Stanford data cex 0 8 Stanford heart data use as Figure 4 E colors seq 0 1 len 20 surv col heart one 3 covtime cols cbhind rep 0 dim heart one 1 heart one 1 cov cols cbhind rep 0 dim heart one 1 heart one 7 num colors 2 colors c 6 10 x lab time under observation in days title Figure 4 cens mark right TRU cens mark ahead 30 dev off now use as Figure 5 two plots postscript c temp ehgfig5 ps par omi c 0 1 25 0 5 1 25 par mfrow c 1 2 event history data heart one subset rows Event history graph for nStanf
10. Description Plot smoothed estimates of x vs y handling missing data for lowess or supsmu and adding axis labels Optionally suppresses plotting extrapolated estimates An optional group variable can be specified to compute and plot the smooth curves by levels of group When group is present the datadensity option will draw tick marks showing the location of the raw x values separately for each curve p1smo has an option to plot connected points for raw data with no smoothing panel plsmo is a panel function for trellis for the xyplot function that uses plsmo and its options to draw one or more nonparametric function estimates on each panel This has advantages over using xyplot with panel xyplot and panel loess 1 by default it will invoke labcurve to label the curves where they are most separated 2 the datadensity option will put rug plots on each curve instead of a single rug plot at the bottom of the graph and 3 when panel plsmo invokes plsmo it can use the super smoother Supsmu function instead of Lowess panel plsmo senses when a group variable is specified to xyplot so that it can invoke panel superpose instead of panel xyplot Using panel plsmo through trellis has some advantages over calling plsmo directly in that conditioning variables are allowed and trellis uses nicer fonts etc When a group variable was used panel plsmo creates a function Key in the session frame that the user can invoke to draw a key for indiv
11. par opar wtd stats Weighted Statistical Estimates Description These functions compute various weighted versions of standard estimators In most cases the weights vector is a vector the same length of x containing frequency counts that in effect wtd stats 323 expand x by these counts weights can also be sampling weights in which setting normwt to TRUE will often be appropriate This results in making weights sum to the length of the non missing elements in x normwt TRUE thus reflects the fact that the true sample size is the length of the x vector and not the sum of the original values of weights which would be ap propriate had normwt FALSE When weights is all ones the estimates are all identical to unweighted estimates unless one of the non default quantile estimation options is specified to wtd quantile When missing data have already been deleted for x weights and in the case of wtd loess noiter y specifying na rm FALSE will save computation time Omit ting the weights argument or specifying NULL or a zero length vector will result in the usual unweighted estimates wtd mean wtd var and wtd quantile compute weighted means variances and quan tiles respectively wtd Ecdf computes a weighted empirical distribution function wtd table computes a weighted frequency table although only one stratification variable is supported at present wtd rank computes weighted ranks using mid ranks
12. sepUnitsTrans c 3 days 4 months 2 years 7 mtitle 175 set seed 1 whichClosest 1 100 3 5 whichClosest 1 100 rep 3 20 whichClosePW 1 100 rep 3 20 whichClosePW 1 100 rep 3 20 f 05 whichClosePW 1 100 rep 3 20 f le 10 x lt seq 1 1 by 01 Vokes KAZ h lt inverseFunction x y formals h Sturns vertex a lt seq 0 1 by 01 plot 0 0 type n xlim c 5 1 5 lines a h a 1 first inverse lines a h a 2 col red second inverse a lt EEEa LaOl 2420 E2 points a h a 1 Not run getLatestSource recent 5 source most recent 5 revised files in Hmisc getLatestSource ae fetch and source latest cut2 s getLatestSource all get everything getLatestSource ae TRUE list available files and latest versions End Not run mtitle Margin Titles Description Writes overall titles and subtitles after a multiple image plot is drawn If par Soma c 0 0 0 0 title is used instead of mt ext to draw titles or subtitles that are inside the plotting region for a single plot Usage mtitle main 11 lc lr if R format Sys time Sdsb y else if under unix unix date t sdth y else date cex m 1 75 cex 1 5 none Arguments main main title to be centered over entire figure default is none 11 subtitle for lower left of figure default is none Te subtitle for lower center of figure default is
13. S3 method for class formula describe x descript data subset na action digits 4 weights S3 method for class describe print x condense TRUE S3 method for class describe latex object title NULL condense TRUE file paste describe first word expr attr object descript tex sep append FALSE size small tabular TRUE greek TRUE S3 method for class describe single latex object title NULL condense TRUE vname file append FALSE size small tabular TRUE greek TRUE Arguments x a data frame matrix vector or formula For a data frame the describe data frame function is automatically invoked For a matrix describe matrix is called For a formula describe data frame model frame x is invoked The formula describe descript 65 may or may not have a response variable For print or latex x is an object created by describe optional title to print for x The default is the name of the argument or the label attributes of individual variables When the first argument is a formula descript defaults to a character representation of the formula exclude missing digits listunique listnchar weights minlength normwt object title condense data subset na action set toTRUE to print the names of variables that contain only missing values This list appears at the bottom of the printou
14. dvips options latexcmd latex For MacOS specify options xdvicmd MacdviX or if TeXShop is installed code options xdvicmd open If running S Plus and your directory for temporary files is not tmp Unix Linux or windows temp Windows add your own tempdir function suchas tempdir lt function yourmaindirectory your To prevent the latex file from being displayed store the result of latex in an object e g w lt latex object file foo tex Value latex and dvi return a list of class latex or dvi containing character string elements file and style file contains the name of the generated file and style is a vector possibly empty of styles to be included using the LaTeX2e usepackage command latexTranslate returns a vector of character strings Side Effects creates various system files and runs various Linux UNIX system commands which are assumed to be in the system path Author s Frank E Harrell Jr Department of Biostatistics Vanderbilt University f harrell vanderbilt edu Richard M Heiberger Department of Statistics Temple University Philadelphia PA rmh astro ocis temple edu David R Whiting School of Clinical Medical Sciences Diabetes University of Newcastle upon Tyne UK david whiting ncl ac uk See Also html format df texi2dvi 154 IdBands Examples x lt matrix 1 6 nrow 2 dimnames list c a b c c d this that
15. plot x xlab Time ylab Z actual NULL S3 method type b labels NULL for class ldBands summary object stdiff NULL n NULL pl NULL p2 NULL hr NULL events NULL pbar NULL sd NULL S3 method print x Arguments n times alpha sided alphaLower alphaUpper information for class summary ldBands number of interim analyses If times is given is automatically taken as the length of times For summary ldBands n is the sample size to obtain detectable standardized difference times at which interim analyses are done overall a level for the multiple tests Default is 0 05 If sided 3 is the a level for the lower bounds otherwise is the total a set to 1 to use a one sided test 3 for asymmetric two sided bounds a level for lower bound if sided 3 Defaults to a 2 When sided 3 alpha is recalculated from alphaLower alphaUpper a level for upper bound if sided 3 Defaults to a 2 a vector of information times if different from times Used for computing covariances of test statistics spending phi spending2 phi2 truncate power pr x xlab ylab actual type labels object stdiff pl p2 hr events pbar sd Details IdBands an a spending function Default is O Brien Fleming function If sided 3 is the spending function for the lower bounds parameter for the third and fourth types of spending functions expon
16. 286 transace See Also Ea tese Examples set seed 1 y lt rnorm 800 group lt sample 1 2 800 TRUE cluster lt sample 1 40 800 TRUE table cluster group t test y group R only t test cluster y cluster group Note negate estimates of differences from t test to compare with t test cluster transace Additive Regression and Transformations using ace or avas Description transace is ace packaged for easily automatically transforming all variables in a matrix transace is a fast one iteration version of transcan without imputation of NAs areg boot uses areg or avas to fit additive regression models allowing all variables in the model including the left hand side to be transformed with transformations chosen so as to opti mize certain criteria The default method uses a reg whose goal it is to maximize R method avas explicity tries to transform the response variable so as to stabilize the variance of the residuals All variables transformed models tend to inflate R 2 and it can be difficult to get confidence limits for each transformation areg boot solves both of these problems using the bootstrap As with the validate function in the Design library the Efron bootstrap is used to estimate the optimism in the apparent R and this optimism is subtracted from the apparent R to optain a bias corrected R This is done however on the transformed response variable scale Tests with
17. Not run latex x creates x tex in working directory The result of the above command is an object of class latex which here is automatically printed by the latex print method The latex print method prepends and appends latex headers and calls the latex program in the PATH If the latex program is not in the PATH you will get error messages from the operating system w lt latex x file tmp my tex Does not call the latex program as the print method was not invoked print default w Shows the contents of the w variable without attempting to latex it d lt dvi w compile LaTeX document make dvi latex assumed to be in path d or show d run xdvi assumed in path to display wW or show w run dvi then xdvi dvips d run dvips to print document dvips w run dvi then dvips library tools texi2dvi tmp my tex compile and produce pdf file in working dir End Not run latex x file just write out LaTeX code to screen Not run After running latex multiple times with different special styles in effect make a file that will call for the needed LaTeX packages when latex is run especially when using Sweave with R if exists latexStyles cat paste usepackage latexStyles sep file stylesused tex sep n Then in the latex job have something like documentclass article input stylesused begin document Se SR SR SR OSE H
18. Repeat first fit using a formula x trans lt transcan age disease blood pressure I pH imputed TRUE age lt impute x trans age predict x trans expand grid age 50 disease pneumonia transcan 307 blood pressure 60 260 pH 7 4 z lt transcan age factor disease code disease code categorical transformed TRUE trantab TRUE imputed TRUE pl FALSE plot z transformed End Not run Multiple imputation and estimation of variances and covariances of regression coefficient estimates accounting for imputation set seed 1 xl lt factor sample c a b c 100 TRU x2 lt xl b 3 xl c rnorm 100 y lt x2 1 xl c rnorm 100 x1 1 20 lt NA 2 18 23 lt NA d lt data frame x1 x2 y n lt naclus d plot n naplot n Show patterns of NAs f lt transcan y x1 x2 n impute 10 shrink FALSE data d options digits 3 summary f Gl Gl f lt transcan y x1 x2 n impute 10 shrink TRUE data d summary f h lt fit mult impute y xl x2 1m f data d Add fit reps TRUE to save all fit objects in h then do something like for i in l length hS fits print summary h fits i diag Varcov h h complete lt Im y xl x2 na action na omit h complete diag Varcov h complete Note had Design s ols function been used in place of lm any function run on h anova
19. able in the formula will have dummy variables generated for all levels instead of omitting one for the first level For combine levels x is a character category or factor vector or other vector that is converted to factor For plot and print x is an object created by varclus Forna pattern x isa list data frame or numeric matrix varclus df similarity type method data subset na action ylab legend loc maxlen labels obj 319 For plotMultSim is a numeric vector specifying the ordered unique values on the x axis corresponding to the third dimension of s a data frame an array of similarity matrices The third dimension of this array corresponds to different computations of similarities The first two dimensions come from a sin gle similarity matrix This is useful for displaying similarity matrices computed by varclus for example A use for this might be to show pairwise similari ties of variables across time in a longitudinal study see the example below If vname is not given s must have dimnames the default is to use squared Spearman correlation coefficients which will detect monotonic but nonlinear relationships You can also specify linear correlation or Hoeffding s 1948 D statistic which has the advantage of being sensitive to many types of dependence including highly non monotonic relationships For binary data or data to be made binary similarity bothpos uses as a similarit
20. font pointsize hor lwd mgp mar pch view pcx tiff name or character string or character vector specifying file prefix For setps or setpdf specify type char if this is a character vector or a quote delimited character string a character string to be processed by psfrag in LaTeX default is yellow on navy blue background black on white for type 1 3 background may also be set to any legitimate background color listed in the S supplied object ps colors rgb foreground color See background for allowable values font for text Replaces the first font in the standard list of fonts in ps options fonts If font Times Roman the fifth font normally He lvetica Bold is set to Times Bold For setpdf font is a number and the default is 1 for Helvetica All default fonts are Helvetica for setps psfig and ps slide postscript point size Set to a larger number if using multiple plots via par mf row default is TRUE to make a horizontal graph line width see par Defaults are chosen according to type margins see par see par set to TRUE to initiate a ghostview run to view the postscript file This option will also close out the postscript file this is done before viewing If you have an active ghost view window for this file already you can just type graphics off or dev off to re create the ps file ghostview will then update the image automatically ti set to TRUE to initiate conver
21. ric transformations derived by avas or ace Default is 100 ForMean areg boot evaluation is the number of points at which to evaluate exact smearing esti mates to approximate them using linear interpolation default is 200 set to TRUE to more quickly do bootstrapping without validating R vector probabilities denoting the quantiles of continuous predictors to use in estimating effects of those predictors singularity criterion list source code for the 1m fit qr bare function a vector of residuals from the transformed model Not required when statistic 1p or statistic fitted statistic to estimate with the smearing estimator For snearingEst the de fault results in computation of the sample median of the model residuals then smearingEst adds the median residual and back transforms to get estimated median responses on the original scale statistic lp causes predicted transformed responses to be computed For smearingEst the result for statistic 1p is the input argument t ransEst statistic fitted gives predicted untransformed responses i e ginverse Ip where ginverse is the inverse of the estimated response transformation estimated by reverse lin ear interpolation on the tabulated nonparametric response transformation or by using an explicit analytic function statistic quantile generalizes median to any single quantile q which must be specified mean causes the population mean response to be estimat
22. tells the function to use scat 1d to draw tick marks showing the nout smallest and nout largest values if nout gt 1 or to show all values less than the nout quantile or greater than the 1 nout quantile if 0 lt nout lt 0 5 If nout is a whole number only the first n 2 observations are shown on either side of the median where n is the total number of observations set to TRUE to invoke scat 1d to draw a data density one dimensional scatter diagram or rug plot inside each box plot a list containing named arguments without abbreviations to pass to scat1d when datadensity TRUE ornout gt 0 see panel bwplot arguments passed to points undocumented arguments to bpp1t cex points if prototype 1 else 0 5 panel bpplot 183 Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu References Esty WW Banfield J The box percentile plot J Statistical Software 8 No 17 2003 See Also bpplot panel bwplot scatld quantile Ecdf Examples set seed 13 x lt rnorm 1000 g lt sample 1 6 1000 replace TRUE x g 1 1 20 lt rnorm 20 3 contaminate 20 x s for group 1 default trellis box plot require lattice bwplot g x box percentile plot with data density rug plot bwplot g x panel panel bpplot probs seq 01 49 by 01 datadensity TRU add scatld opts list tfrac 1 to make all tick marks the
23. 142 Ecdf 12 14 27 69 180 236 322 edit 50 73 eip 73 equalBins 74 errbar 75 329 scapeBS escapeRegex 77 escapeRegex 77 event chart 78 93 event convert 88 event history 86 89 89 expand grid 50 exportDataStripped upData 309 factor 50 66 69 211 238 find matches 95 first word 99 fit mult impute 14 fit mult impute transcan 291 format 103 format default 275 format df 100 147 150 INDEX format mChoice mChoice 158 format pval 103 03 formula 275 formula summary formula cross summary formula 266 Function transcan 291 Function areg boot transace 283 Function transcan 208 gbayes 104 154 gbayeslPowerNP gbayes 104 gbayes2 gbayes 104 gbayesMixPost gbayes 104 gbayesMixPowerNP gbayes 104 gbayesMixPredNoData gbayes 104 genetic 2 11 getHdata 110 getLatestSource Misc 168 get Zip 112 Gompert z2 spower 247 grep 77 241 hclust 318 hdquantile 113 hist 115 116 236 hist data frame 50 114 236 histbackback 115 histogram 116 236 histSpike 72 histSpike scat1d 231 Hmisc Overview HmiscOverview 117 HmiscOverview 117 hoeffd 123 199 318 html 36 125 150 html contents data frame contents 35 improveProb rcorrp cens 202 impute 21 127 199 303 impute transcan 10 128 impute transcan transcan 291 inc dec 128 inc lt inc dec 128 inmChoice mChoice 158 interaction
24. Use the mouse to draw a series of points using one symbol and two smooth curves or straight lines if two points are clicked none of these being labeled d lt drawPlot Points Curve Curve plot d file tmp z send result to tmp z ps Not run Use the mouse to draw a Gaussian density two series of points using 2 symbols one Bezier curve a step function and raw data along the x axis as a 1 d scatter plot rug plot Draw a key The density function is fit to 3 mouse clicks Abline draws a dotted horizontal reference line d lt drawPlot Curve Normal type gauss Points female Points male Curve smooth ask TRUE 1lty 2 Curve step type s 1ty 3 Points type r Abline h 5 lty 2 xlab X ylab y xlim c 0 100 key TRUE plot d ylab yY plot d key FALSE label groups using labcurve End Not run label Label Attribute of an Object Description label x retrieves the label attribute of x label x lt a label stores the label attribute and also puts the class Labelled as the first class of x for S Plus 5 and later this class is not used and methods for handling this class are not defined so the label and units attributes are lost upon subsetting The reason for having this class is so that the subscripting method for labelled labelled can preserve the label attribute in R and S Plus 2000 Also the print method for labelled
25. legend draw key rm boot 221 rm boot Bootstrap Repeated Measurements Model Description For a dataset containing a time variable a scalar response variable and an optional subject iden tification variable obtains least squares estimates of the coefficients of a restricted cubic spline function or a linear regression in time after adjusting for subject effects through the use of subject dummy variables Then the fit is bootstrapped B times either by treating time and subject ID as fixed i e conditioning the analysis on them or as random variables For the former the resid uals from the original model fit are used as the basis of the bootstrap distribution For the latter samples are taken jointly from the time subject ID and response vectors to obtain unconditional distributions If a subject id variable is given the bootstrap sampling will be based on samples with replace ment from subjects rather than from individual data points In other words either none or all of a given subject s data will appear in a bootstrap sample This cluster sampling takes into account any correlation structure that might exist within subjects so that confidence limits are corrected for within subject correlation Assuming that ordinary least squares estimates which ignore the correlation structure are consistent which is almost always true and efficient which would not be true for certain correlation structures or for datasets in which
26. library chron d lt data frame a chron b chron print description for just age print description for two variables print in alphabetic order by var names keep variables 20 30 pop up window for these variables nd suppression of times when they don t vary Ls 20 ee de y 1 20 1 20 100 d 1SOdatetime year rep 2003 20 month rep 4 20 day 1 20 hour rep 11 20 min rep 17 20 sec rep 11 20 f ISOdatetime year rep 2003 20 month rep 4 20 day 1 20 g 1SOdat hour 1 20 min 1 20 sec 1 20 describe d year 2001 2020 month rep 3 20 day 1 20 68 discrete Make a function to run describe latex describe and use the kdvi previewer in Linux to view the result and easily make a pdf file ldesc lt function data options xdvicmd kdvi d lt describe data desc deparse substitute data dvi latex d file tmp z tex nomargins FALSE width 8 5 height 11 ldesc d End Not run discrete Discrete Vector tools Description discrete creates a discrete vector which is distinct from a continuous vector or a factor ordered vector The other function are tools for manipulating descrete vectors Usage as discrete x Default S3 method as discrete x discrete x levels sort unique default x na last TRUE exclude NA S3 replacement method for class discrete x lt value S
27. lt c Q1 Q2 Q3 c N sum is na x qu w lt as vector apply y 2 h names w lt as vector outer c N Q1 Q2 Q3 dimnames y 2 function x y paste y x Use na rm FALSE to count NAs separately by column s lt summary cbind age sysbp diasbp visit stratify treat na rm FALSE fun g data d The result is very wide Re do putting treatment vertically x lt with d factor paste Visit visit treat summary cbind age sysbp diasbp x na rm FALSE fun g data d Compose LaTeX code directly 280 summary formula g lt function y h lt function x qu lt format round quantile x c 25 5 75 na rm TRUE 1 nsmall 1 paste scriptsize sum is na x hfill scriptsize qu 1 textbf qu 2 scriptsize qu 3 sep apply y 2 h s lt summary cbind age sysbp diasbp visit stratify treat na rm FALSE fun g data d latex s prn FALSE need option in latex to not print n Put treatment vertically s lt summary cbind age sysbp diasbp x fun g data d na rm FALSE latex s prn FALSE Plot estimated mean life length assuming an exponential distribution separately by levels of 4 other variables Repeat the analysis by levels of a stratification variable drug Automatically break continuous variables into tertiles We are using the default method response Not run life
28. plot deltas prior deltas type 1 Now compute the power averaged over this prior gbayes2 sqrt vguess prior log 1 1 1 0 6133338 See how much power is lost by ignoring the previous study completely gbayes2 sqrt vguess function delta dnorm delta 0 100 log 1 1 1 0 4984588 What happens to the power if we really don t believe the treatment is very effective Let s use a prior distribution for the log odds ratio that is uniform between log 1 2 and log 1 3 Also check the power against a true null hypothesis prior2 lt function delta dunif delta log 1 2 log 1 3 gbayes2 sqrt vguess prior2 log 1 1 1 0 1385113 gbayes2 sqrt vguess prior2 0 1 0 3264065 Compare this with the power of a two sample binomial test to detect an odds ratio of 1 25 bpower 5 odds ratio 1 25 n1l 500 n2 300 Power 0 3307486 For the original prior consider a new study with equal sample sizes n in the two arms Solve for n to get a power of 0 9 For the variance of the log odds ratio assume a common p in the center of a range of suspected probabilities of response 0 3 For this example we use a zero null value and the uniform prior above v lt function n 2 n 3x 7 pow lt function n gbayes2 sqrt v n prior2 uniroot function n pow n 0 9 c 50 10000 root LI 2119 675 Check this value pow 2119 675 1 0 9 getHdata 113 Get the posteri
29. xtreme values hav xact positions discrete variables get a reasonable amount of jittering whether they have 2 3 5 10 20 levels different from adding noise jitter2 will use the available space optimally and no value will randomly mask another If you want a scatterplot with lowess smooths on the exact values and the point clouds shown jittered you just need pairs dfram panel function x y points jitter2 x jitter2 y lines lowess x y datadensity dfram graphical snapshot of entire data frame datadensity dfram group cut2 dfram x2 g 3 Stratify points and frequencies by x2 tertiles and use 3 colors score binary 241 datadensity data frame split x grouping variable need to explicitly invoke datadensity data frame when the first argument is a list score binary Score a Series of Binary Variables Description Creates a new variable from a series of logical conditions The new variable can be a hierarchical category or score derived from considering the rightmost TRUE value among the input variables an additive point score a union or any of several others by specifying a function using the fun argument Usage score binary fun max points 1 p na rm funtext max retfactor TRUE Arguments a list of variables or expressions which are considered to be binary or logical fun a function to compute on each row of the matrix repres
30. 29 inverseFunction 6 inverseFunction Misc 168 invertTabulated transcan 291 INDEX is discrete discrete 65 is imputed impute 127 is mChoice mChoice 158 is na lt discrete discrete 65 james stein Misc 168 jitter 236 jitter2 scatid 231 km quick Misc 168 labcurve 72 129 183 186 250 329 Label label 138 label 50 72 138 161 186 223 229 255 263 275 289 309 312 329 Label data frame label 138 label data frame label 138 label default label 138 label lt label 138 labelPlotmath label 138 Lag 141 lag 142 lapply 63 158 largest empty labcurve 129 latex 29 63 102 126 142 208 275 latex bystats bystats 27 latex bystats2 bystats 27 latex describe describe 60 latex summary formula cross summary formula 266 latex summary formula response summary formula 266 latex summary formula reverse summary formula 266 latexSN latex 142 latexTabular latex 142 latexTranslate 275 latexTranslate latex 142 latexVerbatim latex 142 ldBands 151 legend 136 217 length lt discrete discrete 65 library 212 list tree 155 llist 263 275 llist label 138 lm 3 223 lm fit qr bare Misc 168 Load Save 230 lo lo lo Lo lo ake lr ls ma 343 ad 711 231 cator 318 ess smooth 322 gnorm2 spower 247 grank 166 grank spower 247 lookup xport 229 lo
31. Author s Frank E Harrell Jr See Also hist scatld Examples d lt data frame a runif 200 b rnorm 200 w factor sample c green red blue 200 TRUE hist data frame d in R just say hist d histbackback Back to Back Histograms Description Takes two vectors or a list with x and y components and produces back to back histograms of the two datasets Usage histbackback x y brks NULL xlab NULL axes TRUE probability FALS xlim NULL ylab Gl histbackback 119 Arguments X Y either two vectors or a list given as x with two components If the components have names they will be used to label the axis modification FEH brks vector of the desired breakpoints for the histograms xlab a vector of two character strings naming the two datasets axes logical flag stating whether or not to label the axes probability logical flag if TRUE then the x axis corresponds to the units for a density If FALSE then the units are counts xlim x axis limits First value must be negative as the left histogram is placed at negative x values Second value must be positive for the right histogram To make the limits symmetric use e g ylim c 20 20 ylab label for y axis Default is no label additional graphics parameters may be given Value a list is returned invisibly with the following components left the counts for the dataset plotted on the left right the counts for the dataset p
32. End Not run 130 impute impute Generic Functions and Methods for Imputation Description These functions do simple and t ranscan imputation and print summarize and subscript variables that have NAs filled in with imputed values The simple imputation method involves filling in NAs with constants with a specified single valued function of the non NAs or from a sample with replacement from the non NA values this is useful in multiple imputation More complex imputations can be done with the t ranscan function which also works with the generic methods shown here i e impute can take a transcan object and use the imputed values created by transcan with imputed TRUE to fill in NAs The print method places after variable values that were imputed The summary method summarizes all imputed values and then uses the next summary method available for the variable The subscript method preserves attributes of the variable and subsets the list of imputed values corresponding with how the variable was subsetted The is imputed function is for checking if observations are imputed Usage impute x Default S3 method impute x fun median S3 method for class impute print x x S3 method for class impute summary object is imputed x Arguments x fun a vector or an object created by t ranscan or a vector needing basic uncondi tional imputation If there are no N
33. NA rm logical flag If TRUE subset observations which have NA for each variable spec ified in subset c will not have an entry on the y axis Also if the following argu ment x reference is specified observations with missing x reference values will also not have an entry on the y axis If FALSE default user can identify those observations which do have NA for every variable specified in subset c or if x reference is specified also those observations which are missing only the x reference value this can easily be done by exam ining the resulting y axis and recognizing the observations without any plotting symbols x reference column of original matrix or data frame with which to reference the x axis That is if specified all columns specified in subset c will be substracted by x reference An example may be to see the timing of events before and after treatment or to see time to event after entry into study The event times will be aligned using the x reference argument as the reference point now the now date which will be used for top of y axis when creating the Goldman eventchart see reference below Defaultismax data subset c na rm TRUE now line logical flag A feature utilized by the Goldman Eventchart When x reference is specified as the start of follow up and y var x reference then the Goldman chart can be created This argument if TRUE will cause the plot region to be square and will dr
34. Specify ytype inverse to instead create the inverse of the transforma tion to be able to obtain originally scaled y values prefix character string defining the prefix for function names created when t ype individual By default the function specifying the transformation for variable x will be named x suffix character string defining the suffix for the function names frame frame number in which to store functions see assign The default is frame 0 the session database which disappears at the end of the S Plus session where location in which to store functions see assign If where is specified e g where 1 to store functions in search position one frame is ignored For R the value of where is passed to assign as the pos argument arguments passed to other functions transace 291 Details As transace only does one iteration over the predictors it may not find optimal transformations and it will be dependent on the order of the predictors in x ace and avas standardize transformed variables to have mean zero and variance one for each boot strap sample so if a predictor is not important it will still consistently have a positive regression coefficient Therefore using the bootstrap to estimate standard errors of the additive least squares regression coefficients would not help in drawing inferences about the importance of the predictors To do this summary areg boot computes estimates of e g the inter quartile ran
35. additional column is specified for the response variable that column is taken as the half width of a precision interval for y and the lower and upper values are computed automatically as y plus or minus the value of the additional column variable When a groups variable is present panel xYplot will create a function in frame 0 GLobalEnv in R called Key that when invoked will draw a key describing the groups labels point symbols and colors By default the key is outside the graph For S Plus if Key locator 1 is spec ified the key will appear so that its upper left corner is at the coordinates of the mouse click For R Lattice the first two arguments of Key x and y are fractions of the page measured from the lower left corner and the default placement is at x 0 05 y 0 95 For R an optional argument to sKey other may contain a list of arguments to pass to draw key see xyplot for a list of possible arguments under the key option When method quantile is specified xYplot automatically groups the x variable into in tervals containing a target of nx observations each and within each x group computes three quan tiles of y and plots these as three lines The mean x within each x group is taken as the x coordinate This will make a useful empirical display for large datasets in which scatterdiagrams are too busy to see patterns of central tendency and variability You can also specify a general function of a data vector that
36. age survival time lt 1 sex lt TRUE 500 TRUE fun qlogis group sex plot logit by sex Plot points and smooth trend line using trellis add type 1 to suppress points or type p to suppress trend lines require lattice xyplot survival time age panel panel plsmo Do this for multiple panels xyplot survival time age sex panel panel plsmo Do this for subgroups of points on each panel show the data dens xyplo Key ity on each curve and draw a key at the default location t survival time age datadensity TRUE sex groups race panel panel plsmo 190 popower Use wloess noiter to do a fast weighted smooth plot x y lines wtd loess noiter x y lines wtd loess noiter x y weights c rep 1 50 100 rep 1 49 col 2 points 51 y 51 pch 18 show overly weighted point Try to duplicate this smooth by replicating 5lst observation 100 times lines wtd loess noiter c x rep x 51 99 c y rep y 51 99 type ordered all col 3 Note These two don t agree exactly popower Power and Sample Size for Ordinal Response Description popower computes the power for a two tailed two sample comparison of ordinal outcomes under the proportional odds ordinal logistic model The power is the same as that of the Wilcoxon test but with ties handled properly posamsize computes the total sample size needed to achieve a given power
37. automatically removed mult for smean cl normal is the multiplier of the standard error of the mean to use in obtaining confidence limits of the population mean default is appropriate quantile of the t distribution For smean sd1 mult is the multiplier of the standard deviation used in obtaining a coverage interval about the sample mean The default is mu1t 2 to use plus or minus 2 standard deviations conf int for smean cl normal and smean cl boot specifies the confidence level 0 1 for interval estimation of the population mean For smedian hilow conf int is the coverage probability the outer quantiles should target When the default 0 95 is used the lower and upper quantiles computed are 0 025 and 0 975 B number of bootstrap resamples for snean cl boot reps set to TRUE to have smean cl boot return the vector of bootstrapped means as the reps attribute of the returned object Gl 248 solvet Value a vector of summary statistics Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also summarize summary formula Examples set seed 1 x lt rnorm 100 smean sd x smean sdl x smean cl normal x smean cl boot x smedian hilow x conf int 5 25th and 75th percentiles Function to compute 0 95 confidence interval for the difference in two means g is grouping variable bootdif lt function y g g lt as factor g a lt attr
38. b abline v 2 Il1ty 2 default value of fweighted abline h rmse lty 2 root MSE of residuals from linear regression Not run Do a similar experiment for the Titanic dataset getHdata titanic3 h lt Im age sex pclass survived data titanic3 rmse lt summary h sigma set seed 21 f lt aregImpute age sex pclass survived n impute 10 data titanic3 match closest sd lt sqrt mean apply f imputed age 1 var ss lt c 0 01 02 seq 05 1 length 20 sds lt ss sds 1 lt sd for i in 2 length ss f lt aregImpute age sex pclass survived data titanic3 n impute 10 fweighted ss i sds i lt sqrt mean apply f imputed age 1 var plot ss sds xlab Smoothing Parameter ylab SD of Imputed Values type b abline v 2 lty 2 default value of fweighted abline h rmse lty 2 root MSE of residuals from linear regression End Not run binconf Confidence Intervals for Binomial Probabilities Description Produces alpha confidence intervals for binomial probabilities Usage binconf x n alpha 0 05 method c wilson exact asymptotic all include x FALSE include n FALSE return df FALS Gl lt Arguments x vector containing the number of successes for binomial variates n vector containing the numbers of corresponding observations alpha probability of a type I error so confidence c
39. corresponding to group Default is current par col vector of line widths for the curves corresponding to group Default is current par lwd 1wd can also be specified as an element of label curves if label curves is a list iter parameter if method lowess default 0 if y is binary and 3 other wise bass parameter if method supsmu default 0 passed to the lowess function for met hod lowess only plots smoothed estimates between trim and 1 trim quantiles of x Default is to use 10th smallest to 10th largest x in the group if the number of observations in the group exceeds 200 0 otherwise Specify trim 0 to plot over entire range after computing the smoothed estimates if fun is given the y values are trans formed by fun a variable either a factor vector or one that will be converted to factor by plsmo that is used to stratify the data so that separate smooths may be computed a character string to appear in group of group labels The presence of prefix ensures that Labcurve will be called even when add TRUE a vector of 2 x axis limits Default is observed range a vector of 2 y axis limits Default is observed range set to FALSE to prevent labcurve from being called to label multiple curves corresponding to groups Set to a list to pass options to labcurve lty and col are passed to Labcurve automatically set to TRUE to draw tick marks on each curve using x coordinates of the raw data x value
40. dateformat SF fixdates c none year comment char autodates TRU allow NULL charfactor FALSE sep skip 0 vnames NULL labels NULL Gl 44 csv get Arguments file the file name for import lowernames set this to TRUE to change variable names to lower case datevars character vector of names after lowernames is applied of variables to con sider as a factor or character vector containing dates in a format matching dateformat The default is SF which uses the yyyy mm dd format datetimevars character vector of names after lowernames is applied of variables to con sider to be date time variables with date formats as described under datevars followed by a space followed by time in hh mm ss format chron is used to store such variables If all times in the variable are 00 00 00 the variable will be converted to an ordinary date variable dateformat for cleanup import is the input format see st rpt ime fixdates for any of the variables listed in datevars that have a dateformat that cleanup import understands specifying fixdates allows corrections of certain formatting inconsistencies before the fields are attempted to be con verted to dates the default is to assume that the dateformat is followed for all observation for datevars Currently fixdates year is imple mented which will cause 2 digit or 4 digit years to be shifted to the alternate number of digits when dateform is the de
41. last source to this file name so that src can be issued to re source the file when it is edited Usage src x stata get 257 Arguments x an unquoted file name aside from s This base file name must be a legal S name Side Effects Sets system option last source Author s Frank Harrell See Also Source Examples Not run src myfile source myfile s src re source myfile s End Not run stata get Enhanced Importing of STATA Files Description Reads a file in Stata version 5 8 or 7 SE binary format into a data frame Usage stata get file lowernames FALSE convert dates TRUE convert factors TRUE missing type FALSE convert underscore TRUE warn missing labels TRUE force single TRUE allow NULL charfactor FALS Gl pe x Arguments file input SPSS save file May be a file on the WWW indicated by file starting with http lowernames set to TRUE to convert variable names to lower case convert dates see read dta convert factors see read dta missing type see read dta 258 stata get convert underscore see read dta warn missing labels see read dta force single set to FALSE to prevent integer valued variables from being converted from storage mode double to integer allow a vector of characters allowed by R that should not be converted to periods in variable names By default underscores in varia
42. lowernames FALSE moveUnits FALSE charfactor FALS exportDataStripped data dataframeReduce data fracmiss 1 maxlevels NULL minprev 0 pr TRUI Gl Arguments obj a data frame or list object a data frame or list data a data frame force single By default double precision variables are converted to single precision in S Plus only unless force single FALSE force single TRUE will also convert vectors having only integer values to have a storage mode of integer in R or S Plus force numeric Sometimes importing will cause a numeric variable to be changed to a factor vector By default cleanup import will check each factor variable to see if the levels contain only numeric values and In that case the variable will be converted to numeric with converted to NA Set force numeric FALSE to prevent this behavior rmnames set to F to not have cleanup import remove names or Names attributes from variables labels a character vector the same length as the number of variables in obj These character values are taken to be variable labels in the same order of variables in obj For upData labels is a named list or named vector with variables in no specific order lowernames set this to TRUE to change variable names to lower case upData does this before applying any other changes so variable names given inside arguments to upData need to be lower case if lowern
43. ora label attribute may be pre attached to the first ar gument In either case the name or label of the first argument is stored as an attribute label of the object returned by Cbind Storing other vectors as a matrix attribute facilitates plotting error bars etc as trellis really wants the x and y variables to be vectors not matrices If a single argument is given to Cbind and that argument is a matrix with column dimnames the first column is taken as the main vector and remaining columns are taken as other A subscript method for Cbind objects subscripts the ot her matrix along with the main y vector The xYplot function is a substitute for xyplot that allows for simulated multi column y It uses by default the panel xYplot and prepanel xYplot functions to do the actual work The method argument passed to panel xYplot from xYplot allows you to make error bars the upper only or lower only portions of error bars alternating lower only and upper only bars bands or filled bands panel xYplot decides how to alternate upper and lower bars according to whether the median y value of the current main data line is above the median y for all groups of lines or not If the median is above the overall median only the upper bar is drawn For bands but not filled bands any number of other columns of y will be drawn as lines having the same thickness color and type as the main data line If plotting bars bands or filled bands and only one
44. set to FALSE to suppress printing counts of missing values for cross number of digits to the right of the decimal place for printing percentages The default is zero so percents will be rounded to the nearest percent specifies which counts are to be printed to the right of percentages The default is to print the frequency numerator of the percent in parentheses You can specify both to print both numerator and denominator denominator or none the size for typesetting npct information which appears after percents The default is Scriptsize When a second row of column headings is added showing sample sizes Nsize specifies the LaTeX size for these subheadings Default is Scriptsize by default method reverse objects will be printed plotted or typeset by removing redundant entries from percentage tables for categorical variables For example if you print the percent of females you don t need to print the percent of males To override this set exclude1 FALSE set to FALSE to suppress printing or latexing units attributes of variables when method reverse or response character to use to separate quantiles when printing method reverse ta bles a vector of test statistic components to print if test TRUE was in effect when summary formula was called Defaults to printing all components Specify prtest FALSE or prtest none to not print any tests This applies to print latex and plot
45. test TRUE f trio of numbers represent 25th 50th 75th percentile print f long TRUE plot f plot f conType bp prtest P summary formula 279 bpplt annotated example showing layout of bp plot Compute predicted probability from a logistic regression model For different stratifications compute receiver operating characteristic curve areas C indexes predicted lt plogis 4 sex m 15 age 50 positive diagnosis lt ifelse runif 500 lt predicted 1 0 roc s function z Mm Seen 1 13 y lt 21 2 n lt length x if n lt 2 return c ROC NA nl lt sum y 1 c ROC mean rank x y 1 n1 1 2 n n1 y lt cbhind predicted positive diagnosis options digits 2 summary y age sex fun roc options digits 3 summary y age sex fun roc method cross Use stratify to produce a table in which time intervals go down the page and going across 3 continuous variables are summarized using quartiles and are stratified by two treatments set seed 1 d lt expand grid visit 1 5 treat c A B reps 1 100 dSsysbp lt rnorm 100 5 2 120 10 label dSsysbp lt Systolic BP dS diasbp lt rnorm 100 5 2 80 RY d diasbp 1 lt NA dSage lt rnorm 100 5 2 50 12 g lt function y N lt apply y 2 function w sum is na w h lt function x qu lt quantile x c 25 5 75 na rm TRUE names qu
46. varclus dotchart2 impute chisq test cut2 Examples x lt c 2 1 0 1 2 y lt c 4 Ip 0 dy 4 Z lt els 2 3 4 NA v lt c 1 2 3 4 5 spearman2 x y plot spearman2 z x y v p 2 f lt chiSquare z x y v f bootkm 25 bootkm Bootstrap Kaplan Meier Estimates Description Bootstraps Kaplan Meier estimate of the probability of survival to at least a fixed time times variable or the estimate of the q quantile of the survival distribution e g median survival time the default Usage bootkm S q 0 5 B 500 times pr TRUI Fl Arguments S a Surv object for possibly right censored survival time q quantile of survival time default is 0 5 for median B number of bootstrap repetitions default 500 times time vector currently only a scalar is allowed at which to compute survival estimates You may specify only one of q and t imes and if t imes is specified q is ignored pr set to FALSE to suppress printing the iteration number every 10 iterations Details bootkm uses Therneau s survfit km function to efficiently compute Kaplan Meier estimates Value a vector containing B bootstrap estimates Side Effects updates Random seed and if pr TRUE prints progress of simulations Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu References Akritas MG 1986 Bootstrapping the
47. ylim c 2 5 4 Put plotting symbols at equally spaced points with a key for the symbols ignoring line types labcurve curves keys l1 m lty 1 type type col 1 m pl TRUE ylim c 2 5 4 Plot and label two curves with line parameters specified with data set seed 191 ages f lt sort rnorm 50 20 7 ages m lt sort rnorm 40 19 7 height f lt pmin ages f 21 2 60 height m lt pmin ages m 21 16 63 labcurve list Female list ages f height f col 2 Male list ages m height m col 3 lty dashed xlab Age ylab Height p1l TRUE add keys c f m to label curves with single letters For S Plus use lty 2 Plot power for testing two proportions vs n for various odds ratios using 0 1 as the probability of the event in the control group A separate curve is plotted for each odds ratio and the curves are labeled at points of maximum separation n lt seq 10 1000 by 10 OR lt seq 2 9 by 1 pow lt lapply OR function or n list x n y bpower pl 1 odds ratio or n n n n names pow lt format OR labcurve pow pl TRUE xlab n ylab Power Plot some random data and find the largest empty rectangl that is at least 1 wide and 1 tall x lt runif 50 y lt runif 50 plot x y z lt largest empty x y 1 1 Zz points z pch 3 mark center of rectangle or key zSx zSy stuff for legend label 141
48. 1 point inc NULL opts NULL key opts NULL empty method c area maxdim numbins 25 pl missing add add FALSE ylim NULL xlab ylab whichLabel 1 length curves grid FALSE xrestrict NULL putKey z labels type pch lty lwd cex par cex col rep par col nc transparent TRUE plot TRUE key opts NULL grid FALSE 134 labcurve putKeyEmpty x y labels type NULL pch NULL lty NULL lwd NULL cex par cex col rep par col nc transparent TRUE plot TRUE key opts NULL empty method c area maxdim numbins 25 xlim prSusr 1 2 ylim prSusr 3 4 grid FALS Gl drawPlot xlim c 0 1 ylim c 0 1 xlab ylab ticks c none x y xy key FALSE opts NULL Points label type c p r n pch pch to use 1 cex par cex rug c none x y xy ymean Curve label type c bezier polygon linear pol step gauss n NULL lty 1 lwd par lwd degree 2 evaluation 100 ask FALSE Abline S3 method for class drawPlot plot x file xlab ylab ticks key xSkey keyloc xSkeyloc largest empty x y width height numbins 25 method c area maxdim xlim prSusr 1 2 ylim prSusr 3 4 pl FALSE grid FALSE Arguments curves a list of lists each of which have at least two components a vector of x values and a vector of corr
49. 257 nese lt L57 m rt lt 18 red lt seq 10 25 by 25 samsiz lt red i lt 0 for r in red i lt i 1 samsiz i lt uniroot function x cpower 5 x mort r 1 5 5 nce c neri pr FALS c 1000 40000 Sroot ti foe samsiz lt samsiz 1000 par mfrow c 1 1 plot red samsiz xlab Reduction in 5 Year Mortality ylab Total Sample Size Thousands type n lines red samsiz lwd 2 title Sample Size for Power 0 80 nDrop in 15 Non adherence 25 title sub alpha 0 05 2 tailed adj 0 Cs Character strings from unquoted names csv get 43 Description Makes a vector of character strings from a list of valid S names Usage CS Cea Arguments any number of names separated by commas Value character string vector See Also sys frame deparse Examples Cs a cat dog subset data frame lt dataframe Cs age sex race bloodpressure height csv get Read Comma Separated Text Data Files Description Read comma separated text data files allowing optional translation to lower case for variable names after making them valid S names There is a facility for reading long variable labels as one of the rows If labels are not specified and a final variable name is not the same as that in the header the original variable name is saved as a variable label Usage csv get file lowernames FALSE datevars NULL datetimevars NULL
50. 3 predictors show that the avas and ace estimates are unstable unless the sample size exceeds 350 Apparent R with low sample sizes can be very inflated and bootstrap estimates of R can be even more unstable in such cases resulting in optimism corrected R that are much lower even than the actual R The situation can be improved a little by restricting predictor transfor mations to be monotonic On the other hand the areg approach allows one to control overfitting by specifying the number of knots to use for each continuous variable in a restricted cubic spline function For method avas the response transformation is restricted to be monotonic You can spec ify restrictions for transformations of predictors and linearity for the response When the first argument is a formula the function automatically determines which variables are categorical i e factor category or character vectors Specify linear transformations by enclosing vari ables by the identify function I and specify monotonicity by using monotone variable Monotonicity restrictions are not allowed with method areg The summary method for areg boot computes bootstrap estimates of standard errors of dif ferences in predicted responses usually on the original scale for selected levels of each predictor transace 287 against the lowest level of the predictor The smearing estimator see below can be used here to estimate differences in predicted means me
51. 45 detach 2 End Not run sys Run Unix or Dos Depending on System Description Runs unix or dos depending on the current operating system For R just runs system with optional concatenation of first two arguments which are assumed named command and text Usage sys command text NULL output TRUE S Plus sys minimized FALSE Arguments command system command to execute text text to concatenate to system command if any typically options or file names or both output set to FALSE to not return output of command as a character vector Value see unix or dos t test cluster 285 Side Effects executes system commands See Also unix system t test cluster t test for Clustered Data Description Does a 2 sample t test for clustered data Usage t test cluster y cluster group conf int 0 95 S3 method for class t test cluster PLINT S digits rsa Arguments y normally distributed response variable to test cluster cluster identifiers e g subject ID group grouping variable with two values conf int confidence coefficient to use for confidence limits x an object created by t test cluster digits number of significant digits to print unused Value a matrix of statistics of class t test cluster Author s Frank Harrell References Donner A Birkett N Buck C Am J Epi 114 906 914 1981 Donner A Klar N J Clin Epi 49 435 439 1996 Hsieh FY Stat in Med 8 1195 1201 1988
52. 5 s lt x matplot x s profile s type 1 col col id lt rep 1 100 each m X lt Y lt id cols lt character 100 names cols lt as character 1 100 for i an 1sIr00 f s lt id i XFsT lt 57 j lt sample 1 4 1 Y s lt profile j cols i lt col j table cols yl lt c 1 4 w lt curveRep X Y id kn 1 kxdist 1 k 4 plot w 1 lattice idcol cols ylim yl Found 4 clusters but two have same profile w lt curveRep X Y id kn 1 kxdist 1 k 3 plot w 1 lattice idcol cols freq cols plotfreq TRUE ylim y1 Incorrectly combined black and red because default value p 5 did not result in different profiles at x xeval w lt curveRep X Y id kn 1 kxdist 1 k 4 p 40 plot w 1 lattice idcol cols ylim yl Found correct clusters becaus valuated curves at 40 equally spaced points and could find the sharp triangular peak in profile 4 End Not run cut2 Cut a Numeric Variable into Intervals Description Function like cut but left endpoints are inclusive and labels are of the form lower upper except that last interval is lower upper If cuts are given will by default make sure that cuts include entire range of x Also if cuts are not given will cut x into quantile groups g given or groups with a given minimum number of observations m Whereas cut creates a category object cut 2 creates a factor object cut2 51 Usage cut2 x cuts m g
53. 50 40 30 20 10 7 4 multi col e 15 g 2075y 2 Tels rA LOrE 295 14 subtitles TRUE Arguments time numeric time vector y continuous numeric response vector of length the same as t ime Subjects hav ing multiple measurements have the measurements strung out x an object returned from rm boot id subject ID variable If omitted it is assumed that each time response pair is measured on a different subject subset subset of observations to process if not all the data plot individual set to TRUE to plot nonparametrically smoothed time response curves for each subject bootstrap type specifies whether to treat the time and subject ID variables as fixed or random nk number of knots in the restricted cubic spline function fit The number of knots may be 0 denoting linear regression or an integer greater than 2 in which k knots results in k 1 regression coefficients excluding the intercept The default is 6 knots knots vector of knot locations May be specified if nk is omitted B number of bootstrap repetitions Default is 500 rm boot 223 smoother a smoothing function that is used if plot individual TRUE Default is supsmu xlab label for x axis Default is units attribute of the original t ime variable or Time if no such attribute was defined using the units function xlim specifies x axis plotting limits Default is to use range of times specified to rm boot ylim for rm boot this is a vector of y ax
54. Both functions compute the efficiency of the design compared with a design in which the response variable is continuous print methods exist for both functions Any of the input arguments may be vectors in which case a vector of powers or sample sizes is returned These functions use the methods of Whitehead 1993 Usage popower p odds ratio n nl n2 alpha 0 05 S3 method for class popower DIN Sp eee posamsize p odds ratio fraction 5 alpha 0 05 power 0 8 S3 method for class posamsize print x Arguments p a vector of marginal cell probabilities which must add up to one The ith ele ment specifies the probability that a patient will be in response level i averaged over the two treatment groups odds ratio the odds ratio to be able to detect It doesn t matter which group is in the nu merator n total sample size for popower You must specify either n or n1 and n2 If you specify n n1 and n2 are set ton 2 nl for popower the number of subjects in treatment group 1 n2 for popower the number of subjects in group 2 alpha type I error x an object created by popower or posamsize fraction for posamsize the fraction of subjects that will be allocated to group 1 power for posamsize the desired power default is 0 8 unused print char list Value 191 a list containing power and eff relative efficiency for popower or containing n and eff for posamsize Author s Frank Harre
55. FAL table env TRUE here FALSE lines page 40 E draft longtable TRUE ctable FALSE booktabs FALSI Fl latex caption NULL caption lot NULL double slash FALSE 147 caption loc c top bottom vbar FALSE collabel just rep c nc na blank TRUE insert bottom NULL first hline double booktabs ctable where tbp size NULL center c center centering none landscape FALSE multicol TRUE math row names FALSE math col names FALSE x is a matrix or data frame S3 method for class function latex object title first word deparse substitute object file paste title tex sep append FALSE assignment TRUE type c example verbatim S3 method for class list latex object title first word deparse substitute object file paste title tex sep append FALSE label caption caption lot caption loc c top bottom S3 method for class latex print x latexTranslate object inn NULL out NULL pb FALSE greek FALSE latexSN x latexTabular x headings colnames x align paste rep c ncol x collapse halign paste rep c ncol x collapse helvetica TRUE latexVerbatim x title first word deparse substitute x fil past titl yo Mee
56. Kaplan Meier estimator JASA 81 1032 1038 26 bpower See Also Survfit survfit km Surv Survival cph Quantile cph Examples Compute 0 95 nonparametric confidence interval for the difference in median survival time between females and males two sample problem set seed 1 library survival S lt Surv runif 200 no censoring sex lt c rep female 100 rep male 100 med female lt bootkm S sex female B 100 normally B 500 med male lt bootkm S sex male B 100 describe med female med male quantile med female med male c 025 975 na rm TRUE na rm needed because some bootstrap estimates of median survival time may be missing when a bootstrap sample did not include the longer survival times bpower Power and Sample Size for Two Sample Binomial Test Description Uses method of Fleiss Tytun and Ury but without the continuity correction to estimate the power or the sample size to achieve a given power of a two sided test for the difference in two propor tions The two sample sizes are allowed to be unequal but for bsamsize you must specify the fraction of observations in group 1 For power calculations one probability p1 must be given and either the other probability p2 an odds ratio ora percent reduction must be given For bpower or bsamsize any or all of the arguments may be vectors in which case they return a vector of powers or s
57. Plot pointwise and simult plot f pointwise band TRUE Plot population response curve at average subject effect ts lt seq 0 1 length 100 lines ts g ts mean sub lwd 3 Not run Handle a 2 sample problem in which curves are fitted separately for males and females and we wish to estimate the difference in the time response curves for the two sexes 228 rMultinom The objective criterion will be taken by plot rm boot as the total of the two sums of squared errors for the two models Al knots lt rcespline eval c time f time m nk 6 knots only TRU Use same knots for both sexes and use a times vector that uses a range of times that is included in the measurement times for both sexes tm lt seq max min time f min time m min max time f max time m length 100 time f bp f id f knots knots times tm time m bp m id m knots knots times tm f female lt rm boo f male lt rm boo plot f female plot f male The following plots female minus male response with a sequence of shaded confidence band for the difference plot f female f male multi TRUE Do 1000 simulated analyses to check simultaneous coverage probability Use a null regression model with Gaussian errors n per pt lt 30 n pt lt 10 null in region lt 0 for i in 1 1000 y lt rnorm n pt n per pt time lt rep l n per pt n pt Add the following line and a
58. Slides and Other Formats Description The ps slide function has nice defaults to create postscript images with larger font thicker lines and better axis labeling These images can be used to make nice slides There is an option to view the constructed postscript file using ghost view and an option to initiate a background process to convert the postscript file to a PC Paintbrush pcx file for importing into various PC presentation graphics packages although with a significant loss in resolution This option assumes you have installed various public domain unix image conversion programs You can preview pcx files using e g xli file pcx amp Specify type 1 to make nice fullsize graphs or t yoe 3 for making 5 x 7 landscape graphs using 14 point type useful for submitting to journals type 2 the default is for color 35mm slides Use t ype 4 to make nice black and white overhead projection transparancies portrait mode This uses line thickness 4 pointsize 14 height 8 width 7 For type 3 numbers on the y axis are written horizontally las defaults to 1 for type 3 ps slide calls mgp axis labels in Hmisc set up axis specific defaults for the 2nd mgp graphical parameter See Overview for Hmisc for help This is only used automatically for select high level graphics functions in Hmisc and Design as S Plus only supports a single distance between tick marks and tick mark labels using par and when 1as 1 a larger distance is needed for th
59. The default NULL value of out results in a data frame or a list of data frames being returned When out is given sasxport get returns only metadata see below invisibly out only works with methods csv out should not have a trailing slash a vector of names of SAS datasets to process original SAS upper case names Must include PROC FORMAT dataset if it exists and if the kept datasets use any of its value label formats a vector of names of SAS datasets to ignore original SAS upper case names SAS character variables are converted to S factor objects if as is FALSE or if as is is a number between 0 and 1 inclusive and the number of unique values of the variable is less than the number of observations n times as is The default if as is is 5 so character variables are converted to factors only if they have fewer than n 2 unique values The primary purpose of this is to keep 232 sasxport get unique identification variables as character values in the data frame instead of using more space to store both the integer factor codes and the factor labels FUN an optional function that will be run on each data frame created when met hod csv and out are specified The result of all the FUN calls is made into a list corre sponding to the SAS datasets that are read This list is the FUN attribute of the result returned by sasxport get Details See contents 1list for a way to print the directory of SAS datasets when mor
60. The default is auto for which the largest empty function to used to find the most empty part of the plot If no empty rectangle large enough to hold the key is found no key will be drawn Specify keyloc none to suppress drawing a legend or set keyloc to a 2 element list containing the x and y coordinates for the center of the legend For example use keyloc locator 1 to click the mouse at the center keyloc specifies the coordinates of the center of the key to be drawn with plot drawPlot when key TRUE for Labcurve ascalar or vector of character strings specifying the method that the points in the curves were connected 1 means ordinary connections be tween points and s means step functions For put Key and putKeyEmpty is a vector of plotting types 1 for regular line p for point b for both point and line and n for none For Points is either p the default for regular points or r for rugplot one dimensional scatter diagram to be drawn using the scat1d function For Curve type is bezier the default 136 labcurve for drawing a smooth Bezier curves which can represent a non 1 to 1 function such as a circle polygon for orginary line segments linear for a straight line defined by two endpoints pol for a degree degree polyno mial to be fitted to the mouse clicked points step for a left step function gauss to plot a Gaussian density fitted to 3 clicked points or a function to draw a user
61. UNIX tr command to translate any character in old in text to the corresponding charac ter in new If multichar T or old and new have more than one element or each have one element but they have different numbers of characters uses the UNIX sed command to translate the se ries of characters in old to the series in new when these characters occur in text If old or new contain a backslash you sometimes have to quadruple it to make the UNIX command work If they contain a forward slash preceed it by two backslashes The Microsoft Windows version of translate invokes the sedit function and does not allow multichar FALSE i e it does not support the UNIX tr function The R version of translate invokes the builtin chartr function if mult ichar FALSE Usage translate text old new multichar FALSE Arguments text scalar vector or matrix of character strings to translate old vector old characters new corresponding vector of new characters multichar See above Details At present mult ichar FALSE which requires the UNIX t r program is not implemented under MS Windows Value an object like text but with characters translated 310 trunc POSIXt See Also unix grep Examples translate c ABC DEF ABCDEFG abcdefg translate 23 12 cdot change to cdot translate c dog cat tiger c dog cat c DOG CAT S Plus gives 1 DOG CATT tiger check disc
62. US pH append FALSE size NULL width Options width hspace NULL length OptionsSlength 148 latex dvi object S3 method for class latex dvi object prlog FALSE nomargins TRUE width 5 5 height 7 S3 method for class dvi print x dvips object S3 method for class latex dvips object S3 method for class dvi dvips object file S3 method for class latex show object or show dvi object or just object dvigv object S3 method for class latex dvigv object or gvdvi dvi object S3 method for class dvi dvigv object Arguments object For latex any S object For dvi or dvigv an object created by latex For latexTranslate is a vector of character strings to translate x any object to be printed verbatim for latexVerbatim For latexSN x is a numeric vector For latexTabular x is a matrix or data frame title name of file to create without the tex extension If this option is not set value string of x see above is printed in the top left corner of the table Set title to suppress this output file name of the file to create The default file name is x tex where x is the first word in the name of the argument for x Set file to have the generated LaTeX code just printed to standard output This is especially useful when run ning under Sweave in R using its result s tex tag to save hav
63. Vector are Numeric Description Tests without issuing warnings whether all elements of a character vector are legal numeric values or optionally converts the vector to a numeric vector Leading and trailing blanks in x are ignored Usage all is numeric x what c test vector extras c NA Arguments x a character vector what specify what vector to return a numeric vector if it passes the test or the original character vector otherwise extras a vector of character strings to count as numeric values other than Value a logical value if what t est or a vector otherwise Author s Frank Harrell See Also as numeric approxExtrap Examples all is numerie e i 1 2 3 all is numeric c 1 1 2 3a all is numeric c 1 1 2 3 vector all is numeric c 1 1 2 3a vector all is numeric c 1 vector approxExtrap Linear Extrapolation Description Works in conjunction with the approx function to do linear extrapolation approx in R does not support extrapolation at all and it is buggy in S Plus 6 Usage approxExtrap x Arguments x y xout method n rule E ties na rm Details Duplicates in x and corresponding y elements are removed before using approx Value Yr xout method linear n 50 rule see approx applies only to R See approx set to TRU E to remove NAs in x and y before proceeding a ve
64. allcat FALSE minfreq 0 iterms FALSE pc FALSE pr FALSI S3 method for class redun Gl print x digits 3 long TRUE Arguments formula a formula Enclose a variable in I to force linearity data a data frame subset usual subsetting expression r2 ordinary or adjusted R cutoff for redundancy type specify adjusted to use adjusted R nk number of knots to use for continuous variables Use nk 0 to force linearity for all variables tlinear set to FALSE to allow a variable to be automatically nonlinearly transformed see areg while being predicted By default only continuous variables on the right hand side i e while they are being predictors are automatically trans formed using regression splines Estimating transformations for target depen dent variables causes more overfitting than doing so for predictors allcat set to TRUE to ensure that all categories of categorical variables having more than two categories are redundant see details below redun minfregq iterms digits long Details 213 For a binary or categorical variable there must be at least two categories with at least min f req observations or the variable will be dropped and not checked for redundancy against other variables minfreq also specifies the minimum frequency of a category or its complement before that category is considered when allcat TRUE set to TRUE to consider derived terms dumm
65. assuming an exponential distribution can also use summarize g lt function y computations for one race group futime lt y 1 event lt y 2 sum futime sum event assume event 1 for death O alive mApply cbind followup time death race g To run mApply on a data frame xn lt asNumericMatrix x m lt mApply xn race h Here assume h is a function that returns a matrix similar to x matrix2dataFrame m Get stratified weighted means g lt function y wtd mean y 1 y 2 268 summarize summarize cbind y wts llist sex race g stat name y mApply cbhind y wts llist sex race g Compare speed of mApply vs by for computing d lt data frame sex sample c female male 100000 TRU country sample letters 100000 TRUE yl runif 100000 y2 runif 100000 g lt function x y lt c median x yl x y2 med sum median x y1 x y2 names y lt c med diff med sum y Ei system time by d llist sex d sex country d country g system time x lt asNumericMatrix d a lt subsAttr d m lt mApply x llist sex d sex country d country g system time x lt asNumericMatrix d summarize x llist sex dS sex country dS country g An example wher ach subject has one record per diagnosis but sex of subject is duplicated for all the rows a subject has Get the cross classified frequencies of
66. c 1 1 sd sqrt n qt 975 n 1 prints confidence limits Fit a linear model data frame create modify check 59 fit lt lm fev other variables detach He Se OSE GE SR ORE The last command is only needed if you want to start operating on another data frame and you want to get FEV out of the way Creating data frames from scratch Data frames can be created from within S Plus To create a small data frame containing ordinary data you can use something like dframe lt data frame age c 10 20 30 SE E E HE sex c male female male You can also create a data frame using the Data Sheet Create an empty data frame with the correct variable names and types then edit in the data dd lt data frame age numeric 0 sex c The sex variable will be stored as levels will be automatically added haracter 0 a factor and to it as you jcetable lt expand define new values for sex in the Data Sheet s sex column When the data frame you need to create is defined by systematically varying variables e g all possible combinations of values of each variable the expand grid function is useful for quickly creating the data Then you can add non systematically varying variables to the object created by expand grid using programming statements or editing the Data Sheet This process is useful for creating a data frame representing all the value
67. carry along rows from a data frame let ycase and ycontrol be non overlapping integer subscripts of the donor data frame tol a vector of tolerances with number of elements the same as the number of columns of y for find matches For matchCases is a scalar tolerance scale a vector of scaling constants with number of elements the same as the number of columns of y maxmatch maximum number of matches to allow For matchCases maximum number of controls to match with a case default is 20 If more than maxmat ch match ing controls are available a random sample without replacement of maxmat ch controls is used if which random object an object created by find matches digits number of digits to use in printing distances idcase idcontrol vectors the same length as xcase and xcontrol respectively specifying the id of cases and controls Defaults are integers specifying original element posi tions within each of cases and controls maxobs maximum number of cases and all matching controls combined maximum di mension of data frame resulting from matchControls Default is ten times the maximum of the number of cases and number of controls maxobs is used to allocate space for the resulting data frame which set to closest the default to match cases with up to maxmat ch controls that most closely match on x Set which random to use randomly chosen controls In either case only those controls within tol on x are allowed to be used unuse
68. character sizes Examples of plotting raw data Show the median and quartiles of height given age stratified by sex and race Draws 2 sets male female of 3 lines per panel xYplot height age race groups sex method quantiles dfr lt expand grid month 1 12 continent c Europe USA sex c female male set seed 1 dfr lt upData dfr y month 10 1 sex female 2 continent Europe runit 48 15 15 lower y runif 48 05 15 upper y runif 48 05 15 xYplot 333 xYplot Cbind y lower upper month subset sex male amp continent USA data dfr xYplot Cbind y lower upper month continent subset sex male data dfr xYplot Cbind y lower upper month continent groups sex data dfr Key add label curves FALSE to suppress use of labcurve to label curves wher farthest apart xYplot Cbind y lower upper month groups sex subset continent Europe data dfr xYplot Cbind y lower upper month groups sex type b subset continent Europe keys lines data dfr keys lines causes labcurve to draw a legend where the panel is most empty xYplot Cbind y lower upper month groups sex type b data dfr subset continent Europe method bands xYplot Cbind y lower upper month groups sex type b data dfr subset continent Europe method upper label dfrSy lt Qu
69. character string for substring location numeric string all digits first a vector of integers specifying the first position to replace for subst ring2 lt first may also be a vector of character strings that are passed to sedit to use as patterns for replacing substrings with setto See one of the last examples below last a vector of integers specifying the ending positions of the character substrings to be replaced The default is to go to the end of the string When first is character last must be omitted setto a character string or vector of character strings used as replacements in subst ring2 lt old a character string to translate from for replace substring wild May be x or or any string containing a single x but not beginning with or ending with new a character string to translate to for replace substring wild test a function of a vector of character strings returning a logical vector whose ele ments are TRUE or FALSE according to whether that string element qualifies as the wild card string forsedit replace substring wild wild literal set to TRUE to not treat asterisks as wild cards and to not look for or in old restrict a vector of two integers for subst ring location which specifies a range to which the search for matches should be restricted front specifying front TRUE and old x is the same as specifying old Aa back specifying back TRUE andold
70. coef norm 2 Arguments knots coef type norm columns before vector of knots used in the regression fit vector of coefficients from the fit If the length of coef is k 1 where k length knots the first coefficient must be for the linear term and remain ing k 2 coefficients must be for the constructed terms e g from rcspline eval If the length of coef is k an intercept is assumed to be in the first element or a zero is prepended to coef for rcsplineFunction The default is to represent the cubic spline function corresponding to the co efficients and knots Set type integral to instead represent its anti derivative a character string to use as the variable name in the LaTeX expression for the formula length of x to count with respect to columns Default is length of character string contained by x You may want to set 1x smaller than this if it includes non printable LaTeX commands normalization that was used in deriving the original nonlinear terms used in the fit See rcspline eval for definitions maximum number of symbols in the LaTeX expression to allow before inserting a newline V command Set to a very large number to keep text all on one line text to place before each line of LaTeX output Use amp amp for an equation ar ray environment in LaTeX where you want to have a left hand prefix e g X amp amp orusing leftegn respline restate 211 afte
71. contents 35 data frame create modify check 49 getHdata 110 Save 230 store 256 upData 309 Topic design ldBands 151 338 Topic device ps slide 192 Topic distribution describe 60 Ecdf 69 hist data frame 114 histbackback 115 ldBands 151 panel bpplot 178 rMultinom 225 scatld 231 wtd stats 319 Topic documentation list tree 155 Topic dplot approxExtrap 4 cnvrt coords 3l hist data frame 114 histbackback 115 labcurve 129 mgp axis 163 scatl1d 231 subplot 260 Topic environment mgp axis 163 Topic file csv get 40 format df 100 getZip 112 html 125 latex 142 mdb get 162 Save 230 spss get 252 src 253 stata get 254 Topic hplot bpplot 25 curveRep 42 dotchart2 66 Ecdf 69 errbar 75 event chart 78 event convert 88 hist data frame 114 histbackback 115 labcurve 129 minor tick 167 INDEX mtitle 172 panel bpplot 178 plsmo 184 ps slide 192 rm boot 218 scatid 231 summary formula 266 symbol freq 280 xYplot 324 Topic htest binconf 17 biVar 19 bpower 23 ciapower 30 cpower 37 data frame create modify check 49 deff 59 find matches 95 gbayes 104 hoef fd 123 impute 127 ldBands 151 mhgr 165 plotCorrPrecision 183 popower 187 reorr 198 rm boot 218 samplesize bin 226 smean sd 244 spower 247 t test cluster 282 Topic interface contents 35 data frame create modi
72. converted to S function code typically a model fit object of some sort When creating separate S functions for each variable in x the name of the new function will be prefix placed in front of the variable name and suffix placed in back of the name The default is to use names of the form varname where varname is the variable name position in search list at which to store new functions for Function De fault is position 1 in the search list See the assign function for more docu mention on the where argument a vector corresponding to x for invert Tabulated if its first argument x is not a list transcan 303 freq a vector of frequencies corresponding to cross classified x and y if x is not a list Default is a vector of ones aty vector of transformed values at which inverses are desired rule see approx transcan assumes rule is always 2 regcoef only set to TRUE to make Varcov default delete positions in the covariance matrix for any non regression coefficients e g log scale parameter from psm or survreg Details The starting approximation to the transformation for each variable is taken to be the original coding of the variable The initial approximation for each missing value is taken to be the median of the non missing values for the variable for continuous ones or the most frequent category for cate gorical ones Instead if imp con is a vector its values are used for imputing NA values When using each
73. created by html For the generic html1 is any object for which an htm1 method exists html 129 file name of the file to create The default file name is object html where object is the first word in the name of the argument for object append set to TRUE to append to an existing file link character vector specifying hyperlink names to attach to selected elements of the matrix or data frame No hyperlinks are used if Link is omitted or for elements of link that are To allow multiple links per link link may also be a character matrix shaped as ob ject in which case 1inkCol is ignored linkCol column number of object to which hyperlinks are attached Defaults to first column linkType defaults to href arguments passed to format df x an object created by html Side Effects print or show launch a browser Author s Frank E Harrell Jr Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Maranget Luc HeVeA a LaTeX to HTML translater URL http para inria fr maranget hevea See Also latex Examples Not run x lt matrix 1 6 nrow 2 dimnames list c a b c c d e w lt latex x h lt html w run HeVeA to convert tex to html h lt html x convert x directly to html options browser konqueror use help browser for S Plus launch html browser by running print html w lt html x link c B hyperlink first row first col to B
74. created using exact values of variables so for continuous numeric variables it is often necessary to round them to the nearest v and to possibly curtail the values to some lower and upper limit before rounding Here v de notes a numeric constant specifying the matching tolerance that will be used dat aRep also stores marginal distribution summaries for all the variables For numeric variables all 101 percentiles are stored and for all variables the frequency distributions are also stored frequencies are computed after any rounding and curtailment of numeric variables For the purposes of rounding and cur tailing the roundN function is provided A print method will summarize the calculations made by dataRep and if long TRUE all unique combinations of values and their frequencies in the original dataset are printed The predict method for dat aRep takes a new data frame having variables named the same as the original ones but whose factor levels are not necessarily in the same order and examines the collapsed cross classifications created by dataRep to find how many observations were similar to each of the new observations after any rounding or curtailment of limits is done predict also does some calculations to describe how the variable values of the new observations stack up against the marginal distributions of the original data For categorical variables the percent of observations having a given variable with the value of the new obse
75. discriminating imputation models xl and x2 have some NAs on the same rows smaller n set seed 5 xl lt factor sample c a b c 100 TRUE x2 lt xl b 3 xl c rnorm 100 0 4 x3 lt rnorm 100 y lt x2 1 xl c 2 x3 rnorm 100 0 4 orig xl lt x1 1 20 orig x2 lt x2 18 23 x1 1 20 lt NA 2 18 23 lt NA x2 21 25 lt NA d lt data frame x1l x2 x3 y n lt naclus d plot n naplot n Show patterns of NAs 100 imputations to study them normally use 5 or 10 f lt aregImpute y xl x2 x3 n impute 100 nk 0 data d par mfrow c 2 3 plot f diagnostics TRUE maxn 2 Note diagnostics TRUE makes graphs similar to those made by r lt range fSimputed x2 orig x2 for i in 1 6 use 1 2 to mimic maxn 2 ry ry aregImpute 19 plot 1 100 fSimputed x2 i ylim r ylab paste Imputations for Obs i abline h orig x2 i 1lty 2 table orig xl apply fS imputed xl 1 modecat par mfrow c 1 1 plot orig x2 apply fSimputed x2 1 mean fmi lt fit mult impute y xl x2 lm f data d sqrt diag Varcov fmi feo lt Ilm y xl x2 summary fcc SES are larger than from mult imputation Study relationship between smoothing parameter for weighting function multiplier of mean absolute distance of transformed predicted values used in tricube weighting function and standard deviation of multipl
76. draw additional line segments connecting events one and two two and three and four and five possibly with different colors an ap propriate 1 ine add argument would be mat rix c first event second event sec fourth event fifth event 2 3 One line segment would be drawn between first event and second event a second line seg ment would be drawn between second event and third event anda 86 event chart third line segment would be drawn between fourth event and fifth event Different line types widths and colors can be specified in arguments listed just below The convention use of subset c and line add must match i e column name must be used for both or column number must be used for both Ifline add NA lengthof line add lty line add lwd and line add col must be the same as number of pairs of additional line segments to add NOTE The drawing of the original default line may be suppressed with Line col 0 and line addcan be used to do all the line plotting for the event chart line add lty a kxl vector corresponding to the columns of line add specifies the line types for the k line segments line add lwd a kx1 vector corresponding to the columns of line add specifies the line widths for the k line segments line add col a kx vector corresponding to the columns of line add specifies the line colors for the k line segments point pch vector of pch values for points repres
77. ee ba ew 131 laDCURVes3 2 2 4 2b ee teat wb e SG oti tee ee ee beds J 132 label n ee ea RR ae a hae ee ee a ee Se eo 141 DAR re oe ony bo ce Bh Oe os de ae ay be we Mage ee Oe Sl 144 latex sb hehe ek Oe ee a Eee DEE EES LEAH EEE S EE aE a 145 IdBands 0s sak BA Oe be Sa e Pa Ge a A a ee a an 154 HStUES od en ee a ee he 4 dee eek bo ee ee ae Se Soe Ge ee eS 158 makeNst s 6 54 to Se be A e eS aE SE Ee A See ele 159 MAPPLY wa ea SD wal beta eS Ewa DR a ee E a eo aa el 160 MENOS e nae eae OR aa ed ye eee eS ae eRe ee a ee 161 MdD get s seiri Geko ek BAA RE ed Sede amp eb eh ee a ee ee ah 165 TOS P AXIS e o oa awe Sa ae A Shwe Re wa ee ee a ae BA ee aac 166 MASP ee eek ae eed bh Kee ee bee ee eb ban eee ee hea ee i 168 MOIMOGUCK s spb Se we a AR ee De ee ee ee ale 170 Mise a cates ep fads pe Gate ates aon Moke et Avis eh Beth a et At eaten my eee Bis ee a Beaks 171 MUG ode he ae kek eae ae ROR edo bee ee OM ee eee ee ai 173 nadelete sose k a bow Eke eR Re eh o be SS a a be ek Ra 176 ma detall respOnse s 424 S064 amp Be AEBS Sw Se ERE Sa Dae aE ES bo 177 MaKe p soeone ges FS dob g poe Bae ee ee bee Sob ds ee Ba ee A 179 AS pa ai ho Gis Ses ace ethene BU a ae oS eee we a we a A Rd Be Ge 180 panel bpplot 2 64 44 44 e wie e i he de a ete Pes S eae he i aia 181 PaON sos eoe be eee Pha a es eee ee dae ba eee 184 Pelea eea ee ee EE ee Sebo Be eo ee Se et oe e a 185 plotCormPrecision sara 884 9 ea 8 AERA we SENS ERSESLE
78. either before or after sorting depending on specification of sort by and value of sort after subset x axis character string specifying whether program will control labelling of x axis with argument auto orif user will control labelling with argument custom event chart 85 If custom is chosen user must specify location and text of labels using x axis custom at and x axis custom labels arguments respec tively listed below X axis custom at user specified vector of x axis label locations Must be used when x axis custom will not be used otherwise X axis custom labels x jul x lim extend x Sca x lab line line line line line ian le by ity lwd COL add user specified vector of x axis labels Must be used when x axis custom will not be used otherwise ES logical flag which will only be considered if x axis auto IfFALS default will convert julian dates or S plus dates objects into mm dd yy for mat for the x axis labels If TRUE dates will be printed in julian numeric format NOTE This argument should remain TRUE if x reference is spec ified two dimensional vector representing the number of time units usually in days that the user wants to increase xlim on left hand side and right hand side of X axis respectively Default is c 0 0 This argument will not take effect if the Goldman chart is utilized a factor w
79. event chart cdcaids subset c c infedate diagdate dethdate censdate x lab time since transfusion in days y lab patients sorted by AIDS diagnosis date titl AIDS data interval event chart la with viral load at diagdate represented point pch c 1 2 15 0 point cex c 1 1 0 8 0 8 point cex mult 0 00002 point cex mult var viralload extra points no mult c 1 NA legend plot TRUE legend location i legend cex 1 0 legend point text c transfusion AIDS diagnosis death censored x reference infedate x julian TRUE legend bty 0 legend point at list c 1400 1950 c 7 1 event convert 91 To ref pos even sub grea Val tit poi leg produce more complicated interval chart which is erenced by infection date and sorted by age and incubation period tscript example3 ps horizontal TRUE t chart cdcaids set c c infedate diagdate dethdate censdate ab time since diagnosis of AIDS in days ab patients sorted by age and incubation length 1 AIDS data interval event chart 2 sorted by age incubation nt pch c 1 2 15 0 point cex c 1 1 0 8 0 8 end plot TRUE legend location i legend cex 1 0 legend point text c transfusion AIDS diagnosis death censored x reference diagdate x julian TRUE sort by c age diffdate line by age line lty c 1 3 2 4 line lwd rep 1 4 line col
80. expect lt function y c Years sum y 1 sum y 2 attach pbc S lt Surv follow up time death s2 lt summary S age albumin ascites edema stratify drug fun life expect g 3 Note You can summarize other response variables using the same independent variables using e g update s2 response or you can change the list of independent variables using e g update s2 response ascites or update s2 ascites You can also print typeset or plot subsets of s2 e g plot s2 c age albumin or plot s2 1 2 s2 invokes print summary formula response Plot results as a separate dot chart for each of the 3 strata levels par mfrow c 2 2 plot s2 cex labels 6 xlim c 0 40 superposeStrata FALS EJ Typeset table creating s2 tex w lt latex s2 cdec 1 Typeset table but just print LaTeX code latex s2 file useful for Sweave Take control of groups used for age Compute 3 quartiles for both cholesterol and bilirubin excluding observations that are missing on EITHER ONE age groups lt cut2 age c 45 60 g lt function y apply y 2 quantile c 25 5 75 summary formula 281 y lt cbhind Chol chol Bili bili label y lt Cholesterol and Bilirubin You can give new column names that are not legal S Plus names by enclosing them in quotes e g Chol mg dl chol s lt summar
81. explicitly handed by the describe and summary formula functions NAs or blanks in input variables are ignored format mChoice will convert the multiple choice representation to text form by substituting levels for integer codes as double mChoice converts the mChoice object to a binary numeric matrix one column per used level or all levels of drop FALSE This is called by the user by invoking as numeric There is a print method anda summary method anda print method for the summary mChoice object The summary method computes frequencies of all two way choice combinations the frequencies of the top 5 combinations information about which 162 mChoice other choices are present when each given choice is present and the frequency distribution of the number of choices per observation This summary output is used in the describe function in mChoice creates a logical vector the same length as x whose elements are TRUE when the observation in x contains at least one of the codes or value labels in the second argument match mChoice creats an integer vector of the indecies of all elements in t able which contain any of the speicified levels is mChoice returns TRUE is the argument is a multiple choice variable Usage mChoice label sort levels c original add none FALSE drop TRUI alphabetic Pls S3 method for class mChoice format x minlength NULL sep S3 method for class
82. flat specifying monotone x2 if method avas would have resulted in a smaller confidence band for x2 summary f use summary f values list x2 c 2 5 8 for example if you want to use nice round values for judging effects Plot Y hat vs Y this doesn t work if there were NAs plot fitted f y or plot predict f statistic fitted y Show fit of model by varying xl on the x axis and creating separate panels for x2 and x3 For x2 using only a few discrete values transace 293 newdat lt expand grid xl seq 2 2 length 100 x2 c 25 75 x3 c cat dog cow yhat lt predict f newdat statistic fitted statistic mean to get estimated mean rather than simple inverse trans xYplot yhat x1 x2 groups x3 type l data newdat Not run Another example on hypothetical data f lt areg boot response I age monotone blood pressure race use I response to not transform the response variable plot f conf int 9 Check distribution of residuals plot fitted f resid f qqnorm resid f Refit this model using ols so that we can draw a nomogram of it The nomogram will show the linear predictor median mean The last two are smearing estimators Function f type individual create transformation functions f ols lt ols response response age blood pressure blood pressure race race Note This model is almost exactly the same as f but t
83. for each of the multiple imputations i e for the ith imputation of a sometimes missing variable i 1 2 n impute a flexible additive model is fitted on a sample with replacement from the original data and this model is used to predict all of the original missing and non missing values for the target variable areg is used to fit the imputation models By default linearity is assumed for target variables variables being imputed and nk 3 knots are assumed for continuous predictors transformed us ing restricted cubic splines If nk is three or greater and t linear is set to FALSE areg simul taneously find transformations of the target variable and of all of the predictors to get a good fit assuming additivity maximizing R using the same canonical correlation method as t ranscan Flexible transformations may be overridden for specific variables by specifying the identity trans formation for them When a categorical variable is being predicted the flexible transformation is Fisher s optimum scoring method Nonlinear transformations for continuous variables may be non monotonic If nk is a vector areg s bootstrap and crossval 10 options will be used to help find the optimum validating value of nk over values of that vector at the last imputation iteration For the imputations the minimum value of nk is used Instead of defaulting to taking random draws from fitted imputation models using random residuals as is done by transcan are
84. for ties This can be used to ob tain Wilcoxon tests and rank correlation coefficients wid loess noiter is a weighted version of loess smooth when no iterations for outlier rejection are desired This results in especially good smoothing when y is binary num denom setup is a utility function that allows one to deal with observations containing numbers of events and numbers of trials by outputting two observations when the number of events and non events trials events exceed zero A vector of subscripts is generated that will do the proper duplications of observations and a new binary variable y is created along with usual cell frequencies weights for each of the y 0 y 1 cells per observation Usage wtd mean x weights NULL normwt ignored na rm TRUE wtd var x weights NULL normwt FALSE na rm TRUE wtd quantile x weights NULL probs c 0 25 5 75 1 type c quantile i 1 n 1 i n 1 i n normwt FALSE na rm TRUE wtd Ecdf x weights NULL type e i n 2 1 7 n 1 iZ ntl 1 normwt FALSE na rm TRUE wtd table x weights NULL type c list table normwt FALSE na rm TRUE wtd rank x weights NULL normwt FALSE na rm TRUE wtd loess noiter x y weights rep 1 n robust rep 1 n span 2 3 degree 1 cell 13333 type c all ordered all evaluate evaluation 100 na rm TRUE num denom setup num denom Gl
85. from the others by ace or avas aregImpute 17 Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Little R An H Robust likelihood based analysis of multivariate data with missing values Statistica Sinica 14 949 968 2004 van Buuren S Brand JPL Groothuis Oudshoorn CGM Rubin DB Fully conditional specifications in multivariate imputation J Stat Comp Sim 72 1049 1064 2006 de Groot JAH Janssen KJM Zwinderman AH Moons KGM Reitsma JB Multiple imputation to correct for partial verification bias revisited Stat Med 27 5880 5889 2008 Siddique J Multiple imputation using an iterative hot deck with distance based donor selection Stat Med 27 83 102 2008 See Also fit mult impute transcan areg naclus naplot mice dotchart2 Ecdf Examples Check that aregImpute can almost exactly estimate missing values when there is a perfect nonlinear relationship between two variables Fit restricted cubic splines with 4 knots for xl and x2 linear for x3 set seed 3 xl lt rnorm 200 x2 lt x1 2 x3 lt runit 200 m lt 30 x2 1 m lt NA a lt aregImpute x1 x2 I x3 n impute 5 nk 4 match closest a matplot x1 1 m 2 a imputed x2 abline a 0 b 1 lty 2 x1 13m 2 aSimputed x2 Multiple imputation and estimation of variances and covariances of regression coefficient estimates accounting for imputation
86. impute age mean impute age random age i summary age i is imputed age i inc dec Increment and Decrement Description inc lt increments x by value Equivelent tox lt x value dec lt decrements x by the value Equivelent to x lt x value Usage inc x lt value dec x lt value Arguments x object to be incremented or decremented value value by which x will be modified 132 labcurve Author s Charles Dupont Examples x lt 1 5 inc x lt 5 x c 6 7 8 9 10 dec x lt 3 x c 3 4 5 6 7 labcurve Label Curves Make Keys and Interactively Draw Points and Curves Description labcurve Optionally draws a set of curves then labels the curves A variety of methods for drawing labels are implemented ranging from positioning using the mouse to automatic labeling to automatic placement of key symbols with manual placement of key legends to automatic placement of legends For automatic positioning of labels or keys a curve is labeled at a point that is maximally separated from all of the other curves Gaps occurring when curves do not start or end at the same x coordinates are given preference for positioning labels If labels are offset from the curves the default behaviour if the closest curve to curve 1 is above curve i curve 1 is labeled below its line If the closest curve is below curve i curve i is labeled above its line These directions are reversed if
87. is not implemented in S Plus versions older than 6 2 vname unused argument in latex describe single Details If options na detail response TRUE has been set andna actionis na delete or na keep summary statistics on the response variable are printed separately for missing and non missing values of each predictor The default summary function returns the number of non missing response values and the mean of the last column of the response values with a names attribute of c N Mean When the response is a Surv object and the mean is used this will result in the crude proportion of events being used to summarize the response The actual summary function can be designated through options na fun response function name Value a list containing elements descript counts values The list is of class describe If the input object was a matrix or a data frame the list is a list of lists one list for each variable analyzed latex returns a standard latex object For numeric variables having at least 20 unique values an additional component intervalFreq This component is a list with two elements range containing two values and count a vector of 100 integer frequency counts Author s Frank Harrell Vanderbilt University f harrell vanderbilt edu See Also sas get quantile table summary model frame default naprint lapply tapply Surv na delete na keep na detail response latex Examples set seed 1
88. latex recursively for each element in the argument latexTranslate translates particular items in character strings to LaTeX format e g makes a 2 a 2S for superscript within variable labels LaTeX names of greek letters e g alpha will have backslashes added if greek TRUE Math mode is inserted as needed latexTranslate assumes that input text always has matches e g and that surrounding by is OK latexSN converts a vector floating point numbers to character strings using LaTeX exponents Dollar signs to enter math mode are not added latex latexTabular creates a character vector representing a matrix or data frame in a simple tabular environment latexVerbatim on an object executes the object s print method capturing the output for a file inside a LaTeX verbatim environment dvi uses the system latex command to compile LaTeX code produced by latex including any needed styles dvi will put a documentclass report and end document wrapper around a file produced by latex By default the geomet ry LaTeX package is used to omit all margins and to set the paper size to a default of 5 5in wide by 7in tall The result of dvi is a dvi file To both format and screen display a non default size use for example print dvi latex x width 3 height 4 width 3 height 4 Note that you can use something like xdvi geometry 460x650 margins 2 25in file without changing LaTeX defaults to em ulate this dvips will
89. levels mean digits minmax TRUE oneval TRUE Arguments x numeric vector to classify into intervals cuts cut points m desired minimum number of observations in a group g number of quantile groups levels mean setto TRUE to make the new categorical vector have levels attribute that is the group means of x instead of interval endpoint labels digits number of significant digits to use in constructing levels Default is 3 5 if levels mean TRUE minmax if cuts is specified but min x lt min cuts or max x gt max cuts aug ments cuts to include min and max x oneval if an interval contains only one unique value the interval will be labeled with the formatted version of that value instead of the interval endpoints unless oneval FALSE onlycuts set to TRUE to only return the vector of computed cuts This consists of the interior values plus outer ranges Value a factor variable with levels of the form a b or formatted means character strings unless onlycuts is TRUE in which case a numeric vector is returned See Also cut quantile Examples set seed 1 x lt runif 1000 0 100 2 lt cut2 Ge c 10 20 30 table z table cut2 x g 10 quantile groups table cut2 x m 50 group x into intevals with at least 50 obs onlycuts FALS Fl 52 data frame create modify check data frame create modify check Tips for Creating Modifying and Checking Data Frames Descr
90. list to find variables that need to be imputed when all vari ables are to be imputed automatically by impute transcan i e when no input variable name is specified Default is first search position that contains the first variable to be imputed where out location in the search list for storing variables with missing values set to im puted values for impute transcan when all variables with missing values are being imputed automatically frame out Instead of specifying where out you can specify an S frame number into which individual new imputed variables will be written For example frame out 1 is useful for putting new variables into a temporary local frame when impute is called within another function see fit mult impute See assign for details about frames For R where out and frame out are ig nored and results are stored in GlobalEnv when list out is not specified it is recommended to use list out TRUE list out If var is not specified you can set List out TRUEtohave impute transcan return a list containing variables with needed values imputed This list will con tain a single imputation check set to FALSE to suppress certain warning messages newdata a new data matrix for which to compute transformed variables Categorical vari ables must use the same integer codes as were used in the call to transcan If a formula was originally specified to t ranscan instead of a data matrix newdata is optional a
91. lt 200 xl lt rnorm n x2 lt rnorm n y lt rnorm n x1 2 z lt areg cbind xl1 x2 y xtype c s 1 nk 3 par mfrow c 2 2 plot z y transformation is inverse quadratic but areg gets the same answer by making x1 quadratic n lt 5000 xl lt rnorm n x2 lt rnorm n y lt x1 rnorm n 2 z lt areg cbind x1 x2 y nk 5 par mfrow c 2 2 plot z Overfit 20 predictors when no true relationships exist n lt 1000 x lt matrix runif n 20 n 20 12 aregImpute y lt rnorm n lt areg x y nk 5 add crossval 4 to expose the problem N Test predict function hy LO x lt Fnorm n y lt rnorm n x g lt sample 1 3 n TRUE z lt areg cbind x g y xtype c s c range predict z cbind x g zSlinear predictors aregImpute Multiple Imputation using Additive Regression Bootstrapping and Predictive Mean Matching Description The transcan function creates flexible additive imputation models but provides only an ap proximation to true multiple imputation as the imputation models are fixed before all multiple imputations are drawn This ignores variability caused by having to fit the imputation models aregIimpute takes all aspects of uncertainty in the imputations into account by using the boot strap to approximate the process of drawing predicted values from a full Bayesian predictive distri bution Different bootstrap resamples are used
92. make individual variables available getHdata plasma all download and open two browser windows description file is available for plasma End Not run getZip Open a Zip File From a URL Description Allows downloading and reading of a zip file containing one file Usage getZip url password NULL Arguments url either a path to a local file or a valid URL password required to decode password protected zip files Details Allows downloading and reading of zip file containing one file The file may be password protected If a password is needed then one will be requested unless given Note to make password protected zip file z zip do zip e z myfile Value Returns a file O I pipe Author s Frank E Harrell See Also pipe 116 hdquantile Examples Not run read csv getZip http biostat mc vanderbilt edu twiki pub Sandbox WebHome z zip Password is foo End Not run hdquantile Harrell Davis Distribution Free Quantile Estimator Description Computes the Harrell Davis 1982 quantile estimator and jacknife standard errors of quantiles The quantile estimator is a weighted linear combination or order statistics in which the order statis tics used in traditional nonparametric quantile estimators are given the greatest weight In small samples the H D estimator is more efficient than traditional ones and the two methods are asymp totically equivalent The H D estimator is
93. methods for method reverse forprint summary formula reverseandlatex summary formula reveres specify round to round the quantiles and optional mean and standard deviation to round digits after the decimal point prmsd msdsize long pdig eps auxCol what twoway which conType cex means xlim xlab add main summary formula set to TRUE to print mean and SD after the three quantiles for continuous vari ables with method reverse defaults to NULL to use the current font size for the mean and standard deviation if prmsd is TRUE Set to a character string to specify an alternate LaTeX font size set to TRUE to print the results for the first category on its own line not on the same line with the variable label for method reverse with print and latex methods number of digits to the right of the decimal place for printing P values Default is 3 This is passed to format pval P values less than eps will be printed as lt eps See format pval an optional auxiliary column of information right justified to add in front of statistics typeset by latex summary formula reverse This argument is a list with a single element that has a name specifying the column heading If this name includes a newline character the portions of the string before and after the newline form respectively the main heading and the subheading typi cally set in smaller font respectively See
94. mfrow and mfcol for multi figure plotting subplot grconvertX and grconvertyY in R2 7 0 and later Examples old par lt par no readonly TRUE par mfrow c 2 2 xpd NA generate some sample data tmp x lt rnorm 25 10 2 tmp y lt rnorm 25 50 10 tmp z lt rnorm 25 0 1 plot tmp x tmp y draw a diagonal line across the plot area tmpl lt cnvrt coords c 0 1 c 0 1 input plt lines tmplSusr col blue draw a diagonal line accross figure region tmp2 lt cnvrt coords c 0 1 c 1 0 input fig lines tmp2Susr col red save coordinate of point 1 and y value near top of plot for future plots tmp pointl lt cnvrt coords tmp x 1 tmp y 1 tmp rangel lt cnvrt coords NA 0 98 input plt make a second plot and draw a line linking point 1 in each plot plot tmp y tmp z tmp point2 lt cnvrt coords tmp pointlS dev input dev arrows tmp y 1 tmp z 1 tmp point2Susr x tmp point2SusrS y col green draw another plot and add rectangle showing same range in 2 plots plot tmp x tmp z tmp range2 lt cnvrt coords NA 0 02 input plt tmp range3 lt cnvrt coords NA tmp rangelSdevSy input dev rect 9 tmp range2SusrSy 11 tmp range3SusrSy border yellow put a label just to the right of the plot and near the top of the figure region text cnvrt coords 1 05 NA input plt SusrS x cnvrt coords NA 0 75 input fig SusrSy Labe
95. nearly flat segment resulting in a many to one transformation in that region Sampling weights are a combination of the frequency of occurrence of x values that are within tol Inverse times the range of y and the squared distance between the associated y values and the target y value aty Usage transcan x method c canonical pc categorical NULL asis NULL nk imputed FALSE n impute boot method c approximate bayesian simple trantab FALSE transformed FALSE impcat c score multinom rpart tree mincut 40 inverse c linearInterp sample tolInverse 05 pr TRUE pl TRUE allpl FALSE show na TRUE imputed actual c none datadensity hist qq ecdf iter max 50 eps 1 curtail TRUE imp con FALSE shrink FALSE init cat mode nres if boot method simple 200 else 400 data subset na action treeinfo FALSE rhsImp c mean random details impcat S3 method for class transcan summary object long FALSE a S3 method for class transcan print x long FALSE S3 method for class transcan plot x aa S3 method for class transcan impute x var imputation name where in data where out 1 frame out list out FALSE pr TRUE check TRUE sa fit mult impute formula fitter xtrans data n impute fit reps FALSE dtrans derived pr TRUE subset S3 method
96. numeric variable with more than two unique values 1 for a binary numeric variable and c for a factor categorical or character variable number of knots 0 for linear or 3 or more Default is 4 which will fit 3 param eters to continuous variables one linear term and two nonlinear terms number of bootstrap resamples used to estimate covariance matrices of transfor mation parameters Default is no bootstrapping set to FALSE if you are sure that observations with NAs have already been re moved singularity tolerance List source code for 1m fit qr bare for details set to a positive integer k to compute k fold cross validated R squared square of first canonical correlation and mean and median absolute error of predictions on the original scale number of digits to use in formatting for printing an object created by areg integer or character vector specifying which predictors are to have their trans formations plotted default is all The y transformation is always plotted tells predict whether to obtain predicted untransformed y t ype 1p the default or predicted y on the original scale t ype fitted When the y transform is non monotonic you may specify what sample to predict to obtain a random sample of y values on the original scale instead of a matrix of all y inverses See inverseFunction arguments passed to the plot function areg is a competitor of ace in the acepack package Tran
97. of non missing obser vations na rm set to FALSE to suppress checking for NAs Details The rcorr cens function which although slower than somers2 for large sample sizes can also be used to obtain Dxy for non censored binary y and it has the advantage of computing the standard deviation of the correlation index Value a vector with the named elements C Dxy n number of non missing pairs and Missing Uses the formula C mean rank x y 1 nl 1 2 n n1 where n1 is the frequency of y 1 250 spower Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu See Also rceorr cens rank wtd rank Examples set seed 1 predicted lt runif 200 dead lt sample 0 1 200 TRUE roc area lt somers2 predicted dead C Check weights lt 176 lt 0 0 1 0 1 4 lt 03 272537271 somers2 x y somers2 rep x f rep y f somers2 x y f hK x Se spower Simulate Power of 2 Sample Test for Survival under Complex Condi tions Description Given functions to generate random variables for survival times and censoring times spower simulates the power of a user given 2 sample test for censored data By default the logrank Cox 2 sample test is used and a Logrank function for comparing 2 groups is provided For composing S Plus functions to generate random survival times under complex conditions the Q
98. of the target variable that are monotonically related to predicted values from every other bootstrap resample The same is true for Bayesian predicted values This causes predictive mean matching to always match on the same donor observation When the missingness mechanism for a variable is so systematic that the distribution of observed values is truncated predictive mean matching does not work It will only yield imputed values that are near observed values so intervals in which no values are observed will not be populated by im puted values For this case the only hope is to make regression assumptions and use extrapolation With type regression aregImpute will use linear extrapolation to obtain a hopefully reasonable distribution of imputed values The regression option causes aregImpute to impute missing values by adding a random sample of residuals with replacement if there are more NAs than measured values on the transformed scale of the target variable After random residuals are added predicted random draws are obtained on the original untransformed scale using reverse linear interpolation on the table of original and transformed target values linear extrapolation when a random residual is large enough to put the random draw prediction outside the range of observed values The bootstrap is used as with type pmm to factor in the uncertainty of the imputation model As model uncertainty is high when the transformation of a target
99. option for connecting labels to curves using arrows When keys is a vector of integers it is taken to represent plotting symbols pchs and these symbols are plotted at equally spaced x coordinates on each curve by default using 5 points per curve The points are offset in the x direction between curves so as to minimize the chance of collisions labcurve 133 To add a legend defining line types colors or line widths with no symbols specify keys 1ines e g labcurve curves keys lines lty 1 2 putKey provides a different way to use key by allowing the user to specify vectors for labels line types plotting characters etc Elements that do not apply e g pch for lines t ype 1 may be NA When a series of points is represented by both a symbol and a line the corresponding elements of both pch and lt y col or 1wd will be non missing putKeyEmpty given vectors of all the x y coordinates that have been plotted uses largest empty to find the largest empty rectangle large enough to hold the key and draws the key using putKey drawP1lot is a simple mouse driven function for drawing series of lines step functions polyno mials Bezier curves and points and automatically labeling the point groups using labcurve or putKeyEmpty When drawPlot is invoked it creates temporary functions Points Curve and Abline in the session frame frame zero The user calls these functions inside the call to drawP lot to define g
100. plotted It is used the same as Key If the which argument is not specified to plot two pages of plots will be produced If you don t define par mfrow yourself plot summary formula reverse will try to lay out a multi panel graph to best fit all the individual dot charts for continuous variables There is a subscripting method for objects created with met hod response This can be used to print or plot selected variables or summary statistics where there would otherwise be too many on one page cumcategory is a utility function useful when summarizing an ordinal response variable It converts such a variable having k levels to a matrix with k 1 columns where column i is a vector of zeros and ones indicating that the categorical response is in level i 1 or greater When the left hand side of formula is cumcategory y the default fun will summarize it by computing all of the relevant cumulative proportions Usage S3 method for class formula summary formula data subset na action fun NULL method c response reverse cross 270 summary formula overall method response method cross continuous 10 na rm TRUE na include method reverse go 4 quant lt 0 025 70 05 0 025 0 25 0 375 0 57 0 625 0 275 0 38 75 nmin if method reverse 100 else 0 test FALSE conTest function grou
101. problems with the S Plus pdf graph function used by set pdf though 1 the default for points open circle is too large 2 graphs are not centered properly 3 gray scale does not work and 4 there is some wasted space at the bottom of the graph When drawing points the user may want to specify cex 0 7 It may be better to use set ps followed by topdf tex is a little function to save typing when including t ex commands in graphs that are used with the psfrag package in LaTeX to typeset any LaTeX text inside a postscript graphic tex surrounds the input character string with tex options This is especially useful for getting Greek letters and math symbols in postscript graphs By default tex returns a string with ps frag commands specifying that the string be centered not rotated and not specially enlarged or shrunk showPsfrag is used to display using ghostview a postscript image that contained psfrag LaTeX strings by building a small LaTeX script and running latex and dvips Usage ps slide file background if type 2 white else navy blue foreground if type 2 yellow else if background white black else white font Helvetica pointsize c 24 28 14 14 type hor type 4 lwd c 2 5 2 4 typel mgp if under unix list c 1 8 0 4 0 c 1 5 0 2 0 c 2 0 4 0 c 1 5 0 2 0 type else list G 1 85 0 5 0r Ee les 0247 0 2 O25 0 5 c 1 5 0 4 0 typel
102. redundant in another setting To ensure that all categories may be predicted well from other variables use the allcat option To ignore categories that are too infrequent or too frequent set minfreq to a nonzero integer When the number of observations in the category is below this number or the number of observations not in the category is below this number no attempt is made to predict observations being in that category individually for the purpose of redundancy detection Value an object of class redun Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu 214 reorder factor See Also areg dataframeReduce transcan varclus genetic Examples set seed 1 n lt 100 xi lt runif n x2 lt runif n x3 lt xl x2 runif n 10 x4 lt xl x2 x3 runif n 10 x5 lt factor sample c a b c n replace TRU x6 lt 1 x5 a x5 c redun x1 x2 x3 x4 x5 x6 r2 8 redun x1 x2 x3 x4 x5 x6 r2 8 minfreq 40 redun x1 x2 x3 x4 x5 x6 r2 8 allcat TRUE x5 is no longer redundant but x6 is Fi reorder factor Reorder Factor Levels Description Reorders the levels of a factor variable by the values or the summarized values of another variable Usage reorder factor x v FUN mean Arguments xX a factor variable v a numeric variable the same length as x FUN a statistical summarization function ap
103. rep 1 4 legend bty 0 legend point at list c 1350 800 c 7 1 legend line at list c 1350 800 c 16 8 legend line text c age 1 Se ot Sieh y gt ot 4 To produce the Goldman chart pos even sub x l tit y v poi leg leg X r tscript example4 ps horizontal TRUE t chart cdcaids set c c infedate diagdate dethdate censdate ab time since transfusion in days y lab dates of observation 1 AIDS data Goldman event chart 1 ar c infedate y var type d now line TRUE y jitter FALSE nt pch c 1 2 15 0 point cex c 1 1 0 8 0 8 mgp c 3 1 1 6 0 end plot TRUE legend location i legend cex 1 0 end point text c transfusion AIDS diagnosis death censored ference infedate x julian TRUE leg To surv cens surv event event nd bty 0 legend point at list c 1500 2800 c 9300 10000 convert coded time to event data then draw an event chart time lt c 5 6 3 1 2 ind R 61 0 L 1 0 data lt cbhind surv time cens ind data lt event convert surv data chart cbind rep 0 5 event data x julian TRUE x reference 1 event convert Event Conversion for Time to Event Data Description Convert a two column data matrix with event time and event code into multiple column event time with one event in each column Usage event convert data2 event time 1 eve
104. returns a matrix of statistics for the method argument Arguments can be passed to that function via a list methodArgs The statistic in the first column should be the measure of central tendency Examples of useful method functions are those listed under the help file for summary formula suchas smean cl normal xYplot can also produce bubble plots This is done when size is specified to xYplot When size is used a function sKey is generated for drawing a key to the character sizes See the bubble 328 xYplot plot example size can also specify a vector where the first character of each observation is used as the plotting symbol if rangeCex is set to a single cex value An optional argument to sKey other may contain a list of arguments to pass to draw key see xyplot for a list of possible arguments under the key option See the bubble plot example Dotplot isa substitute for dot plot allowing for a matrix x variable automatic superpositioning when groups is present and creation of a Key function When the x variable created by Cbind to simulate a matrix contains a total of 3 columns the first column specifies where the dot is positioned and the last 2 columns specify starting and ending points for intervals The intervals are shown using line type width and color from the trellis plot line list By default you will usually see a darker line segment for the low and high values with the dotted reference line elsewhere A good choice
105. second lines in column names default is Scriptsize see format Adf numeric dollar logical default dcolumn Set to TRUE to place dollar signs around nu meric values when dcolumn FALSE This assures that latex will use minus signs rather than hyphens to indicate negative numbers Set to FALSE when dcolumn TRUE as dcolumn sty automatically uses minus signs math row names logical set true to place dollar signs around the row names math col names cdot longtable logical set true to place dollar signs around the column names see format df Set to TRUE to use David Carlisle s LaTeX Longtable style allowing long tables to be split over multiple pages with headers repeated on each page The style element is setto Longtable The latex usepackage must reference longtable The file longtable sty will need to be in a directory in your TEXINPUTS path draft longtable ctable I forgot what this does set to TRUE to use Wybo Dekker s ctable style from CTAN Even though for historical reasons it is not the default it is generally the preferred method latex booktabs table env here lines page caption caption lot caption loc double slash vbar 151 Thicker but not doubled hlines are used to start a table when ctable is in effect set bookt abs TRUE to use the bookt abs Style of horizontal rules for better tables In this case double hl ines are not used to
106. set to FALSE to suppress printing r squares and shrinkage fac tors For impute transcan set to FALSE to suppress messages concerning the number of NA values imputed or for fit mult impute set to FALSE to suppress printing variance inflation factors accounting for imputation rate of missing information and degrees of freedom pl Set to FALSE to suppress plotting the final transformations with distribution of scores for imputed values if show na TRUE allpl Set to TRUE to plot transformations for intermediate iterations show na Set to FALSE to suppress the distribution of scores assigned to missing values as tick marks on the right margin of each graph See also imputed imputed actual The default is none to suppress plotting of actual vs imputed values for all variables having any NA values Other choices are datadensity to use datadensity to make a single plot hist to make a series of back to back histograms qq to make a series of q q plots or ecdf to make a series of empirical cdfs For imputed actual datadensity for 300 iter max eps curtail imp con shrink init cat nres data subset na action treeinfo transcan example you get a rug plot of the non missing values for the variable with be neath it a rug plot of the imputed values When imputed actual is not none imputed is automatically set to TRU Fl maximum number
107. smean cl boot y g levels g 1 B 2000 reps TRUE reps b lt attr smean cl boot y g levels g 2 B 2000 reps TRUE reps meandif lt diff tapply y g mean na rm TRUE a b lt quantile b a c 025 975 res lt c meandif a b names res lt c Mean Difference 025 975 res solvet solve Function with tol argument Description A slightly modified version of solve that allows a tolerance argument for singularity to1 which is passed to qr Usage solvet a b tol 1e 09 somers2 249 Arguments a a square numeric matrix b a numeric vector or matrix tol tolerance for detecting linear dependencies in columns of a See Also solve somers2 Somers Dxy Rank Correlation Description Computes Somers Dxy rank correlation between a variable x and a binary 0 1 variable y and the corresponding receiver operating characteristic curve area c Note that Dxy 2 c 0 5 somers allows fora weights variable which specifies frequencies to associate with each obser vation Usage somers2 x y weights NULL normwt FALSE na rm TRUI GI Arguments x typically a predictor variable NAs are allowed y a numeric outcome variable coded 0 1 NAs are allowed weights a numeric vector of observation weights usually frequencies Omit or specify a zero length vector to do an unweighted analysis normwt set to TRUE to make weights sum to the actual number
108. some or all of the numeric variables If ranges is not given or if a certain variable is not found in the list the empirical range mod ified by pretty is used Example ranges list age c 10 100 pressure c 50 150 a vector of labels to use in labeling the axes for datadensity data frame Default is to use the names of the variables in the input data frame Note mar gin widths computed for setting aside names of variables use the names and not these labels For hist Spike ifminf is specified low bin frequencies are set to a minimum value of minf times the maximum bin frequency so that rare data points will re main visible A good choice of minf is 0 075 datadensity data frame passes minf 0 075 to scat1d to pass to hist Spike Note that specifying minf will cause the shape of the histogram to be distorted somewhat multiplier for the smoothing window width computed by histSpike when type density a 2 vector specifying the outer limits of x for binning and plotting if add FALS and nint is a number Fl x axis label add FALSE default is name of input argument x set to TRUE to add the spike histogram to an existing plot to show marginal data densities y axis range for plotting if add FALS y axis label add FALS Gl Gl scatld 239 Details For scat 1d the length of line segments usedis frac min par pin par Suin opp data units where opp is the index of the opposite axis an
109. specified Defaults depend on whether S Plus or R are being used lref LaTeX reference point for st ring See the psf rag documentation referenced below Default is c for centered this is also the default for psref psref PostScript reference point scale scall factor default is 1 srt rotation for st ring in degrees default is zero Value nothing for most of the functions t ex returns a modified character string Side Effects Starts a postscript file or a process to convert it to pcx format or starts a Trellis postscript device ps slide Stores a system option ps slide file pdf graph opens a graphics file using pdf graph setps creates a function topdf in frame 0 the session database Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Grant MC Carlisle 1998 The PSfrag System Version 3 Full documentation is obtained by searching www ctan org for pfgguide ps See Also postscript par ps options mgp axis labels pdf trellis device setTrellis Examples Not run ps slide myslide myslide is file name prefix use ps slide myslide back green to use e g green background plot x y title My Title ps slide view TRUE makes myslide ps file use ps slide close TRUE to close file without viewing with ghostview ps slide view TRUE pcx TRUE converts myslide ps into myslide pcx PC Paintbrush format suitable for importi
110. stratification variable which is converted to a factor vector if it is not one already which method cat col group n unique show na naxes q cex axis cex var imgp tck ranges labels minf mult width xlim ylim xlab ylab add scatld set which cont inuous to only plot continuous variables or which categorical to only plot categorical character or discrete numeric ones By default all types of variables are depicted set method cat freq to depict frequencies of categorical variables with digits representing the cell frequencies with size proportional to the square root of the frequency By default vertical bars are used colors representing the group strata The vector of colors is recycled to be the same length as the levels of group number of unique values a numeric variable must have before it is considered to be a continuous variable set to FALSE to suppress drawing the number of NAs to the right of each axis number of axes to draw on each page before starting a new plot You can set naxes larger than the number of variables in the data frame if you want to compress the plot vertically a vector of quantiles to display By default quantiles are not shown character size for draw labels for axis tick marks character size for variable names and frequence of NAs spacing between numeric axis labels and axis see par for mgp see tck under par a list containing ranges for
111. string to label that group of points n number of points to accept from the mouse Default is to input points until a right mouse click rug for Points Default is none to not show the marginal x or y distributions as rug plots for the points entered Other possibilities are used to execute scat 1d to show the marginal distribution of x y or both as rug plots ymean for Points subtracts a constant from each y coordinate entered to make the overall mean ymean degree degree of polynomial to fit to points by Curve evaluation number of points at which to evaluate Bezier curves polynomials and other func tions in Curve ask set ask TRUE to give the user the opportunity to try again at specifying points for Bezier curves step functions and polynomials The labcurve function used some code from the function plot multicurve written by Rod Tjoelker of The Boeing Company t joelker espresso rt cs boeing com If there is only one curve a label is placed at the middle x value and no fancy features such as angle or positive negative offsets are used labcurve 139 key is called once with the argument plot FALSE to find the key dimensions Then an empty rectangle with at least these dimensions is searched for using largest empty Then key is called again to draw the key there using the argument corner c 5 5 so that the center of the rectangle can be specified to key If you want to plot the data an easier way to use l
112. summarize works especially well with the Hmisc xYplot function for displaying multiple summaries of a single variable on each panel such as means and upper and lower confidence limits asNumericMatrix converts a data frame into a numeric matrix saving attributes to reverse the process by matrix2dataframe It saves attributes that are commonly preserved across row subsetting i e it does not save dim dimnames or names attributes matrix2dataFrame converts a numeric matrix back into a data frame if it was created by asNumericMatrix Usage summarize X by FUN stat name deparse substitute X type c variables matrix subset TRUI Gl lt asNumericMatrix x matrix2dataFrame x at origAttributes restoreAll TRU E Arguments X a vector or matrix capable of being operated on by the function specified as the FUN argument by one or more stratification variables If a single variable by may be a vector oth erwise it should be a list Using the Hmisc 11ist function instead of list will result in individual variable names being accessible to summarize For exam ple you can specify Llist age group sex or llist Age age group sex The latter gives age group a new temporary name Age FUN a function of a single vector argument used to create the statistical summaries for summarize FUN may compute any number of statistics meer extra arguments are passed to FUN stat name the name to use when creat
113. test levels list y list a a b c b1 b2 dat2 describe dat2 dat lt dat2 copy to original name and delete dat2 if OK rm dat2 Remove hard to analyze variables from a redundancy analysis of all variables in the data frame lt dataframeReduce dat fracmiss 1 minprev 05 maxlevels 5 Could run redun data d at this point or include dataframeReduce arguments in the call to redun If you import a SAS dataset created by PROC CONTENTS CNTLOUT x datadict the LABELs from this dataset can be added to the data Let s also convert names to lower case for the main data file Not run mydata2 lt cleanup import mydata2 lowernames TRUE sasdict datadict End Not run 316 valueTags valueTags Store Discriptive Information About an Object Description Functions get or set useful information about the contents of the object for later use Usage valueTags x valueTags x lt value valueLabel x valueLabel x lt value valueName x valueName x lt valu valueUnit x valueUnit x lt value Arguments x an object value for valueTags lt a named list of value tags a character vector of length 1 or NULL Details These functions store the a short name of for the contents a longer label that is useful for display and the units of the contents that is useful for display valuetTag is an accessor and value
114. the list inside prior This setup was used because of difficulties in passing arguments through integrate for some situations mixing probability or weight for the Gaussian prior having mean dO and vari ance v0 mix must be between 0 and 1 inclusive mean of the first Gaussian distribution only Gaussian for gbayes1PowerNP and is a required argument variance of the first Gaussian only Gaussian for gbayes1PowerNP and is a required argument mean of the second Gaussian if mix lt 1 variance of the second Gaussian if mix lt 1 Any of these last 5 arguments can be omitted to gbayesMixPredNoData as they can be provided at run time to the function created by gbayesMixPredNoData specifies whether the predictive density or the CDF is to be computed Default is density a 2 vector containing the lower and upper limit for possible values of the test statistic x that would result in a left credible interval exceeding delta w with probability 1 alpha 2 110 gbayes nsim defaults to zero causing gbayesMixPowerNP to solve numerically for the critical value of x then to compute the power accordingly Specify a nonzero number such as 20000 for nsim to instead have the function estimate power by simulation In this case 0 95 confidence limits on the estimated power are also computed This approach is sometimes necessary if uniroot can t solve the equation for the critical value Value gbayes returns a list of class gbayes co
115. the number of characters in width and number of lines in height for each string in the st ring argument Value height a vector of the number of lines in each string width a vector with the number of character columns in the longest line Author s Charles Dupont See Also Sstring bounding box ichar Examples a lt c this is a single line string This is a nmulty line string stringDims a subplot 263 subplot Embed a new plot within an existing plot Description Subplot will embed a new plot within an existing plot at the coordinates specified in user units of the existing plot Usage subplot fun x y size c 1 1 vadj 0 5 hadj 0 5 pars NULL Arguments fun an expression defining the new plot to be embedded x x coordinate s of the new plot in user coordinates of the existing plot y y coordinate s of the new plot x and y can be specified in any of the ways understood by xy coords size The size of the embedded plot in inches if x and y have length 1 vadj vertical adjustment of the plot when y is a scalar the default is to center verti cally 0 means place the bottom of the plot at y 1 places the top of the plot at y hadj horizontal adjustment of the plot when x is a scalar the default is to center horizontally 0 means place the left edge of the plot at x and 1 means place the right edge of the plot at x pars a list of parameters to be passed to par before running fun Details The coo
116. the plots bpplt make a local copy of bwplot that always uses panel bpplot S Plus only bwplotSpanel lt panel bpplot bwplot g x nout 05 partition Patitions an object into different sets Description Partitions an object into subsets of length defined in the sep argument Usage partition vector x sep partition matrix x rowsep colsep Arguments x object to be partitioned sep determines how many elements should go into each set The sum of sep should be equal to the length of x rowsep determins how many rows should go into each set The sum of rowsep must equal the number of rows in x colsep determins how many columns should go into each set The sum of colsep must equal the number of columns in x ae arguments used in other methods of partition Value A list of equal length as sep containing the partitioned objects Author s Charles Dupont pel 185 See Also split Examples a lt Ley partition vector a sep c 1 3 2 1 pel First Principal Component Description Given a numeric matrix which may or may not contain NAs pc1 standardizes the columns to have mean 0 and variance 1 and computes the first principal component using prcomp The proportion of variance explained by this component is printed and so are the coefficients of the original not scaled variables These coefficients may be applied to the raw data to obtain the first PC Usage pcl x hi Arguments x n
117. the range of similarity values for scaling the y axes By default this is the observed range over all of s set to slimds to TRUE to scale diagonals and off diagonals separately set to TRUE to add similarities to an existing plot usually specifying 1ty or col line type color or line thickness for plotMultSim optional vector of variable names in order used in s relative height for subplot relative width for subplot relative extra height and width to leave unused inside the subplot Also used as the space between y axis tick mark labels and graph border set to FALSE to suppress drawing of labels in the x direction amount of space on a scale of 1 n where n is the number of variables to set aside for y axis labels options contrasts c contr treatment contr poly isissued temporar ily by varclus to make sure that ordinary dummy variables are generated for factor variables Pass arguments to the dataframeReduce function to remove problematic variables especially if analyzing all variables in a data frame Value for varclus or naclus a list of class varclus with elements call containing the calling statement sim similarity matrix n sample size used if x was not a correlation matrix already n is a matrix hclust the object created by hclust similarity and method For plot returns the object created by plclust naclus also returns the two vectors listed under varclus 321 description and napl
118. the screen col name align if column names are used they can be aligned right left or centre Default cen results in names centred between the sides of the columns they name If the width of the text in the columns is less than the width of the name col name align will have no effect Other options are right and left col txt align how character columns are aligned Options are the same as for col name align with no effect when the width of the column is greater than its name cell align how numbers are displayed in columns hsep character string to use as horizontal separator i e what separates columns vsep character string to use as vertical separator i e what separates rows Length cannot be more than one csep character string to use where vertical and horizontal separators cross If hsep is more than one character csep will need to be the same length There is no provision for multiple vertical separators row names logical are we printing the names of the rows col names logical are we printing the names of the columns append logical if file is not are we appending to the file or overwriting top border logical do we want a border along the top above the columns left border logical do we want a border along the left of the first column unused 194 prnz Details If any column of x is a mixture of character and numeric the distinction between character and numeric columns will be lost This is especi
119. the tabular environment are separated with vertical bar characters When voar FALSE columns are separated with white space The default vbar FALSE produces tables consistent with the style sheet for the Journal of the American Statistical Association collabel just assignment where size center landscape justification for column labels logical When TRUE the default the name of the function and the assignment arrow are printed to the file specifies placement of floats if a table environment is used Default is tbp To allow tables to appear in the middle of a page of text you might specify where htbp to latex default size of table text if a size change is needed default is no change For example you might specify size smal1 to use LaTeX font size small default is center to enclose the table in a center environment Use center centering to instead use a LaTeX centering directive or center none touse no centering This option was implemented by Markus J ntti markus jantti iki fi of Abo Akademi University set to TRUE to enclose the table ina Landscape environment When ctable is TRUE will use the rotate argument to ctable type inn out pb greek headings align halign helvetica hspace length width height prlog multicol nomargins Details latex The default uses the S Example environment for latex function assum ing you
120. to FALSE to suppress printing of iteration messages plotTrans set to TRUE to plot ace or avas transformations for each variable for each of the multiple imputations This is useful for determining whether transforma tions are reasonable If transformations are too noisy or have long flat sections resulting in lumps in the distribution of imputed values it may be advisable to place restrictions on the transformations monotonicity or linearity tolerance singularity criterion list the source code in the 1m fit qr bare function for details B number of bootstrap resamples to use if nk is a vector digits number of digits for printing nclass number of bins to use in drawing histogram datadensity see Ecdf diagnostics Specify diagnostics TRUE to draw plots of imputed values against sequen tial imputation numbers separately for each missing observations and variable maxn Maximum number of observations shown for diagnostics Default is maxn 10 which limits the number of observations plotted to at most the first 10 other arguments that are ignored Details The sequence of steps used by the aregImpute algorithm is the following 1 For each variable containing m NAs where m gt 0 initialize the NAs to values from a random sample without replacement if a sufficient number of non missing values exist of size m from the non missing values 2 For burnintn impute iterations do the following steps The first burnin iter
121. type c chars width Arguments string vector of strings type character whether to count characters or screen columns string break line 261 Value rows vector containing the number of character rows in each string columns vector containing the maximum number of character columns in each string Note compatable with Splus string bounding box Author s Charles Dupont See Also nchar stringDims Examples a lt c this is a single line string This is a nmulty line string stringDims a string break line Breaka String into Many Lines at Newlines Description Takes a string and breaks it into seperate substrings where there are newline characters Usage string break line string Arguments string character vector to be separated into many lines Value Returns a list that is the same length of as the st ring argument Each list element is a character vector Each character vectors elements are the split lines of the corresponding element in the string argument vector Author s Charles Dupont 262 stringDims See Also Streplit Examples a lt c this is a single line string This is a nmulti line string b lt string break line a stringDims String Dimentions Description Finds the height and width of all the string in a character vector Usage stringDims string Arguments string vector of strings Details stringDims finds
122. unless you really need to avoid conflicts with variables in search position 1 that have the same names as the variables in FEV Attaching into search position 1 will cause S Plus to be more of a memory hog attach FEV Use e g attach FEV Cs age sex if you only want to analyze a small subset of the variables Use e g attach FEV FEVSsex male to analyze a subset of the observations summary height age sex fun function y c smean sd y smedian hilow y conf int 5 fit lt lIm height agexsex Run generic summary function on height and fev stratified by sex by data frame height fev sex summary Cross classify into 4 sex x smoke groups by FEV list sex smoke summary Plot 5 quantiles s lt summary fev age sex height fun function y quantile y c 1 25 5 75 9 plot s which 1 5 pch c 1 2 15 2 1 pch c o main A Discovery xlab FEV Use the nonparametric bootstrap to compute a 0 95 confidence interval for the population mean fev smean cl boot fev in Hmisc Use the Statistics Compare Samples One Sample keys to get a normal theory based C I Then do it more manually The following method assumes that there are no NAs in fev sd lt sqrt var fev xbar lt mean fev xbar sd n lt length fev qt 975 n 1 prints 0 975 critical value of t dist with n 1 d f xbar
123. using nice defaults for presentations slides publications wtd mean wtd var wtd quantile wtd Ecdf wtd table wtd rank wtd loess noiter num denom setup zoom Set of function for obtaining weighted estimates Zoom in on any graphical display Bill Dunlap bill statsci com Copyright Notice GENERAL DISCLAIMER This program is free software you can redistribute it and or modify it under the terms of the GNU General Public License as published by the Free Software Foundation either version 2 or at your option any later version This program is distributed in the hope that it will be useful but WITHOUT ANY WARRANTY without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 126 hoeffd PURPOSE See the GNU General Public License for more details In short You may use it any way you like as long as you don t charge money for it remove this notice or hold anyone liable for its results Also please acknowledge the source and communicate changes to the author If this software is used is work presented for publication kindly reference it using for example Harrell FE 2004 Hmisc S function library Programs available from http biostat mc vanderbilt edu s Hmisc Be sure to reference S Plus or R itself and other libraries used Acknowledgements This work was supported by grants from the Agency for Health Care Policy and Research US Public Health Service and the Robert Wood Johnson Found
124. varclus 314 Topic nonlinear transace 283 Topic nonparametric biVar 19 bootkm 22 bpplot 25 cut2 47 describe 60 Ecdf 69 hoef fd 123 panel bpplot 178 plsmo 184 340 rcorr 198 rceorr cens 199 rcorrp cens 202 smean sd 244 somers2 246 transace 283 wtd stats 319 xy group 323 Topic print equalBins 74 format pval 103 print char list 188 print char matrix 190 prnz 191 simplifyDims 243 string bounding box 257 string break line 258 stringDims 259 Topic programming data frame create modify check 49 dropUnusedLevel1s 68 escapeRegex 77 Misc 168 requirePackage 212 sro 253 Topic regression abs error pred 2 areg 5 aregImpute 9 na detail response 174 rceorrp cens 202 respline eval 204 respline plot 205 respline restate 207 redun 209 rm boot 218 transace 283 transcan 291 Topic robust abs error pred 2 describe 60 wtd stats 319 Topic smooth areg 5 aregImpute 9 plsmo 184 rcspline eval 204 INDEX redun 209 transace 283 transcan 291 wtd stats 319 Topic survival bootkm 22 ciapower 30 cpower 37 event chart 78 event convert 88 event history 89 reorr cens 199 rcorrp cens 202 spower 247 Topic univar hdquantile 113 Topic utilities combine 34 Cs 39 dropUnusedLevel1s 68 eip 73 Format df 100 html 125 inc dec 128 label 138 latex 142 Misc 168 nstr 177 require
125. variable as a dependent variable NA values on that variable cause all observations to be temporarily deleted Once a new working transformation is found for the variable along with a model to predict that transformation from all the other variables that latter model is used to impute NA values in the selected dependent variable if imp con is not specified When that variable is used to predict a new dependent variable the current working imputed values are inserted Trans formations are updated after each variable becomes a dependent variable so the order of variables on x could conceivably make a difference in the final estimates For obtaining out of sample predic tions transformations predict uses the same iterative procedure as t ranscan for imputation with the same starting values for fill ins as were used by transcan It also by default uses a conservative approach of curtailing transformed variables to be within the range of the original ones Even when method pc is specified canonical variables are used for imputing missing values Note that fitted transformations when evaluated at imputed variable values on the original scale will not precisely match the transformed imputed values returned in xt This is because transcan uses an approximate method based on linear interpolation to back solve for imputed values on the original scale Shrinkage uses the method of Van Houwelingen and Le Cessie 1990 similar to Copas 1983 T
126. width of horizontal end pieces for error bars as a fraction of the length of the x axis lty bar line type for bars lwd lty pch cex font col see trellis args These are vectors when groups is present and the order of their elements corresponds to the different groups regardless of how many bands or bars are drawn If you don t specify 1t y bands for example all band lines within each group will have the same lty lty bands lwd bands col bands used to allow lt y lwd col to vary across the different band lines for different groups These parameters are vectors or lists whose elements correspond to the added band lines i e they ignore the central line whose line characteristics are defined by lt y lwd col For example suppose that 4 lines are drawn in addition to the central line Specifying lwd bands 1 4 will cause line xYplot minor ticks col fill size rangeCex strip blank 331 widths of 1 4 to be used for every group regardless of the value of 1wd To vary characteristics over the groups use e g lwd bands list rep 1 4 rep 2 4 orlist c 1 2 1 2 c 3 4 3 4 a list with elements at and labels specifying positions and labels for minor tick marks to be used on the x axis of each panel if any This is intended for timeDate variables used to override default colors used for the bands in method filled bands This is a vector when groups is present and the order of the elements cor re
127. 10 OR lt seq 2 9 by 1 plot 0 0 xlim range n ylim c 0 1 xlab n ylab Power type n for or in OR lines n bpower 1 odds ratio or n n text 350 bpower 1 odds ratio or n 350 02 format or Another way to plot the same curves but letting labcurve do the work including labeling each curve at points of maximum separation pow lt lapply OR function or n list x n y bpower pl 1 odds ratio or n n n n names pow lt format OR labcurve pow pl TRUE xlab n ylab Power Contour graph for various probabilities of outcome in the control group fixing the odds ratio at 8 p2 1 p2 pl 1 pl 8 n is varied also pl lt seq 01 99 by 01 n lt seq 100 5000 by 250 pow lt outer pl n function pl n bpower p1 n n odds ratio 8 This forms a length pl length n matrix of power estimates contour p1 n pow bpplot Box percentile plots bpplot 29 Description Producess side by side box percentile plots from several vectors or a list of vectors Usage bpplot name TRUE main Box Percentile Plot xlab ylab srtx 0 Arguments vectors or lists containing numeric components e g the output of split name character vector of names for the groups Default is TRUE to put names on the x axis Such names are taken from the data vectors or the names attribute of the first argument if it is a list Set name to F
128. 180 226 268 378 355 319 256 171 325 325 217 255 256 2 91323 2537 206 206 237 211 229 234 209 status lt o 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 24 0 0 rep 1 19 treatment lt c rep 1 10 rep 2 10 rep 1 10 rep 2 10 sex lt Cs F F M F M F F M M M F F M M M F M F F M M M M M F M M F F F M M M F F M F F F F data frame days status treatment sex table treatment status logrank Surv days status treatment agrees with p 1807 For stratified tests the picture is puzzling survdiff Surv days status treatment strata sex Schisq is 7 246562 which does not agree with SAS 7 1609 But summary coxph Surv days status treatment strata sex yields 7 16 whereas summary coxph Surv days status treatment yields 5 21 as the score test not agreeing with SAS or logrank 5 6485 End Not run spss get Enhanced Importing of SPSS Files Description spss get invokes the read spss function in the foreign package to read an SPSS file with a default output format of data frame The label function is used to attach labels to indi vidual variables instead of to the data frame as done by read spss By default integer valued variables are converted to a storage mode of integer unless force single FALSE Date vari ables are converted to R Date variables By default underscores in names are converted to periods Usage s
129. 1l TRUI areg boot x data weights subset na action na delete B 100 method c areg avas nk 4 evaluation 100 valrsq TRU probs c 25 5 75 tolerance NULL S3 method for class areg boot print x S3 method for class areg boot plot x ylim boot TRUE col boot 2 lwd boot 15 conf int 95 Gl transace smearingEst transEst inverseTrans res statistic c median quantile mean fitted lp q S3 method for class areg boot summary object conf int 95 values adj to statistic median q S3 method for class summary areg boot print x S3 method for class areg boot predict object newdata statistic c lp median quantile mean fitted terms g NULL S3 method for class areg boot Function object type c list individual ytype c transformed inverse prefix suffix frame 0 where 1 Mean object Quantile object S3 method for class areg boot Mean object evaluation 200 S3 method for class areg boot Quantile object gq 5 Arguments x for transace a numeric matrix For areg boot x is a formula For print or plot an object created by areg boot Forprint summary areg boot and object created by summary areg boot object an object created by areg boot or a model fit object suitable for Mean or Quantile
130. 1th Annual SAS Users Group Intnl Conf p 646 651 Cary NC SAS Institute Inc See Also ns rcspline restate rcs Examples x lt 1 100 rcespline eval x nk 4 inclx TRUE lrm fit rcspline eval age nk 4 inclx TRUE death rcespline plot Plot Restricted Cubic Spline Function Description Provides plots of the estimated restricted cubic spline function relating a single predictor to the re sponse for a logistic or Cox model The rcspline plot function does not allow for interactions as do 1rm and cph but it can provide detailed output for checking spline fits This function uses the rcspline eval lrm fit and Therneau s coxph fit functions and plots the estimated spline regression and confidence limits placing summary statistics on the graph If there are no adjustment variables rcspline plot can also plot two alternative estimates of the regression function when model logistic proportions or logit proportions on grouped data and a nonparametric estimate The nonparametric regression estimate is based on smoothing the binary responses and taking the logit transformation of the smoothed estimates if desired The smoothing uses supsmu Usage rcespline plot x y model c logistic cox ols xrange event nk 5 knots NULL show c xbeta prob adjJ NULL xlab ylab ylim plim c 0 1 plotcl TRUE showknots TRUE add FALSE subset lty 1 noprint FALSE m smooth FALSE bas main auto statloc
131. 2 1 5 alphaLower 01 alphaUpper 04 truncate 4 b plot b Compute differences in proportions and odds ratios needed to cross the boundaries given a mean probability in two treatment arms of 0 1 and given a vector of sample sizes per arm corresponding to the looks s lt summary b n seq 200 1000 by 200 pbar 1 s d lt s data plot d n dSor lower xlab N Per Arm ylab Critical Odds Ratio type b ylim range d or lower dSor upper log y lines d n dSor upper type b abline h 1 lty 2 End Not run list tree Pretty print the Structure of a Data Object Description This is a function to pretty print the structure of any data object usually a list It is similar to the R function str Usage list tree struct depth 1 numbers FALSE maxlen 22 maxcomp 12 attr print TRUE front fill name of size TRU Gl makeNstr Arguments struct depth numbers maxlen maxcomp attr print front 5 ca le name of size Author s 159 The object to be displayed Maximum depth of recursion of lists within lists to be printed negative value means no limit on depth If TRUE use numbers in leader instead of dots to represent position in structure Approximate maximum length in characters allowed on each line to give the first few values of a vector maxlen 0 suppresses printing any values Maximum number of components of any list that
132. 214 236 322 tapply 63 158 323 tex ps slide 192 texi2dvi 150 text 136 242 title 75 173 topdf ps slide 192 transace 283 transcan 7 14 128 211 291 translate 29 306 trap rule Misc 168 trellis device 193 196 trellis strip blank Misc 168 trunc POSIXt 307 under unix Misc 168 units 50 223 308 INDEX units lt default units 308 unix 173 282 unPaste Misc 168 upData 36 50 309 update 275 val prob 203 validate 289 validate ols 3 valueLabel valueTags 313 valueLabel lt valueTags 313 valueName valueTags 313 valueName lt valueTags 313 valuetags 313 valueTags lt valueTags 313 valueUnit valueTags 313 valueUnit lt valueTags 313 var 322 varclus 21 124 199 211 314 Varcov transcan 291 Varcov fit mult impute transcan 291 Varcov glm transcan 291 Varcov 1lm transcan 291 Varcov multinom transcan 291 Weibull2 spower 247 hichClosePW Misc 168 hichClosest Misc 168 td Ecdf 72 td Ecdf wtd stats 319 td loess noiter wtd stats 319 td mean wtd stats 319 td quantile wtd stats 319 td rank 247 td rank wtd stats 319 td stats 319 td table wtd stats 319 td var wtd stats 319 3z 5 333353385335345 84 xless Misc 168 xy group 323 xYplot 44 72 136 324 xyplot 44 186 324 325 329 yearDays 332 347
133. 23 xS test numeric string sedit 23xx Y_ test all digits replace substring wild abcdefabcdef d ef xy show pch 245 x lt abcd substring2 x bc lt BCX x substring2 x B ed lt BxD x show pch Display Colors Plotting Symbols and Symbol Numeric Equivalents Description show pch plots the definitions of the pch parameters show col plots definitions of integer valued colors character table draws numeric equivalents of all latin characters the charac ter on line xy and column z of the table has numeric code xyz which you would surround in quotes and preceed by a backslash Usage show pch object par font show col object NULL character table font 1 Arguments object font for show pch ignored for show col font font Author s Pierre Joyet pierre joyet bluewin ch Frank Harrell See Also points text Examples Not run show pch show col character table End Not run 246 simplifyDims simplifyDims List Simplification Description Takes a list where each element is a group of rows that have been spanned by a multirow row and combines it into one large matrix Usage simplifyDims x Arguments x list of spanned rows Details All rows must have the same number of columns This is used to format the list for printing Value a matrix that contains all of the spanned rows Author s Char
134. 25 975 a d lt quantile a d c 025 975 b d lt quantile b d c 025 975 tardar e le errbar c a b a b poe ae rege e cla 1 clb 1 cld 1 cla 1 clb 1 cla 1 cld 1 clb 1 cld 1 e cla 3 clb 3 cld 3 a b 2 a d 2 b a 2 e cla 2 clb 2 cld 2 a b 1 a d 1 b d 1 eee ala ye eee AN re ylab 80 escapeRegex escapeRegex Escapes any characters that would have special meaning in a reqular expression Description Escapes any characters that would have special meaning in a reqular expression Usage escapeRegex string escapeBS string Arguments string string being operated on Details escapeRegex will escape any characters that would have special meaning in a reqular expression For any string grep regexpEscape string string will always be true escapeBS will escape any backslash backslash in a string Value The value of the string with any characters that would have special meaning in a reqular expression escaped Author s Charles Dupont Department of Biostatistics Vanderbilt University See Also grep Examples string lt this system is full escapeRegex string escapeBS string event chart 81 event chart Flexible Event Chart for Time to Event Data Description Creates an event chart on the current graphics device Also allows user to plot legend on plot area or on separate page Contains features useful for p
135. 3 method for class discrete x drop FALSE S3 method for class discrete x i is discrete x S3 replacement method for class discrete is na x lt value S3 replacement method for class discrete length x lt value Arguments x a vector drop Should unused levels be dropped exclude logical should NA be excluded i indexing vector levels charater list of individual level values value index of elements to set to NA arguments to be passed to other functions dotchart2 69 Details as discrete converts a vector into a discrete vector discrete creates a discrete vector from provided values is discrete tests to see if the vector is a discrete vector Value as discrete discrete returns a vector of discrete type GI is discrete returan logical TRUE if the vector is of class discrete other wise it returns FALS Author s Charles Dupont See Also TL Bacar Examples a lt discrete 1 25 a is discrete a b lt as discrete 2 4 b dotchart2 Enhanced Dot Chart Description dot chart2 is an enhanced version of the dot chart function with several new options Usage dotchart2 data labels groups NULL gdata NA horizontal TRUE pch 16 xlab ylab auxdata auxgdata NULL auxtitle lty if R 1 else 2 lines TRUE dotsize 8 cex par cex cex labels cex cex group labels cex labels 1 25 sort TRUE add F
136. 42 how pch 242 howPsfrag ps slide 192 implifyDims 243 mean cl boot smean sd 244 mean cl normal smean sd 244 mean sd 244 275 mean sdl smean sd 244 mearingEst transace 283 median hilow smean sd 244 solve 246 solvet 245 somers2 201 203 246 322 source 254 spearman biVar 19 spearman2 biVar 19 split 182 spower 31 38 247 spss get 252 Src 253 stata get 254 tore 256 tores store 256 toreTemp store 256 tr 156 trata 275 tratify summary formula 266 tring bounding box 257 259 tring break line 258 tringDims 74 258 259 tripplot 236 ANNAN HANAANANAAANHA ARN AN NH ANN HDHYWHAANAANAUHN YN YN INDEX strptime 41 42 311 312 strsplit 170 259 subplot 33 260 substring 24 substring location sedit 239 substring2 sedit 239 substring2 lt sedit 239 sum 238 summarize 245 261 275 329 summary 63 summary areg boot transace 283 summary data frame 50 summary find matches find matches 95 summary formula 50 245 266 summary impute impute 127 summary 1dBands ldBands 151 Summary mChoice mChoice 158 summary mChoice mChoice 158 summary transcan transcan 291 supsmu 186 206 223 Surv 23 63 170 202 203 survfit 23 survfit km 23 Survival cph 23 symbol freq 280 symbols 261 280 281 sys 281 system 282 t test 283 t test cluster 282 table 59 63 72
137. 63 8489 8554 8644 8713 8733 8854 8855 8863 8983 9035 9037 9132 9164 9186 9221 9224 9252 9274 9404 9405 9433 9434 9470 9470 9472 9489 9500 9585 9649 diffdate c 826 431 302 306 698 393 368 1124 191 124 330 642 408 244 1001 391 246 615 1118 539 612 245 813 516 1309 241 327 469 126 317 689 981 303 976 dethdate c 8434 8304 NA 8414 8715 NA 8667 9142 8731 8750 8963 9120 9005 9028 9445 9180 9189 9406 9711 9453 9465 9289 9640 9608 10010 9488 9523 9633 9667 9547 9755 NA 9686 10084 censdate c NA NA 8321 NA NA 8519 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 10095 NA NA viralload c 13000 36000 70000 90000 21000 110000 75000 12000 125000 110000 13000 39000 79000 135000 1400 42000 123000 20000 12000 18000 16000 140000 16000 58000 11000 120000 85000 31000 24000 11500 17000 13100 72000 13500 event chart cdcaids lt upData cdcaids labels c ag Age y infedate Date of blood transfusion diagdate Date of AIDS diagnosis diffdate Incubation period days from HIV to AIDS dethdate Fictitious date of death censdate Fictitious censoring date viralload Fictitious viral load Note that the style options listed with these xamples are best suited for output to a postscript file i e using the postscript function with horizontal TRUE as opposed to a graph
138. 832 8 1 trunc date units years 1832 1 1 round POSIXt date digits months 1832 7 1 units Units Attribute of a Vector Description Sets or retrieves the units attribute of an object For units default replaces the builtin version which only works for time series objects If the variable is also given a Label subsetting using Labelled will retain the units attribute For S Plus 6 which uses version 4 of the S language the latter does not work Usage nits x ses Default S3 method units x none Default S3 replacement method units x lt value Arguments x any object ignored value the units of the object or none value to which to set result if no appropriate attribute is found Value the units attribute of x if any otherwise the units attribute of the tspar attribute of x if any otherwise the value none 312 upData See Also label Examples fail time lt c 10 20 units fail time lt Day describe fail time label fail time lt Failure Time fail time Not run f lt cph Surv fail time event xx plot xx xx2 xlab paste label xx units xx s sep End Not run upData Update a Data Frame or Cleanup a Data Frame after Importing Description cleanup import will correct errors and shrink the size of data frames created by the S Plus File Import dialog or by other methods such as scan and read table By def
139. 998 Nonparametric estimation of covariance structure in logitudinal data Biometrics 54 401 415 Chapman IM Hartman ML et al 1997 Effect of aging on the sensitivity of growth hormone secretion to insulin like growth factor I negative feedback J Clin Endocrinol Metab 82 2996 3004 Li Y Wang YG 2008 Smooth bootstrap methods for analysis of longitudinal data Stat in Med 27 937 953 potential improvements to cluster bootstrap not implemented here See Also rcespline eval lm lowess supsmu bootcov units label polygon reShape Examples Generate multivariate normal responses with equal correlations 7 within subjects and no correlation between subjects Simulate realizations from a piecewise linear population time response profile with large subject effects and fit using a 6 knot spline Estimate the correlation structure from the residuals as a function of the absolute time differenc Function to generate n p variate normal variates with mean vector u and covariance matrix S Slight modification of function written by Bill Venables See also the built in function rmvnorm mvrnorm lt function n p 1 u rep 0 p S diag p Z lt matrix rnorm n p p n t u t chol S Z rm boot 227 n lt 20 Number of subjects sub lt 5x 1 n Subject effects Specify functional form for time trend and compute non stochastic component times lt seq 0 1 by 1 g lt func
140. AE Ely 2 3 4 NA q lt c 1 2 3745 5 hoeffd cbind x y Z q Hoeffding s test can detect even one to many dependency set seed 1 x lt seq 10 10 length 200 y x x sign runif 200 1 1 plot x y hoeffd x y html Convert an S object to HTML Description html is a generic function for which only two methods are currently implemented html latex and a rudimentary html data frame The former uses the HeVeA LaTeX to HTML translator by Maranget to create an HTML file from a LaTeX file like the one produced by latex The resulting HTML file may be displayed using a show or a print method The browser specified in options browser for R help browser for S Plus is launched to display the HTML file html default just runs html data frame Usage html object S3 method for class latex tml object file S3 method for class data frame tml object file paste first word deparse substitute object html sep append FALSE link NULL linkCol 1 linkType c href name Default S3 method html object file paste first word deparse substitute object html sep append FALSE link NULL linkCol 1 linkType c href name S3 method for class html PEIRE RI cas S3 method for class html show object T o y oe Arguments object a data frame or an object created by latex For show is an object
141. ALSE dotfont par font groupfont 2 reset par add xaxis TRUE width factor 1 1 lcolor if R gray else par col 70 Arguments data labels groups gdata horizontal pch xlab ylab auxdata auxgdata auxtitle lty lines dotsize Cex cex labels dotchart2 a numeric vector whose values are shown on the x axis a vector of labels for each point corresponding to x If omitted names data are used and if there are no names integers prefixed by are used an optional categorical variable indicating how data values are grouped data values for groups typically summaries such as group medians set to FALSE to make the chart vertical instead of the default default character number or value for plotting dots in dot charts The default is 16 x axis title y axis title a vector of auxiliary data given to dot chart 2 of the same length as the first data argument If present this vector of values will be printed outside the right margin of the dot chart Usually auxdata represents cell sizes similar to auxdata but corresponding to the gdata argument These usually represent overall sample sizes for each group of lines if auxdata is given auxtitle specifies a column heading for the extra printed data in the chart e g N line type for horizontal lines Default is 1 for R 2 for S Plus set to FALSE to suppress drawing of reference lines cex value for d
142. ALSE to suppress names If a character vector is supplied the names in the vector are used to label the groups main main title for the plot xlab x axis label ylab y axis label srtx rotation angle for x axis labels Default is zero Value There are no returned values Side Effects A plot is created on the current graphics device BACKGROUND Box percentile plots are similiar to boxplots except box percentile plots supply more information about the univariate distributions At any height the width of the irregular box is proportional to the percentile of that height up to the 50th percentile and above the 50th percentile the width is proportional to 100 minus the percentile Thus the width at any given height is proportional to the percent of observations that are more extreme in that direction As in boxplots the median 25th and 75th percentiles are marked with line segments across the box Author s Jeffrey Banfield umsfjban bill oscs montana edu Modified by F Harrell 30Jun97 References Esty WW Banfield J The box percentile plot J Statistical Software 8 No 17 2003 30 bystats See Also panel bpplot boxplot Ecdf bwplot Examples set seed 1 xl lt rnorm 500 x2 lt runif 500 2 2 x3 lt abs rnorm 500 2 bpplot xl x2 x3 g lt sample 1 2 500 replace TRUE bpplot split x2 g name c Group 1 Group 2 rm x1 x2 x3 g bystats Statistics by Categories Desc
143. As and x is a vector it is returned unchanged the name of a function to use in computing the single imputed value from the non NAs The default is median If instead of specifying a function as fun a single value or vector numeric or character if ob ject is a factor is spec ified those values are used for insertion fun can also be the character string random to draw random values for imputation with the random values not forced to be the same if there are multiple NAs For a vector of constants the vector must be of length one indicating the same value replaces all NAs or must be as long as the number of NAs in which case the values correspond to consecutive NAs to replace For a factor ob ject constants for imputation may include character values not in the current levels of object In that case new inc dec 131 levels are added If object is of class factor fun is ignored and the most frequent category is used for imputation object an object of class impute ignored Value a vector with class impute placed in front of existing classes For is imputed a vector of logical values is returned all TRUE if object is not of class impute Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also transcan impute transcan describe na include sample Examples age lt c 1 2 NA 4 age i lt impute age Could have used impute age 2 5
144. CK OES HEE 186 PISMO 2 Gnkbbied Awe eb bbe ea eee de baw bbGdebtaw gehen 187 POPOWER s wackis eae a gt Sas a ides Ws gy lay Be perk ohh he as laces ee gOS aa Gee dae ee oh 190 print eharlist eiie sae ea SOG Bo Bs he tare Hate Bl ee is Bike Be eh 191 print char MatIX o ce coe we AR A E ee ee 193 PNZ ayoa e A OR OR Re E Re Se e E RR ee e 194 pSishide sai gaens e Fae Bos ah os CPt ie Ge Te Ewa Se 4 eh Ee Oe TE 195 PSAP s 64 PEARS OA EE SA eee EHR RES Dem ee es 4 200 TEOT sugo ew Ae eH eM BAe SOR eG BR ea He oe eg ee wr 201 FCOUM CCHS 3442 6 67 x Si 4 a Odie OMe hediasS eee owkts eee he edad 202 TCOMP CENS e woe be Heed he ee BERS ee Da oe ee 205 resplinesevall es s e eee PRE CS AER RES ER See Ye ee 8 207 respline plot s e er ae Sede eo ec ba we ew EPS oe eed Sas 208 respline restat 2k eek ee bebe DA HEA EEA SEH E dG ha ee 210 TEMUD see coke E a A Ke A eee Oe Ye See EERE ee eo 212 TEOrder factor sosea a Ba ae Be GS Bao eG Bk SA BU eg 214 requirePackave cup toe A a Re 215 TESHAPE Sot oe gs Seek aS we A A Ee ee ee ee OF 216 legend peona PERO wo a ee Ke wep Re ak Ae hee Sew deal args 2 219 TODO kA AA he A OAR eRe haw ae 221 ENINA 3 5 scar ge Be setae n e a A a a E e a A e ethe a a E E E a A 228 Index R topics documented Samplesize DIM a s ao e oe ee ew ae ES Ge EA ee a ea 229 SASXPOIL Set fbr ee se AY ee a Ge eS ee RR ee 230 Save 65 bee bte cha weeudewged aids se beh da woh Se eo ae 233 SCatld 424 2645 eae Redes Sawa e
145. Compare jackknife standard errors with those from the bootstrap library boot boot x function x i hdquantile x i probs 1 3 4 R 400 End Not run hist data frame Histograms for Variables in a Data Frame Description This functions tries to compute the maximum number of histograms that will fit on one page then it draws a matrix of histograms If there are more qualifying variables than will fit on a page the function waits for a mouse click before drawing the next page Usage S3 method for class data frame hist x n unique 3 nclass compute na big FALSE rugs FALSE mtitl FALSE For S Plus you must use hist data frame as hist is not generic there Gl T Arguments x a data frame n unique minimum number of unique values a variable must have before a histogram is drawn 118 histbackback nclass number of bins Default is max 2 trunc min n 10 25 log n 10 2 where n is the number of non missing values for a variable na big set to TRUE to draw the number of missing values on the top of the histogram in addition to in a subtitle In the subtitle n is the number of non missing values and m is the number of missing values rugs set to TRUE to add rug plots at the top of each histogram mtitl set to a character string to set aside extra outside top margin and to use the string for an overall title arguments passed to scat1d Value the number of pages drawn
146. Device area coordinates range from 0 to 1 with 0 corresponding to the bottom and left of the device region within the outer margins and 1 is the top and right of the region withing the outer margins If the outer margins are all set to 0 then tdev and dev should be identical tdev Total Device Total Device area coordinates range from 0 to 1 with 0 corresponding to the bottom and left edges of the device piece of paper window on screen and corresponds to the top and right edges Value A list with 5 components each component is a list with vectors named x and y The 5 sublists are usr The coordinates of the input points in usr User coordinates plt The coordinates of the input points in plt Plot coordinates fig The coordinates of the input points in fig Figure coordinates dev The coordinates of the input points in dev Device coordinates tdev The coordinates of the input points in tdev Total Device coordinates Note You must provide both x and y but one of them may be NA This function is becoming depricated with the new functions grconvertX and grconvertyY in R version 2 7 0 and beyond These new functions use the correct coordinate system names and have more coordinate systems available you should start using them instead Author s Greg Snow greg snow imail org 36 cnvrt coords See Also par specifically usr plt and fig Also xpd for plotting outside of the plotting region and
147. E End Not run ldBands Group Sequential Boundaries using the Lan DeMets Approach Description This function computes and plots group sequential stopping boundaries from the Lan DeMets method with a variety of a spending functions using the 1d98 program from the Department of Biostatistics University of Wisconsin written by DM Reboussin DL DeMets KM Kim and KKG IdBands 155 Lan Such stopping boundaries are useful for early termination of clinical trials for safety prob lems or for efficacy Simple plot and print methods are implemented Simple sample size and minimally detectable effect sizes given sample sizes may be obtained with a summary method if power was specified to 1dBands Alternatively summary computes for each look the differ ence in means that must be achieved to cross a boundary if n and sd are specified or the minimum difference in proportions and the odds ratios that must be achieved to cross a boundary if n and pbar are specified Usage ldBands n length times times NULL alpha 0 05 sided 2 alphaLower alpha 2 alphaUpper alpha 2 information NULL spending c OBrien Fleming Pocock alphaxt phi S3 method print x Stee S3 method phi 1 spending2 c OBrien Fleming Pocock alphaxt phi Hwang Shih DeCani Hwang Shih DeCani phi2 phi truncate Inf power NULL pr TRUE for class ldBands for class ldBands
148. E 2004 Statistical tables and plots using S and LaTeX Document available from http bpiostat mc vanderbilt edu twiki pub Main StatReport summary pdf See Also mChoice smean sd summarize label strata dotchart2 print char matrix update formula cut2 llist format default latex latexTranslate bpplt Examples options digits 3 set seed 173 sex lt factor sample c m f 500 rep TRUE age lt rnorm 500 50 5 treatment lt factor sample c Drug Placebo 500 rep TRUE Generate a 3 choice variable each of 3 variables has 5 possible levels symp lt c Headache Stomach Ache Hangnail Muscle Ache Depressed symptoml lt sample symp 500 TRUE symptom2 lt sample symp 500 TRUE symptom3 lt sample symp 500 TRUE Symptoms lt mChoice symptoml symptom2 symptom3 label Primary Symptoms table Symptoms Note In this example some subjects have the same symptom checked multiple times in practice these redundant selections would be NAs mChoice will ignore these redundant selections Frequency table sex xtreatment sex Symptoms summary sex treatment Symptoms fun table could also do summary sex treatment mChoice symptoml symptom2 symptom3 fun table Compute mean age separately by 3 variables summary age sex treatment Symptoms f lt summary treatment age sex Symptoms method reverse
149. Example 1 large sample size much missing data no overlap in NAs across variables xl lt factor sample c a b c 1000 TRUE x2 lt xl b 3 xl c rnorm 1000 0 2 x3 lt rnorm 1000 y lt x2 1 x1 c 2 x3 rnorm 1000 0 2 orig xl lt x1 1 250 orig x2 lt x2 251 350 18 aregImpute x1 1 250 lt NA x2 251 350 lt NA d lt data frame x1l x2 x3 y Find value of nk that yields best validating imputation models tlinear FALSE means to not force the target variable to be linear f lt aregImpute y x1 x2 x3 nk c 0 3 5 tlinear FALSE data d B 10 normally B 75 Try forcing target variable x1 then x2 to be linear while allowing predictors to be nonlinear could also say tlinear TRUE lt aregImpute y x1 x2 x3 nk c 0 3 5 data d B 10 Fh Fh SE SE Oh Use 100 imputations to better check against individual true values f lt aregImpute y x1 x2 x3 n impute 100 data d f par mfrow c 2 1 plot f modecat lt function u tab lt table u as numeric names tab tab max tab 1 table orig xl apply fSimputed xl 1 modecat par mfrow c 1 1 plot orig x2 apply fSimputed x2 1 mean fmi lt fit mult impute y xl x2 x3 lm f data d sqrt diag Varcov fmi fcc lt Im y xl x2 x3 summary fcc SES are larger than from mult imputation Example 2 Very
150. Faris PD Ghali WA et al Multiple imputation versus data enhancement for dealing with missing data in observational health care outcome analyses J Clin Epidem 55 184 191 2002 See Also aregImpute impute naclus naplot ace avas cancor prcomp rcspline eval lsfit approx datadensity mice Examples Not run x lt cbind age disease blood pressure pH cbind will convert factor object disease to integer par mfrow c 2 2 x trans lt transcan x categorical disease asis pH transformed TRUE imputed TRUE summary x trans Summary distribution of imputed values and R squares f lt lm y x transStransformed use transformed values in a regression Now replace NAs in original variables with imputed values if not using transformations age lt impute x trans age disease lt impute x trans disease blood pressure lt impute x trans blood pressure pH lt impute x trans pH Do impute x trans to impute all variables storing new variables under the old names summary pH uses summary impute to tell about imputations and summary default to tell about pH overall Get transformed and imputed values on some new data frame xnew newx trans lt predict x trans xnew w lt predict x trans xnew type original age lt w age inserts imputed values blood pressure lt w blood pressure Function x trans creates age disease blood pressure pH
151. Hmisc load silently issue opt ions Hverbose FALSE before library Hmisc Functions Function Name abs error pred Purpose Computes various indexes of predictive accuracy based on absolute errors for linear models all is numeric Check if character strings are legal numerics approxExtrap Linear extrapolation aregImpute Multiple imputation based on additive regression bootstrapping and predictive mean matching areg boot Nonparametrically estimate transformations for both sides of a multiple additive regression and bootstrap these estimates and R ballocation Optimum sample allocations in 2 sample proportion test binconf Exact confidence limits for a proportion and more accurate narrower score stat based Wilson interval Rollin Brant mod FEH bootkm Bootstrap Kaplan Meier survival or quantile estimates bpower Approximate power of 2 sided test for 2 proportions Includes bpower sim for exact power by simulation bpplot Box Percentile plot Jeffrey Banfield umsfjban bill oscs montana edu bsamsize Sample size requirements for test of 2 proportions bystats Statistics on a single variable by levels of gt 1 factors bystats2 2 way Statistics calltree Calling tree of functions David Lubinsky david hoqax att com character table Shows numeric equivalents of all latin characters HmiscOverview ciapower cleanup import combine levels comment confbar contents cpower Cs csv get cut2 datadensity dataRep
152. NAs are allowed When a variable has any NAs transformed scores for that variable are imputed using least squares multiple regression incorporating optimum transformations or NAs are optionally set to constants Shrink age can be used to safeguard against overfitting when imputing Optionally imputed values on the original scale are also computed and returned For this purpose recursive partitioning or multino mial logistic models can optionally be used to impute categorical variables using what is predicted to be the most probable category By default transcan imputes NAs with best guess expected values of transformed variables back transformed to the original scale Values thus imputed are most like conditional medians as suming the transformations make variables distributions symmetric imputed values are similar to condition modes for categorical variables By instead specifying n impute transcan does approximate multiple imputation from the distribution of each variable conditional on all other variables This is done by sampling n impute residuals from the transformed variable with replacement a la bootstrapping or by default using Rubin s approximate Bayesian boot strap where a sample of size n with replacement is selected from the residuals on n non missing transcan 295 values of the target variable and then a sample of size m with replacement is chosen from this sample where m is the number of missing values ne
153. NULL xscale NULL prepanel Dotplot x y panel Dotplot x y groups NULL pch dot symbolSpch col dot symbolScol cex dot symbolScex font dot symbolSfont abline setTrellis strip blank TRUE lty dot line 2 lwd dot line 1 numericScale x label NULL skip weekends FALS Gl lt Arguments for Cbind is any number of additional numeric vectors Unless you are using Dotplot which allows for either 2 or 4 other variables or xYplot with method bands vectors after the first two are ignored If drawing bars and only one extra variable is given in upper and lower values are computed as described above If the second argument to Cbind is a matrix that matrix is stored in the other attribute and arguments after the second are ignored For bubble plots name an argument cex Also can be other arguments to pass to labcurve or extra arguments sent from numericScale to axis time formula a trellis formula consistent with xyplot or dotplot X x axis variable For numericScale xis any vector such as as numeric x returns a numeric vector suitable for x or y coordinates y a vector or an object created by Cbind for xYplot y represents the main variable to plot i e the variable used to draw the main lines For Dotplot the first argument to Cbind will be the main x axis variable data subset ylim subscripts 330 xYplot groups type scales pan
154. P environment and the letters 1 r and c are valid values When format df is called by SAS the col just is used to determine whether a is needed on the input line of the sysin file and the letters 1 and r are valid values When x is a data frame containing a matrix so that new column names are constructed from the name of the matrix object and the names of the individual columns of the matrix mat rix sep specifies the character to use to separate object names from individual column names specifies ranges of exponents or a logical vector specifying values not to con vert to scientific notation See format default for details should escaping backslashes be themselves escaped other arguments are accepted and ignored For latexVerbat im these argu ments are passed to the print function a character matrix with character images of properly rounded x Matrix components of input x are now just sets of columns of character matrix Object attribute col just repeats the value of the argument col just when provided otherwise it includes the recommended justification for columns of output See the discussion of the argument col just The default justification is 1 for characters and factors e r for numeric When dcolumn TRUE numerics will have as the justification character Author s Frank E Harrell Jr Department of Biostatistics Vanderbilt Univer
155. Package Hmisc April 29 2009 Version 3 6 0 Date 2009 04 27 Title Harrell Miscellaneous Author Frank E Harrell Jr lt f harrell vanderbilt edu gt with contributions from many other users Maintainer Charles Dupont lt charles dupont vanderbilt edu gt Depends R gt 2 4 0 methods Imports lattice cluster survival Suggests lattice grid nnet foreign chron acepack TeachingDemos Design cluster Description The Hmisc library contains many functions useful for data analysis high level graphics utility operations functions for computing sample size and power importing datasets imputing missing values advanced table making variable clustering character string manipulation conversion of S objects to LaTeX code and recoding variables Please submit bug reports to http biostat mc vanderbilt edu trac Hmisc License GPL gt 2 LazyLoad Yes URL http biostat mc vanderbilt edu s Hmisc http biostat mc vanderbilt edu twiki pub Main RS sintro pdf http biostat mc vanderbilt edu twiki pub Main StatReport summary pdf http biostat mc vanderbilt edu trac Hmisc Repository CRAN Date Publication 2009 04 29 17 54 42 2 R topics documented R topics documented ABSE PICO oe YBa ob a ee RR ee ES PO ee Oa a ce ee A 4 Allis MAMET ies eg BR eR a BA pee BE RS Ee oh eS BAO ph a 6 apProxExtrap 464 2005 Poh ba bebe SAGE SEEMS MEER REE HAS 7 AER asec We Rh a AS ew SR i ae Se a a Be ae a 8 areslMpule gnc
156. Package 212 Save 230 src 253 trunc POSIXt 307 units 308 valuetags 313 yearDays 332 Last 257 R Misc 168 SV4 Misc 168 66 Cbind xYplot 324 describe describe 60 discrete discrete 65 factor 69 impute impute 127 Labelled label 138 mChoice mChoice 158 coundN dataRep 57 summary formula response INDEX summary formula 266 transcan transcan 291 discrete discrete 65 abbreviate 160 abline 136 abs error pred 2 ace 7 289 303 all digits sedit 239 all is numeric 3 any 238 apply 97 approx 4 136 303 approxExtrap 4 areg 5 14 209 211 283 areg boot transace 283 aregImpute 9 303 as character mChoice mChoice 158 as data frame labelled label 138 as discrete discrete 65 as double mChoice mChoice 158 as numeric 3 as vector 214 asNumericMatrix 158 asNumericMatrix summarize 261 assign 257 attach 50 257 attributes 314 avas 283 289 303 axis 164 168 ballocation bpower 23 bezier labcurve 129 binconf 17 25 biVar 19 200 201 bootcov 60 223 bootkm 22 boxplot 27 bpower 23 38 188 250 bpplot 25 180 bpplt 275 bpplt panel bpplot 178 bsamsize bpower 23 341 bwplot 27 by 158 263 bystats 27 bystats2 bystats 27 cancor 7 303 capitalize 29 casefold 50 cat 192 Cbhind xYplot 324 ceil trunc POSIXt 307 characte
157. RUE xl lt runif 20 x2 lt runif 20 x2 1 4 lt NA y lt rnorm 20 describe y x1 x2 nstr Creates a string of arbitry length Description Creates a vector of strings which consists of the string segment given in each element of the string vector repeated times Usage nstr string times Arguments string character vector of string segments to be repeated Will be recycled if argument times is longer times integer vector of number of times to repeat the corisponding segment Will be recycled if argument st ring is longer Value returns a character vector the same length as the longest of the two arguments Note Will throw a warning if the length of the longer argment is not a even multiple of the shorter argument Author s Charles Dupont See Also paste rep panel bpplot 181 Examples nstr c a c 0 3 4 nstere a Th Toti ex 2 3 nstr c a b c 4 panel bpplot Box Percentile Panel Function for Trellis Description For all their good points box plots have a high ink information ratio in that they mainly display 3 quartiles Many practitioners have found that the outer values are difficult to explain to non statisticians and many feel that the notion of outliers is too dependent on false expectations that data distributions should be Gaussian panel bpplot is a panel function for use with trellis especially for bwplot It draws box plots wit
158. SE SST measure is the mean or median absolute Yhat Y divided by the mean or median absolute Y median Y Usage abs error pred fit lp NULL y NULL S3 method for class abs error pred print x Arguments fit a fit object typically from 1m or o1s that contains a y vector i e you should have specified y TRUE to the fitting function unless the y argument is given to abs error pred If you do not specify the 1p argument fit must contain fitted values or linear predictors You must specify fit or both of lp and y lp a vector of predicted values Y hat above if fit is not given y a vector of response variable values if fit with y TRUE in effect is not given x an object created by abs error pred unused Value a list of class abs error pred used by print abs error pred containing two matri ces differences and ratios Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu 6 all is numeric References Schemper M 2003 Stat in Med 22 2299 2308 Tian L Cai T Goetghebeur E Wei LJ 2007 Biometrika 94 297 311 See Also Im ols cor validate ols Examples set seed 1 SO can regenerate results xl lt rnorm 100 x2 lt rnorm 100 y lt exp x1l x2 rnorm 100 f lt lm log y x1 poly x2 3 y TRUE abs error pred lp exp fitted f y y rm x1 x2 y f all is numeric Check if All Elements in Character
159. THANSCAM 3 525 ges e a oh i a ee SAA ee oa Se ba eee 294 translate e aa Wow Bao ee a Ee ewe Se Sa eae al Ee ew eee 309 trune POSIX 42485 hed e baw ee cd yee ee eee dee PSR ee eR oS 310 UNIS y tents Oho ee Gee Bo Se GR Ge bore hod ee Wb aoe ed BS Bh hee bh a 311 UpDalars si pee e aE ee ine ae Sd eG i BAS aed doe ee ae Beets 312 Valuclags 22 4008 he an Peewee cd he eee beng eho eee bata es 316 VANCIUS 5 yrs ache oats Giese ete RoR cw Be ce Berd oh wi ee Ges eds Se OS Ge Bey Se eh wl 317 WU SIAIS lt 2 20 4 Mice ae GS Bane Bed bk So ON See AS wae ee ees 322 KVOTO ca ea ER RO OR ew Rea ee ee ee a 326 RY POU k con Gow se BAe eee ew RAS Soe oe BA Be 4k ee ee gies 327 yearDays sce ORAS a a e n ae OR RPE EOE Re ee 335 TOMI 04 aen a ee Maal ee Ae Pa Se Sak GAG Oe ee Ae ae Gee 336 337 abs error pred 5 abs error pred Indexes of Absolute Prediction Error for Linear Models Description Computes the mean and median of various absolute errors related to ordinary multiple regression models The mean and median absolute errors correspond to the mean square due to regression error and total The absolute errors computed are derived from Yhat median Yhat Yhat Y and Y median Y The function also computes ratios that correspond to Rsquare and 1 Rsquare but these ratios do not add to 1 0 the Rsquare measure is the ratio of mean or median absolute Yhat median Yhat to the mean or median absolute Y median Y The 1 Rsquare or S
160. Tag lt is a replacement function for all of the value s infor mation valueName is an accessor and valueName lt is a replacement function for the value s name This name is used when a plot or a latex table needs a short name and the variable name is not useful valueLabel is an accessor and valueLabel lt is a replacement function for the value s label The label is used in a plots or latex tables when they need a descriptive name valueUnit is an accessor and valueUnit lt is a replacement function for the value s unit The unit is used to add unit information to the R output Value valuetTag returns NULL or a named list with each of the named values name label unit set if they exists in the object varclus 317 For valueTag lt returns list For valueName valueLable and valueUnit returns NULL or character vector of length 1 For valueName lt valueLabel lt and valueUnit returns value Author s Charles Dupont See Also names attributes Examples age lt c 21 65 43 y lt 1 3 valueLabel age lt Age in Years plot age y xlab valueLabel age xl lt 1 10 x2 lt 10 1 valueLabel x2 lt Label for x2 valueUnit x2 lt mmHg x2 x2 1 5 dframe lt data frame x1 x2 Label dframe In these examples of llist note that labels are printed after Variable names because of print labelled a lt 1 3 b lt 4 6 valueLab
161. ULL drop NULL as is 0 5 FUN NULL sasdsLabels file Arguments file force single method formats allow out keep drop as is name of a file containing the SAS transport file file may be a URL beginning with http For sasdsLabels file is the name of a file containing a PROC CONTENTS output listing For method csv file is the name of the directory containing all the CSV files created by running the exportlib SAS macro set to FALSE to keep integer valued variables not exceeding 231 1 in value from being converted to integer storage mode set to dataload if you have the dat aload executable installed and want to use it instead of read xport This seems to correct some errors in which rarely some factor variables are always missing when read by read xport when in fact they have some non missing values a data frame or list like that created by read xport containing PROC FORMAT output if such output is not stored in the main transport file a vector of characters allowed by R that should not be converted to periods in variable names By default underscores in variable names are converted to periods as with R before version 1 9 a character string specifying a directory in which to write separate R save files rda files for each regular dataset Each file and the data frame inside it is named with the SAS dataset name translated to lower case and with underscores changed to periods
162. USA Europe year 1999 2001 reps 1 100 Generate binary events from a population probability of 0 2 of the event same for all years and continents d y lt ifelse runif 6 100 lt 2 1 0 s lt with d summarize y llist continent year function y n lt sum is na y s lt sum y na rm TRUE binconf s n type matrix Dotplot year Cbind y continent data s ylab Year xlab Probability Dotplot z x glxg2 2 way conditioning Dotplot z x gl groups g2 Key Key defines symbols for g2 If the data are organized so that the mean lower and upper confidence limits are in separate records the Hmisc reShape function is useful for assembling these 3 values as 3 variables a Single observation e g assuming type has values such as c Mean Lower Upper a lt reShape y id month colvar type This will make a matrix with 3 columns named Mean Lower Upper and with 1 3 as many rows as the original data yearDays Get Number of Days in Year or Month 336 nin Description Returns the number of days in a specific year or month Usage yearDays time monthDays time Arguments time A POSIXt or Date object describing the month or year in question Author s Charles Dupont See Also POSIAt Date snin Find Matching or Non Matching Elements Description nin amp is a binary operator which returns a logical vector in
163. a Time vector along with one of the following survival probabilities for either treat ment group and with or without contamination caused by non compliance hazard rates in a similar way intervention control hazard ratio function with and without contamination and dropin and dropout functions Logrank returns a single chi square statistic and Weibul12 Lognorm2 and Gompert z2 return an S function with three arguments only the first of which the vector of times is intended to be specified by the user Side Effects spower prints the interation number every 10 iterations if pr TRUI ea Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu References Lakatos E 1988 Sample sizes based on the log rank statistic in complex clinical trials Biometrics 44 229 241 Correction 44 923 Cuzick J Edwards R Segnan N 1997 Adjusting for non compliance and contamination in ran domized clinical trials Stat in Med 16 1017 1029 Cook T 2003 Methods for mid course corrections in clinical trials with survival outcomes Stat in Med 22 3431 3447 Barthel FMS Babiker A et al 2006 Evaluation of sample size and power for multi arm survival trials allowing for non uniform accrual non proportional hazards loss to follow up and cross over Stat in Med 25 2521 2542 See Also cpower Ciapower bpower cph coxph labcurve Examples Simulate a simple 2 arm c
164. a very flexible way e g the geometric mean 33rd percentile Kaplan Meier 2 year survival estimate mixtures of several statistics The default summary statistic for these methods is the mean the proportion of positive responses for a binary response variable The cross method is useful for creating data frames which contain summary statistics that are passed to trellis as raw data to make multi panel dot charts for example The print methods use the print char matrix function to print boxed tables if it is available it is included in S Plus versions 3 2 and later The right hand side of formula may contain mChoice multiple choice variables When test TRUE each choice is tested separately as a binary categorical response The plot method for method reverse creates a temporary function Key in frame 0 as is done by the xYplot and Ecdf formula functions After plot runs you can type Key to put a legend in a default location or e g Key locator 1 to draw a legend where you click the left mouse button This key is for categorical variables so to have the opportunity to put the key on the graph you will probably want to use the command plot object which categorical Note however that in Windows S Plus you can switch back and forth between multiple pages on a graph sheet and issue a Key or Key2 command according to which graph sheet page is active A second function Key2 is created if continuous variables are being
165. abcurve is through xYplot as shown in some of its examples Value labcurve returns an invisible list with components x y offset adj cex col and if tilt TRUE angle offset is the amount to add to y to draw a label offset is negative if the label is drawn below the line adj is a vector containing the values 0 5 1 largest empty returns a list with elements x and y specifying the coordinates of the center of the rectangle which was found Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also approx text legend setps scatld xYplot abline Examples n lt 2 8 m lt length n bype lt G T yy ALt Spe gt 1 s step function l ordinary line polygon curves lt vector list m plot 0 1 xlim c 0 1 ylim c 2 5 4 type n set seed 39 for i in 1 m x lt sort runit nli y lt rnorm n i lines x y lty i type type i col i curves i lt list x x y y labels lt paste Label for letters 1 m labcurve curves labels tilt TRUE type type col 1 m Put only single letters on curves at points of maximum space and use key to define the letters with automatic positioning of the key in the most empty 140 labcurve part of the plot Have labcurve do the plotting leaving extra space for key names curves lt labels labcurve curves keys letters l m type type col 1 m pl TRUE
166. abel of the object or object a data frame a list of variables or expressions to be formed into a list Ignored for print labelled file the name of a file to which to write S Plus source code Default is meaning standard output append set to TRUE to append code generated by Labe 1 to file file labels set to FALSE to make 11ist ignore the variables 1abe1 attribute and use the variables names Value nn label returns the label attribute of x if any otherwise label is used most often for the individual variables in data frames The function sas get copies labels over from SAS if they exist See Also sas get describe Examples age lt c 21 65 43 y lt 1 3 label age lt Age in Years plot age y xlab label age 144 data lt data frame age age label data self self label data lt label data RUE RUE x1 lt 1210 x2 lt 10 1 label x2 units x2 x2 2 1 5 dframe lt data frame xl Label dframe lt Label for x2 lt mmHg x2 In these examples of llist variable names becaus a lt 1 3 b lt 4 6 label b lt llist a b ER llist a b B Label r w lt llist a b gt 5 d 101 103 sapply w function x hist as numeric x locator 1 Or for u in w hist u title label Lag y y A data frame note that labels ar printed after of print labelle
167. ae 42 eee ee ee ee ee 4 oo eee eee Ee es as 12 DINCONE 2 5 5 doke Wane eb a e a aaa NE eee EPS eee ks 20 DINARS sae 2 ew Ae miy g a Vee ete eee e BAER YE OG ve he Se BES 2i DOOIKM egoi be Bee eed ee OR a he bee a ee ho E a E e ee 24 bpOWet soscat a he we eee Re wah bbe wb KR ee ee eh eked a 26 DPPlOt 8 4 a8 Sw Bee it He BE MEELS te 6 eee Eade 4 oe BE hs 28 DyStatS s eoi EGA A ee eA ee See ESAS RSE Ee DARE eS 30 capitalize ois g e VR Ee Oe EARS See SS Oe ee ee Ea See 32 CIAPOWET es asa pork hs SU A a aa a a a a Ges ee Re ace BB GE E 33 COVIL COOMS o i coe ew ea ee DA wae ee ee hae ee eae 34 COMDING s ane oF OE Se ae SA ee eR ae a ER a ee 37 CONTENTS eia daaa se tS heb ey he eee gee Eee he et aa SL Bete Re ade ae 38 POWER s 4 2 eee bbe d ee che eee ee bw se eA oe 2 a ae ead 40 Cies ee GORA RAE AS Chow EGR AER Ree eee ee ee he el 42 CSVIBEE para aos bon eR bw eee ee eg ee eo bee ne Re A ee ee ee ow A 43 CURVERCD 2 25 8 Reh eRe APS HE SHEE EE Ree ee Pee ea eS 45 CU wk GA EGE E Mee a baw Dee wee ed eee eae nee eh ee ad 50 data frame create modify check 2 2 a 51 dataRepy sos egos ba SSA eh eS SEE ME ROARS SOE AN EES ee a 59 OUD ge tes ep Sods chiar ah Usha Rs aoa nde e Moines eh Bed aS tt tes eas te es Meee ein ree chine 62 AESC De ceas ced ESR Ae hae Ree Ee EE ae eae eae 63 GISCTEIG y ariek i amp par WS eke es hw Behe amp Br S Be a A BR St ee ep a 67 dothan 4 2ci e ead eed we Be EAE Se GEMS ORES 4 oe EHS ELE rs 69 dropU
168. al to say the 0 95 quantile of the vector of B 1 objective function values For the coefficients satisfying this condition predicted curves are computed at the time grid and minima and maxima of these curves are computed separately at each time point to derive the final simultaneous confidence band By default the log likelihoods that are computed for obtaining the simultaneous confidence band assume independence within subject This will cause problems unless such log likelihoods have very high rank correlation with the log likelihood allowing for dependence To allow for correlation or to estimate the correlation function see the cor pattern argument below 222 rm boot Usage rm boot time y id seq along time subset plot individual FALSE bootstrap type c x fixed x random nk 6 knots B 500 smoother supsmu xlab xlim ylim range y times seq min time max time length 100 absorb subject effects FALSE rho 0 cor pattern c independent estimate ncor 10000 s S3 method for class rm boot plot x obj2 conf int 95 xlab x xlab ylab xSylab xlim ylim xSylim individual boot FALSE pointwise band FALSE curves in simultaneous band FALSE col pointwise band 2 objective c 2 log L sse dep 2 log L add FALSE ncurves multi FALSE multi method c color density multi conf HO C0591 eee Bip 4g cp Oy ep ORs Op gs 99 y multi density c 1 90 80 70 60
169. ality of Life Score label is in Hmisc library attr y label lt Quality will be y axis label can also specify Cbind Quality of Life Score y lower upper xYplot Cbind y lower upper month groups sex subset continent Europe method alt bars offset if R unit offset passed to labcurve to label using grid lattice for R unit function e g offset unit 1 inches The following offset unit 4 native or unit 05 npc uses th 1 inches else 4 offset is specified xampl set seed 111 dfr lt expand grid month 1 12 year c 1997 1998 month lt dfr Smonth year lt dfrSyear y lt abs month 6 5 s lt summarize y xYplot Cbind y Lower Upper keys lines 2xrunif length month llist month year Can also do s lt summarize y method alt llist month year stat name c month groups year type b quantile y Q1 Q3 type b 4 y units away from curve probs c 5 25 data dfr using the grid or summarize function in Hmisc to compute the median and outer quartiles displayed using error bars The outer quartiles are reps 1 100 year 1997 smedian hilow conf int 5 data s Zor xYplot Cbind y Q1 Q3 month groups year data s type b keys lines Or xYplot y month groups year keys lines nx FALSE method quantile
170. all the independent variables This is typically used to make tables comparing baseline variables by treatment group for example s5 lt summary drug bili albumin stage protime sex 282 summary formula age spiders method reverse TO summarize all variables use summary drug data pbc TO summarize all variables with no stratification use summary atbt c or summary data options digits 1 print s5 npct both npct both print both numerators and denominators plot s5 which categorical Key locator 1 draw legend at mouse click par oma c 3 0 0 0 leave outer margin at bottom plot s5 which continuous Key2 draw legend at lower left corner of plot oma above makes this default key fit the page better options digits 3 w lt latex s5 npct both here TRUE creates s5 tex Turn to a different dataset and do cross classifications on possibly more than one independent variable The summary function with method cross produces a data frame containing the cross classifications This data frame is suitable for multi panel trellis displays although summarize works better for that attach prostate size quartile lt cut2 sz g 4 bone lt factor bm labels c no mets bone mets s7 lt summary ap gt 1 size quartile bone method cross In this case quartiles are the default so could have said sz bone op
171. ally so if the matrix is of a form where you would not want to print the column names the column information being in the rows at the beginning of the matrix Row names if not specified in the making of the matrix will simply be numbers To prevent printing them set row names FALSE Value No value is returned The matrix or dataframe will be printed to file or to the screen Author s Patrick Connolly p connolly hortresearch co nz See Also write write tabl Examples data HairEyeColor print char matrix HairEyeColor Male col names TRUE print char matrix HairEyeColor Female col txt align left col names TRUE z lt rbind e N My c 1 34 40 3 n 40 30 48 5 n 48 49 58 4 n 58 44 87 8 50 n 50 n 50 n 50 0 530 n0 489 n0 514 n0 507 c female nmale 94 n106 0 552 n0 473 OTe y M200 VO S10 dimnames z lt list c age sex Overall NULL print char matrix z prnz Print and Object with its Name Description Prints an object with its name and with an optional descriptive text string This is useful for anno tating analysis output files and for debugging Usage prn x txt ps slide 195 Arguments x any object txt optional text string Side Effects prints See Also print cat Examples x lt 1 5 prn x prn fit Full Model Fit ps slide Postscript and Adobe PDF Setup for 35mm
172. alpha 0 05 Arguments mean prior mean of the prior distribution cut prior cut prob prior var prior variance of the prior Use a large number such as 10000 to effectively use a flat noninformative prior Sometimes it is useful to compute the variance so that the prior probability that st at is greater than some impressive value u is only alpha The correct var prior touseis then u mean prior qnorm 1 alpha 2 You can specify cut prior uand cut prob prior alpha whose default is 0 025 in place of var prior to have gbayes compute the prior variance in this manner m1 sample size in group 1 m2 sample size in group 2 stat statistic comparing groups 1 and 2 e g log hazard ratio difference in means difference in angular transformations of proportions var stat variance of stat assumed to be known var stat should either be a con stant allowed if n1 is not specified or a function of two arguments which specify the sample sizes in groups 1 and 2 Calculations will be approximate when the variance is estimated from the data x an object returned by gbayes or the value of the statistic which is an estimator of delta the parameter of interest gbayes sd prior pedf delta n1 n2 xlim ylim name stat delta w alpha upper prior aux mix do vO d1 v1 what interval 109 the standard deviation of the treatment effect a function of possibly a vector of unknown treatment eff
173. aluated at the original x values which will allow curveRep to still know the x distribution By default if any curves have only one point all curves consisting of one point will be placed in a separate stratum To prevent this separation set forcel FALSE see clara By default linear interpolation is used on raw data to obtain y values to cluster to determine x y profiles Specify smooth TRUE to replace observed points curveRep 47 with lowess before computing y points on the grid Also when smooth is used it may be desirable to use ext rap TRUE extrap set to TRUE to use linear extrapolation to evaluate y points for x y clustering Not recommended unless smoothing has been or is being done pr set to TRUE to print progress notes which an integer vector specifying which sample size intervals to plot Must be speci fied if method lattice and must be a single number in that case method The default makes individual plots of possibly all x distribution by sample size by cluster combinations Fewer may be plotted by specifying which Spec ify method lattice to show a lattice xyplot of a single sample size interval with x distributions going across and clusters going down m the number of curves in a cluster to randomly sample if there are more than mina cluster Default is to draw all curves in a cluster For method lattice you can specify m quantiles to use the xYplot function to show quantil
174. ame The following is based on the fact that the default output location for impute transcan is given by where out l1 search position 1 Not run xt lt transcan data mine imputed TRUE shrink TRUE n impute 10 trantab TRUE attach mine pos 1 use names FALSE impute xt imputation 1 use first imputation omit imputation if using single imputation detach 1 mine2 End Not run Example of using invertTabulated outside transcan x lt A 1 2733 447 57 67 1787 9710 y g e i2 yA a 70420797 94 10 fregs eIll 2z AL x 5 6 7 8 with prob 1 2 3 4 when y 5 Within a tolerance of 05 10 1 all y s match exactly so the distance measure does not play a role set seed 1 SO can reproduce for inverse in c linearInterp sample print table invertTabulated x y freq rep 5 1000 inverse inverse Test inverse sample when th stimated transformation is flat on the right First show default imputations set seed 3 x lt rnorm 1000 y lt pmin x 0 translate 309 x 1 500 lt NA for inverse in c linearInterp sample par mfrow c 2 2 w lt transcan x y imputed actual hist inverse inverse curtail FALSE data data frame x y if inverse sample next cat Click mouse on graph to proceed n locator 1 translate Translate Vector or Matrix of Text Strings Description Uses the
175. ame values of ol dor with front TRUE or back TRUE sedit ifwild literal FALSE and replace substring wild will edit the largest substring satisfying test substring2 is just a copy of substring so that subst ring2 lt will work Usage sedit text from to test wild literal FALSE substring location text string restrict substring text first last lt setto S Plus only replace substring wild text old new test front FALSE back FALS numeric string string all digits string Gl sedit 243 substring2 text first last substring2 text first last lt value Arguments text a vector of character strings for sedit substring2 substring2 lt or a single character string for substring location replace substring wild from a vector of character strings to translate from for sedit A single asterisk wild card meaning allow any sequence of characters subject to the test function if any in place of the An element of from may begin with to force the match to begin at the beginning of text and an element of from can end with to force the match to end at the end of text to a vector of character strings to translate to for sedit If a corresponding el ement in from had an the element in to may also have an Only single asterisks are allowed If to is not the same length as from the rep function is used to make it the same length string a single
176. ames TRUE 314 upData big a value such that values larger than this in absolute value are set to missing by cleanup import sasdict the name of a data frame containing a raw imported SAS PROC CONTENTS CNTLOUT dataset This is used to define variable names and to add attributes to the new data frame specifying the original SAS dataset name and label pr set to TRUE or FALSE to force or prevent printing of the current variable number being processed By default such messages are printed if the product of the number of variables and number of observations in obj exceeds 500 000 For dat aframeReduce set pr to FALSE to suppress printing information about dropped or modified variables datevars character vector of names after lowernames is applied of variables to con sider as a factor or character vector containing dates in a format matching dateformat The default is SF which uses the yyyy mm dd format datetimevars character vector of names after lowernames is applied of variables to con sider to be date time variables with date formats as described under datevars followed by a space followed by time in hh mm ss format chron is used to store date time variables If all times in the variable are 00 00 00 the variable will be converted to an ordinary date variable dateformat for cleanup import is the input format see st rpt ime fixdates for any of the variables listed in datevars that have a dateformat that cl
177. ample sizes All vector arguments must have the same length Given pl p2 ballocation uses the method of Brittain and Schlesselman to compute the optimal fraction of observations to be placed in group 1 that either 1 minimize the variance of the difference in two proportions 2 minimize the variance of the ratio of the two proportions 3 minimize the variance of the log odds ratio or 4 maximize the power of the 2 tailed test for differences For 4 the total sample size must be given or the fraction optimizing the power is not returned The fraction for 3 is one minus the fraction for 1 bpower sim estimates power by simulations in minimal time By using bpower sim you can see that the formulas without any continuity correction are quite accurate and that the power of a continuity corrected test is significantly lower That s why no continuity corrections are imple mented here Usage bpower pl p2 odds ratio percent reduction n nl n2 alpha 0 05 bpower 27 bsamsize pl p2 fraction 5 alpha 05 power 8 ballocation pl p2 n alpha 05 bpower sim pl p2 odds ratio percent reduction ny Aly 2 alpha 0 05 nsim 10000 Arguments p1 population probability in the group 1 p2 probability for group 2 odds ratio percent reduction n total sample size over the two groups If you omit this for ballocation the fraction which optimizes power will not be returned td n2 the individual group sample sizes F
178. amples set seed 1 library survival xl lt rnorm 400 x2 lt xl rnorm 400 d time lt rexp 400 x1 min x1 cens lt runif 400 5 2 death lt d time lt cens d time lt pmin d time cens reorrp cens xl x2 Surv d time death rcorrp cens xl x2 y no censoring set seed 1 xl lt runif 1000 x2 lt runif 1000 y lt sample 0 1 1000 TRUE reorrp cens xl x2 y improveProb xl x2 y respline eval 207 rcespline eval Restricted Cubic Spline Design Matrix Description Computes matrix that expands a single variable into the terms needed to fit a restricted cubic spline natural spline function using the truncated power basis Two normalization options are given for somewhat reducing problems of ill conditioning The antiderivative function can be optionally created If knot locations are not given they will be estimated from the marginal distribution of x Usage rcespline eval x knots nk 5 inclx FALSE knots only FALS Arguments x knots nk inclx knots only type norm rpm Value n a x Gl type ordinary norm 2 rpm NULL pc FALS a vector representing a predictor variable knot locations If not given knots will be estimated using default quantiles of x For 3 knots the outer quantiles used are 10 and 90 For 4 6 knots the outer quantiles used are 05 and 95 For nk gt 6 the outer quantiles are 025
179. and 975 The knots are equally spaced between these on the quantile scale For fewer than 100 non missing values of x the outer knots are the 5th smallest and largest x number of knots Default is 5 The minimum value is 3 set to TRUE to add x as the first column of the returned matrix return the estimated knot locations but not the expanded matrix ordinary to fit the function integral to fit its anti derivative O to use the terms as originally given by Devlin and Weeks 1986 1 to nor malize non linear terms by the cube of the spacing between the last two knots 2 to normalize by the square of the spacing between the first and last knots the default norm 2 has the advantage of making all nonlinear terms be on the x scale If given any NAs in x will be replaced with the value rpm after estimating any knot locations Set to TRUE to replace the design matrix with orthogonal uncorrelated princi pal components computed on the scaled centered design matrix If knot s only TRUE returns a vector of knot locations Otherwise returns a matrix with x if inclx TRUE followed by nk 2 nonlinear terms The matrix has an attribute knots which is the vector of knots used When pc is TRUE an additional attribute is stored pcparms which contains the center and scale vectors and the rotation matrix 208 rcspline plot References Devlin TF and Weeks BJ 1986 Spline functions for logistic regression modeling Proc 1
180. anel function for trellis bwplot box percentile plots Panel function for trellis xyplot uses plsmo Compute first prin component and get coefficients on original scale of variables Plot precision of estimate of correlation coefficient Plot smoothed x vs y with labeling and exclusion of NAs Also allows a grouping variable and plots unsmoothed data Power and sample size calculations for ordinal responses two treatments proportional odds model prn expression does print expression but titles the output with expression Do prn expression txt to add a heading txt before the expression title Sunflower plots Andreas Ruckstuhl Werner Stahel Martin Maechler Tim Hesterberg Set up postcript using nice defaults for different types of graphics media Stamp a plot with date in lower right corner pstamp Add pwd T and or time T to add current directory name or time Put additional text for label as first argument e g pstamp Figure 1 will draw Figure 1 date Different way to use key Put key at most empty part of existing plot Pearson or Spearman correlation matrix with pairwise deletion of missing data Somers Dyx rank correlation with censored data Assess difference in concordance for paired predictors Evaluate restricted cubic spline design matrix Plot spline fit with nonparametric smooth and grouped estimates Restate restricted cubic spline in unrestricted form and create TeX expressi
181. angnail symptom3 Hangnail summary Symptoms Frequency table sex treatment sex Symptoms summary sex treatment Symptoms fun table Check ma lt inmChoice Symptoms Muscle Ache table sex ma mdb get 165 could also do summary sex Compute mean treatment mChoice symptoml symptom2 symptom3 fun table age separately by 3 variables summary age summary age f lt summary t sex treatment Symptoms sex treatment Symptoms method cross reatment age sex Symptoms method reverse test TRUE trio of numbers represent 25th 50th 75th percentile print f long TRUE mdb get Read Tables in a Microsoft Access Database Description Assuming the mdb tools package has been installed on your system and is in the system path mdb get imports one or more tables in a Microsoft Access database Date time variables are converted to dates or chron package date time variables The csv get function is used to import automatically exported csv files If tables is unspecified all tables in the database are retrieved If more than one table is imported the result is a list of data frames Usage mdb get file tables NULL lowernames FALSE allow NULL dateformat m d Sy Arguments file tables lowernames allow dateformat Details the file name containing the Access database character vec
182. ary method will not use the imputation adjusted variances You may obtain the new variances using fit S varorVarcov fit When you specify a Design function to fit mult impute e g lrm ols cph psm bj automatically computed transformation parameters e g knot locations for rcs that are es timated for the first imputation are used for all other imputations This ensures that knot locations will not vary which would change the meaning of the regression coefficients Warning even though fit mult impute takes imputation into account when estimating vari ances of regression coefficient it does not take into account the variation that results from estima tion of the shapes and regression coefficients of the customized imputation equations Specifying shrink TRUE solves a small part of this problem To fully account for all sources of variation you should consider putting the t ranscan invocation inside a bootstrap or loop if execution time allows Better still use acegImpute or one of the libraries such as MICE that uses real Bayesian posterior realizations to multiply impute missing values correctly It is strongly recommended that you use the Hmisc naclus function to determine is there is a good basis for imputation naclus will tell you for example if systolic blood pressure is miss ing whenever diastolic blood pressure is missing If the only variable that is well correlated with diastolic bp is systolic bp there is no basis fo
183. as more than 2 levels an indicator variable is formed for the most frequently level vs all others and another indicator for the second most frequent level and all others The correlation is taken as the maximum of the two in absolute value rcorr cens 203 Usage Pl rceorr cens x S outx FALSI S3 method for class formula rceorrcens formula data NULL subset NULL na action na retain exclude imputed TRUE outx FALS GI Arguments x a numeric predictor variable an Surv object or a vector If a vector assumes that every observation is un censored outx set to TRUE to not count pairs of observations tied on x as a relevant pair This results in a Goodman Kruskal gamma type rank correlation formula a formula with a Surv object or a numeric vector on the left hand side data subset na action the usual options for models Default for na action is to retain all values NA or not so that NAs can be deleted in only a pairwise fashion exclude imputed set to FALSE to include imputed values created by impute in the calcula tions extra arguments passed to biVar Value rcorr cens returns a vector with the following named elements C Index Dxy S D N missing uncensored Relevant Pairs Concordant and Uncertain n number of observations not missing on any input variables missing number of observations missing on x or S relevant number of pairs of non missing observations for which S could be
184. aset for the final imputation The var matrix in the fit object has the imputation corrected variance covariance matrix coefficients is the average over imputations of the coefficient vectors variance inflation impute is a vector containing the ratios of the di agonals of the between imputation variance matrix to the diagonals of the average apparent within imputation variance matrix missingInfo is Rubin s rate of missing information and dfmi is Rubin s degrees of freedom for a t statistic for testing a single parameter The last two objects are vectors corresponding to the diagonal of the variance matrix Side Effects prints plots and impute transcan creates new variables Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu transcan References Kuhfeld Warren F The PRINQUAL Procedure SAS STAT User s Guide Fourth Edition Volume 2 pp 1265 1323 1990 Van Houwelingen JC Le Cessie S Predictive value of statistical models Statistics in Medicine 8 1303 1325 1990 Copas JB Regression prediction and shrinkage JRSS B 45 311 354 1983 He X Shen L Linear regression after spline transformation Biometrika 84 474 481 1997 Little RJA Rubin DB Statistical Analysis with Missing Data New York Wiley 1987 Rubin DJ Schenker N Multiple imputation in health care databases An overview and some appli cations Stat in Med 10 585 598 1991
185. ation Author s Frank E Harrell Jr Professor of Biostatistics Chair Department of Biostatistics Vanderbilt University School of Medicine Nashville Tennessee f harrell vanderbilt edu References See Alzola CF Harrell FE 2004 An Introduction to S and the Hmisc and Design Libraries at http biostat mc vanderbilt edu twiki pub Main RS sintro pdf for ex tensive documentation and examples for the Hmisc package hoeffd Matrix of Hoeffding s D Statistics Description Computes a matrix of Hoeffding s 1948 D statistics for all possible pairs of columns of a matrix D is a measure of the distance between F x y and G x H y where F x y is the joint CDF of X and Y and G and H are marginal CDFs Missing values are deleted in pairs rather than deleting all rows of x having any missing variables The D statistic is robust against a wide variety of alternatives to independence such as non monotonic relationships The larger the value of D the more dependent are X and Y for many types of dependencies D used here is 30 times Hoeffding s original D and ranges from 0 5 to 1 0 if there are no ties in the data print hoeffd prints the information derived by hoeffd The higher the value of D the more dependent are x and y hoeffd 127 Usage hoeffd x hoeffd x y S3 method for class hoeffd print x sss Arguments x a numeric matrix with at least 5 rows and at least 2 columns if y is absent
186. ation will be presented in the graph Colors are utilized for the time dependent covariate level for an individual This argument provides the number of unique covariate levels which will be dis played by mapping the number of colors via num colors to the number of desired covariate levels This will divide the covariate span into roughly equally sized intervals via the S Plus cut function Default is one color mean ing no time dependent information will be presented in the graph Note that this argument will be ignored superceded if a non NULL argument is provided for the cut cov parameter This argument allows the user to explicitly state how to define the intervals for the time dependent covariate such that different colors will be allocated to the user defined covariate levels For example for plotting five colors six ordered points within the span of the data s covariate levels should be provided Default is NULL meaning that the num colors argument value will dictate the num ber of breakpoints with the covariate span defined into roughly equally sized intervals via the S Plus cut function However if is null cut cov FALSE then this argument supercedes any entry for the num colors argu ment This is a vector argument defining the actual colors used for the time dependent covariate levels in the plot with the index of this vector corresponding to the ordered levels of the covariate The number of colors i e the len
187. ations provide a burn in and imputations are saved only from the last n impute iterations 3 For each variable containing any NAs draw a sample with replacement from the observations in the entire dataset in which the current variable being imputed is non missing Fit a flexible additive model to predict this target variable while finding the optimum transformation of it unless the identity transformation is forced Use this fitted flexible model to predict the target variable in all of the original observations Impute each missing value of the target variable with the observed value whose predicted transformed value is closest to the predicted transformed value of the missing value if mat ch closest and type pmm or use a draw from a multinomial distribution with probabilities derived from distance weights if mat ch weighted the default 4 After these imputations are computed use these random draw imputations the next time the curent target variable is used as a predictor of other sometimes missing variables When match closest predictive mean matching does not work well when fewer than 3 variables are used to predict the target variable because many of the multiple imputations for an observation will be identical In the extreme case of one right hand side variable and assuming 16 aregImpute that only monotonic transformations of left and right side variables are allowed every bootstrap resample will give predicted values
188. ault double precision numeric variables are changed to single precision S Plus only or to integer when they contain no fractional components Infinite values or values greater than 1e20 in absolute value are set to NA This solves problems of importing Excel spreadsheets that contain occasional charac ter values for numeric columns as S Plus converts these to Inf without warning There is also an option to convert variable names to lower case and to add labels to variables The latter can be made easier by importing a CNTLOUT dataset created by SAS PROC FORMAT and using the sasdict option as shown in the example below cleanup import can also transform character or factor variables to dates upData is a function facilitating the updating of a data frame without attaching it in search position one New variables can be added old variables can be modified variables can be removed or renamed and labels and units attributes can be provided Various checks are made for errors and inconsistencies with warnings issued to help the user Levels of factor variables can be replaced especially using the list notation of the standard merge levels function Unless force single is set to FALSE upData also converts double precision vectors to single precision if not under R or to integer if no fractional values are present in a vector Both cleanup import and upData will fix a problem with data frames created under S Plus before version 5 that ar
189. aw a line with a slope of 1 from the top of the y axis to the right end of the x axis Essentially it denotes end of current follow up period for looking at the time to event data Default is FALSE now line 1lty line type of now line event chart now line 1wd line width of now line now line col color of now line pty graph option pt y m is the default use pt y s for the square looking Goldman s event chart date orig date of origin to consider if dates are in julian SAS or S Plus dates object format default is January 1 1960 which is the default origin used by both S Plus and SAS Utilized when either y julian FALSEorx julian FALSE at title for event chart Default is Event Chart y idlabels column or data frame variable name used for y axis labels For example if c pt no is specified patient ID stored in pt no will be seen on y axis labels instead of sequence specified by subset r This argument takes precedence over both y axis auto andy axis custom see below NOTE Program will issue warning if this argument is specified and ifis na y var FALSE y idlabels will not be used in this situ ation Also attempting to plot too many patients on a single event chart will cause undesirable plotting of y idlabels y axis character string specifying whether program will control labelling of y axis with argument auto orif user will c
190. be categorical no matter how many levels they have TRUE the default to exclude NAs before passing data to fun to compute statis tics FALSE otherwise na rm FALSE is useful if the response variable is a matrix and you do not wish to exclude a row of the matrix if any of the columns in that row are NA na rm also applies to summary statistic functions such as smean cl normal For these na rm defaults to TRUE unlike built in func tions for method response set na include FALSE to exclude missing values from being counted as their own category when subsetting the response s by levels of a categorical variable Formethod reverse setna include TRUI to keep missing values of categorical variables from being excluded from the ta ble number of quantile groups to use when variables are automatically categorized with method response or cross using cut2 if fewer than nmin observations exist in a category for response over all strata combined that category will be ignored For reverse for categories of the response variable in which there are less than or equal to nmin non missing observations the raw data are retained for later plotting in place of box plots applies if method reverse Set to TRUE to compute test statistics using tests specified in conTest and catTest Gl ma 274 summary formula conTest a function of two arguments grouping variable and a continuous variable t
191. be used as part of R plotmath expressions If the input string has a space or percent inside the string is surrounded by a call to plotmath s paste function as data frame labelledis a utility function that is called by data frame It is just a copy of as data frame vector data frame labelledis another utility function that adds a class labelled to every variable in a data frame that has a label attribute but not a labelled class reLabelled is used to add a labelled class back to variables in data frame that have a label attribute but no labelled oldClass Useful for changing cleanup import d S Plus 6 data frames back to general form for R and S Plus 2000 Usage label x default NULL Default S3 method label x default NULL units FALSE plot FALSE grid FALSE 7 S3 method for class data frame label x default NULL self FALSE label x lt value Default S3 replacement method label x lt value S3 replacement method for class data frame label x self TRUE lt value labelPlotmath label units NULL plotmath R grid FALS Gl lt S3 method for class labelled PLING vaes or x calls print labelled Label object S3 method for class data frame Label object file append FALSE Gl ll
192. ble names are converted to periods as with R before version 1 9 charfactor set to TRUE to change character variables to factors if they have at least two characters in an observation but have fewer than n 2 unique values arguments passed to read dta Details stata get invokes the read dta function in the foreign package to read an STATA file with a default output format of data frame The label function is used to attach labels to individual variables instead of to the data frame as done by read dta By default integer valued variables are converted to a storage mode of integer unless force single FALSE Date variables are converted to R Date variables By default underscores in names are converted to periods Value A data frame Author s Charles Dupont See Also read dta cleanup import label label data frame Date Examples Not run w lt stata get sQuote tmp my dta End Not run store 259 store Store an Object Permanently Description By default store will copy the object to Data under the same name This function is most useful when you have attached a data frame or a temporary directory in position 1 store is also useful for setting up to store later objects in a temporary work area Data tempnnnn where nnnn is a number computed by the system so that they are not stored on disk For this usage just invoke store with no arguments i e store After that you can still i
193. bline lsfit zS ty w tx par mfrow c 2 2 Example where one category in y differs from others but only in variance of x n lt 50 y lt sample 1 5 n TRUE x lt rnorm n x y 1 lt rnorm sum y 1 0 5 z lt areg x y xtype 1 ytype c Zz plot z z lt areg x y ytype c plot z areg 11 Not run Examine overfitting when true transformations are linear par mfrow c 4 3 for n in c 200 2000 4 x lt rnorm n y lt rnorm n x for nk in c 0 3 5 z lt areg x y nk nk crossval 10 B 100 print z plot z title paste n n par mfrow c 1 1 Underfitting when true transformation is quadratic but overfitting when y is allowed to be transformed set seed 49 n lt 200 x lt rnorm n y lt rnorm n 5 x 2 areg x y nk 0 crossval 10 B 100 areg x y nk 4 ytype l crossval 10 B 100 z lt areg x y nk 4 crossval 10 B 100 Zz Plot x vs predicted value on original scale Since y transform is not monotonic there are multiple y inverses xx lt seq 3 5 3 5 length 1000 yhat lt predict z xx type fitted plot x y xlim c 3 5 3 5 for j in l ncol yhat lines xx yhat j col j Plot a random sample of possible y inverses yhats lt predict z xx type fitted what sample points xx yhats pch 2 End Not run True transformation of xl is quadratic y is linear n
194. c NA NA NA g c NA 2 3 h c NA NA 3 par mfrow c 2 2 for m in if R c ward complete median else 322 wtd stats c compact connected average plot naclus df method m title m naplot naclus df n lt naclus df plot n naplot n na pattern df builtin function x lt c 1 rep 2 11 rep 3 9 combine levels x x lt c 1 2 rep 3 20 combine levels x plotMultSim example Plot proportion of observations for which two variables are both positive diagonals show the proportion of observations for which the one variable is positive Chance correct the off diagonals by subtracting the product of the marginal proportions On each subplot the x axis shows month 0 4 8 12 and there is a separate curve for females and males d lt data frame sex sample c female male 1000 TRUE month sample c 0 4 8 12 1000 TRUE xl sample 0 1 1000 TRUE x2 sample 0 1 1000 TRU x3 sample 0 1 1000 TRU s lt array NA c 3 3 4 opar lt par mar c 0 0 4 1 0 waste less space for sx in c female male for i in 1 4 mon lt i 1 4 s i lt varclus xl x2 x3 sim ccbothpos data d subset d month mon amp d sex sx Ssim E E E plotMultSim s c 0 4 8 12 vname c x1 x2 x3 add sx male slimds TRUE lty 1 sx male Slimds TRUE causes separate scaling for diagonals and off diagonals
195. category membership transcan 301 rhsImp Set to random to use random draw imputation when a sometimes miss ing variable is moved to be a predictor of other sometimes missing variables Default is rhsImp mean which uses conditional mean imputation on the transformed scale Residuals used are residuals from the transformed scale When random is used transcan runs 5 iterations and ignores eps details impcat set to a character scalar that is the name of a category variable to include in the resulting transcan object an element details impcat containing details of how the categorical variable was multiply imputed ae arguments passed to scat 1dor tothe fitter function for fit mult impute long for summary set to TRUE to print all imputed values For print set to TRUE to print details of transformations imputations var For impute is a variable that was originally a column in x for which im putated values are to be filled in imputed TRUE must have been used in transcan Omit var to impute all variables creating new variables in search position where imputation specifies which of the multiple imputations to use for filling in NA values name name of variable to impute for impute function Default is character string version of the second argument var in the call to impute For invert Tabulated is the name of variable being transformed used only for warning messages where in location in search
196. ch pair of variables and P the asymptotic P values Pairs with fewer than 2 non missing values have the r values set to NA The diagonals of n are the number of non NAs for the single variable corresponding to that row and column Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Hollander M and Wolfe D A 1973 Nonparametric Statistical Methods New York Wiley Press WH Flannery BP Teukolsky SA Vetterling WT 1988 Numerical Recipes in C Cam bridge Cambridge University Press See Also hoeffd cor combine levels varclus dotchart2 impute chisq test cut2 Examples z lt OH 2 ay 07 Ty 2 y lt c 4 1 0 1 4 zZz lt o 1 2 3 4 NA y x e l 27 3 4 5 reorr cbind x y 2Z v rcorr cens Rank Correlation for Censored Data Description Computes the c index and the corresponding generalization of Somers Dxy rank correlation for a censored response variable Also works for uncensored and binary responses although its use of all possible pairings makes it slow for this purpose Dxy and c are related by Dxy 2 c 0 5 rcorr cens handles one predictor variable rcorrcens computes rank correlation measures separately by a series of predictors In addition rcorrcens has a rough way of handling categor ical predictors If a categorical factor predictor has two levels it is coverted to a numeric having values and 2 If it h
197. colors with the default colors chosen so that when the graph is printed under S Plus for Windows 4 0 to an HP LaserJet printer the confidence regions are naturally rm boot 225 ordered by darkness of gray scale Regions closer to the point estimates i e the center are darker Specify mult i method density to instead use den sities of lines drawn per inch in the confidence regions with all regions drawn with the default color The polygon function is used to shade the regions multi conf vector of confidence levels in ascending order Default is to use 12 confidence levels ranging from 0 05 to 0 99 multi density vector of densities in lines per inch corresponding to multi conf As is the convention in the polygon function a density of 1 indicates a solid region multi col vector of colors corresponding to multi conf See multi method for rationale subtitles set to FALSE to suppress drawing subtitles for the plot Details Observations having missing t ime or y are excluded from the analysis As most repeated measurement studies consider the times as design points the fixed covariable case is the default Bootstrapping the residuals from the initial fit assumes that the model is correctly specified Even if the covariables are fixed doing an unconditional bootstrap is still appropriate and for large sample sizes unconditional confidence intervals are only slightly wider than conditional ones For moderate to small sample s
198. colvar month Create a data frame reshaping a long dataset in which groups are formed not just by subject id but by combinations of subject id and visit number Also carry forward a variable that is supposed to be constant within subject visit number combinations In this example it is not constant so an arbitrary visit number will be selected w lt data frame id c a a a a b b b d d d Vas aster SEn aie De Aa a DS Dy 2 Ayr DDG Kae TAT TA By BY 8C UC DI Ey Ply IG varse acl a toy ye eT ap ts R ye ay gg a pa AN val 1 10 with w reShape val id data frame id visit constant data frame k colvar var Get predictions from a regression model for 2 systematically varying predictors Convert the predictions into a matrix with rows corresponding to the predictor having the most values and columns corresponding to the other predictor SE SR SH HE rlegend 219 d lt expand grid x2 0 1 x1 1 100 pred lt predict fit d reShape pred id d xl colvar d x2 makes 100 x 2 matrix Reshape a wide data frame containing multiple variables representing repeated measurements 3 repeats on 2 variables 4 subjects set seed 33 n lt 4 w lt data frame age rnorm n 40 10 sex sample c female male n TRUE sbpl rnorm n 120 15 sbp2 rnorm n 120 15 sbp3 rnorm n 120 15 dbpl rnorm n 807 53 gt dbp2 rnorm n 80 15 dbp3 rnorm n 80 15
199. confidence intervals y coordinate for horizontal set to TRUE to have get Latest Source return a data frame of available files and latest versions instead of fetching any a numeric vector character expansion factor interval to truncate limits vector of colors a named numeric vector number of digits used for round vector of point estimates for confidence limits see strsplitinR a scaling constant function to transform scale a categorical grouping variable set to FALSE to not automatically add a column of ones to the x matrix which element of b to add to the result if a does not contain a column for inter cepts set to FALSE to omit drawing confidence coefficients line widths name of package for get Latest Source default is Hmisc vector of confidence coefficients or quantiles quantiles on transformed scale an integer telling get Latest Source to get the recent most recently mod ified files from the package set to TRUE to round converted values a Surv object se sep setAside side space str string ticks times title tolerance type width xpxi Author s Frank Harrell Examples Misc vector of standard errors a single character string specifying the delimiter for adj 1 side 4 is a character string used to determine the space to set aside for all strings for confbaris b 1 t xr for bottom left top right For out Text is the using integers 1 4 corre
200. corresponding to the next to last vector in INDEX etc and the elements of the list array correspond to the val ues computed by FUN In this situation the returned value is a regular array if simplify TRUE The order of dimensions is as previously but the additional last dimension corresponds to values computed by FUN Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also asNumericMatrix matrix2dataFrame tapply sapply lapply mapp1ly by Examples require datasets TRUE a lt mApply iris 5 irisSSpecies mean mChoice Methods for Storing and Analyzing Multiple Choice Variables Description mChoice is a function that is useful for grouping variables that represent individual choices on a multiple choice question These choices are typically factor or character values but may be of any type Levels of component factor variables need not be the same all unique levels or unique character values are collected over all of the multiple variables Then a new character vector is formed with integer choice numbers separated by semicolons Optimally a database system would have exported the semicolon separated character strings with a levels attribute containing strings defining value labels corresponding to the integer choice numbers mChoice is a function for creating a multiple choice variable after the fact mChoice variables are
201. cs functions Details errbar adds vertical error bars to an existing plot or makes a new plot with error bars It can also make a horizontal error bar plot that shows error bars for group differences as well as bars for groups For the latter type of plot the lower x axis scale corresponds to group estimates and the upper scale corresponds to differences The spacings of the two scales are identical but the scale for differences has its origin shifted so that zero may be included If at least one of the confidence intervals includes zero a vertical dotted reference line at zero is drawn Author s Charles Geyer University of Chicago Modified by Frank Harrell Vanderbilt University to han dle missing data to add the parameters add and lt y and to implement horizontal charts with differences Examples set seed 1 x lt 1 10 y lt x rnorm 10 delta lt runif 10 errbar x y y delta y delta Show bootstrap nonparametric CLs for 3 group means and for pairwise differences on same graph group lt sample c a b d 200 TRUE y lt runif 200 25 group b 5x group d cla lt smean cl boot y group a B 100 reps TRUE usually B 1000 a lt attr cla reps clb lt smean cl boot y group b B 100 reps TRUE b lt attr clb reps cld lt smean cl boot y group d B 100 reps TRUE d lt attr cld reps a b lt quantile a b c 0
202. ct dslabels S3 method for class contents list print x sort c none names labels NAS vars Arguments object a data frame For ht m1 is an object created by contents Forcontents list is a list of data frames x an object created by contents sort Default is to print the variables in their original order in the data frame Specify one of names labels or NAs to sort the variables by respectively alphabetically by names alphabetically by labels or by increaseing order of number of missing values For contents list sort may also be the value vars to cause sorting by the number of variables in the dataset prlevels set to FALSE to not print all levels of factor variables number set to TRUE to have the print and latex methods number the variables by their order in the data frame file file to which to write the html code Default is conents dfname htm1 where dfname is the name of the data frame processed by contents levelType By default bullet lists of category levels are constructed in html Set level Type table to put levels in html table format append set to TRUE to add html code to an existing file arguments passed from html to format df unused otherwise dslabels named vector of SAS dataset labels created for example by sasdsLabels Value an object of class contents data frame or contents list Author s Frank Harrell Vanderbilt University f harrell van
203. ctor of numeric values for tick marks and a corresponding charac ter vector labels with formatted values e g using time or date formats When you use such a variable with xYplot and do not specify a corresponding scales element tick marks and scale labeling are taken from scales major The at element for scales minor is used by panel xYplot to add minor tick marks numericScale by default will keep the name of the input variable as a label attribute for the new numeric variable Usage Coind see xYplot formula data sys frame sys parent groups subset xlab NULL ylab NULL ylim NULL panel panel xYplot prepanel prepanel xYplot scales NULL minor ticks NULL panel xYplot x y subscripts groups NULL type if is function method method quantiles b else p method c bars bands upper bars lower bars alt bars quantiles filled bands methodArgs NULL label curves TRUE abline xYplot 329 probs c 5 25 75 nx NULL cap 0 015 lty bar 1 lwd plot lineSlwd lty plot lineSlty pch plot symbolSpch cex plot symbolScex font plot symbolSfont col NULL lwd bands NULL lty bands NULL col bands NULL minor ticks NULL col fill NULL size NULL rangeCex c 5 3 prepanel xYplot x y Dotplot formula data sys frame sys parent groups subset xlab NULL ylab NULL ylim NULL panel panel Dotplot prepanel prepanel Dotplot scales
204. ctor the same length as xout Author s Frank Harrell See Also approx 2 f 0 ties ordere 8 areg Examples approxExtrap 1 3 1 3 xout c 0 4 areg Additive Regression with Optimal Transformations on Both Sides us ing Canonical Variates Description Expands continuous variables into restricted cubic spline bases and categorical variables into dummy variables and fits a multivariate equation using canonical variates This finds optimum transforma tions that maximize R Optionally the bootstrap is used to estimate the covariance matrix of both left and right hand side transformation parameters and to estimate the bias in the R due to over fitting and compute the bootstrap optimism corrected R Cross validation can also be used to get an unbiased estimate of R but this is not as precise as the bootstrap estimate The bootstrap and cross validation may also used to get estimates of mean and median absolute error in predicted val ues on the original y scale These two estimates are perhaps the best ones for gauging the accuracy of a flexible model because it is difficult to compare R under different y transformations and because R allows for an out of sample recalibration i e it only measures relative errors Note that uncertainty about the proper transformation of y causes an enormous amount of model uncertainty When the transformation for y is estimated from the data a high variance in predicte
205. d 100 find matches Value find matches returns a list of class find matches with elements mat ches and distance Both elements are matrices with the number of rows equal to the number of rows in x and with k columns where k is the maximum number of matches lt maxmatch that occurred The elements of mat ches are row identifiers of y that match with zeros if fewer than maxmatch matches are found blanks if y had row names matchCases returns a data frame with vari ables idcase id of case currently being matched t ype factor variable with levels case and control id id of case if case row or id of matching case and y Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Ming K Rosenbaum PR 2001 A note on optimal matching with variable controls using the assignment algorithm J Comp Graph Stat 10 455 463 Cepeda MS Boston R Farrar JT Strom BL 2003 Optimal matching with a variable number of controls vs a fixed number of controls for a cohort study trade offs J Clin Epidemiology 56 230 237 Note These papers were not used for the functions here but probably should have been See Also scale apply Examples y lt gt xbind e 1 2 pe 11 22 Glay lt 4 e031 41 et 32 5 x lt rbind ie 09 21 gt 295539 y x w lt find matches x y maxmatch 5 tol c 05 05 set seed 111 so can replicate results x
206. d xlab label x wait for mouse click u Lag Lag a Numeric Character or Factor Vector Description Shifts a vector shift elements later Character or factor variables are padded with numerics with NA The shift may be negative Usage Lag x shift 1 Arguments X a vector shift integer specifying the number of observations to be shifted to the right Negative values imply shifts to the left latex 145 Details A ttributes of the original object are carried along to the new lagged one Value a vector like x Author s Frank Harrell See Also lag Examples Lag 1 5 2 Lag letters 1 4 2 Lag factor letters 1 4 2 Find which observations are the first for a given subject ird lt taty Maly Vat Dra pry Ver id Lag id duplicated id latex Convert an S object to LaTeX and Related Utilities Description latex converts its argument to a tex file appropriate for inclusion in a LaTeX2e document latex is a generic function that calls one of Latex default latex function latex list latex default does appropriate rounding and decimal alignment and produces a file containing a LaTeX tabular environment to print the matrix or data frame x as a table latex function prepares an S function for printing by issuing sed commands that are similar to those in the S to latex procedure in the s to latex package Chambers and Hastie 1993 latex list calls
207. d contents data frame creates an object containing the following attributes of the variables from a data frame names labels if any units if any number of factor levels Gf any factor lev els class storage mode and number of NAs print contents data frame will print the results with options for sorting the variables html contents data frame creates HTML code for displaying the results This code has hyperlinks so that if the user clicks on the number of levels the browser jumps to the correct part of a table of factor levels for all the factor variables If long labels are present longlabe1 attributes on variables these are printed at the bottom and the htm1 method links to them through the regular labels Variables having the same levels in the same order have the levels factored out for brevity contents list prints a directory of datasets when sasxport get imported more than one SAS dataset Usage contents object S3 method for class data frame contents object S3 method for class contents data frame print x sort c none names labels NAsS prlevels TRUE number FALSE S3 method for class contents data frame html object sort c none names labels NAsS prlevels TRUE file paste contents object dfname html sep levelType c list table append FALSE number FALSE S3 method for class list contents 39 contents obje
208. d values on the original y scale may result especially if the true transformation is linear Comparing bootstrap or cross validated mean absolute errors with and without restricted the y transform to be linear yt ype 1 may help the analyst choose the proper model complexity Usage areg x y xtype NULL ytype NULL nk 4 B 0 na rm TRUE tolerance NULL crossval NULL S3 method for class areg print x digits 4 S3 method for class areg plot x whichx 1 ncol xSx S3 method for class areg predict object x type c lp fitted what c all sample Arguments x A single predictor or a matrix of predictors Categorical predictors are required to be coded as integers as factor does internally For predict x isa data matrix with the same integer codes that were originally used for categorical variables xtype ytype nk na rm tolerance crossval digits object whichx type what Details a factor categorical character or numeric response variable a vector of one letter character codes specifying how each predictor is to be modeled in order of columns of x The codes are s for smooth function using restricted cubic splines 1 for no transformation linear or c for categorical to cause expansion into dummy variables Default is s if nk gt 0 and 1 if nk 0 same coding as for xt ype Default is s for a
209. d frac defaults to 02 Assumes that plot has already been called Current par usr is used to determine the range of data for the axis of the current plot This range is used in jittering and in constructing line segments Value histSpike returns the actual range of x used in its binning Side Effects scatld adds line segments to plot datadensity data frame draws a complete plot histSpike draws a complete plot or adds to an existing plot Author s Frank Harrell Department of Biostatistics Vanderbilt University Charlottesville VA USA f harrell vanderbilt edu Martin Maechler improved scat 1d Seminar fuer Statistik ETH Zurich SWITZERLAND maechler stat math ethz ch Jens Oehlschlaegel Akiyoshi wrote jitter2 Center for Psychotherapy Research Christian Belser Strasse 79a D 70597 Stuttgart Germany oehl psyres stuttgart de See Also segments jitter rug plsmo stripplot hist data frame Ecdf hist histogram table density Examples plot x lt rnorm 50 y lt 3 x rnorm 50 2 scatld x density bars on top of graph scatld y 4 density bars at right histSpike add TRUE histogram instead 100 bins x histSpike y 4 add TRUE histSpike x type density add TRUE smooth density at bottom histSpike y 4 type density add TRUE smooth lt lowess x y add nonparametric regression curve lines smooth Note plsmo does this 240 scatld scatld x y appr
210. dd id id to rm boot to use clustering id lt sort rep 1 n pt n per pt Because we are ignoring patient id this simulation is effectively using 1 point from each of 300 patients with times 1 2 3 30 JF E SE te f lt rm boot time y B 500 nk 5 bootstrap type x fixed g lt plot f ylim c 1 1 pointwise FALSE null in region lt null in region all g lower lt 0 amp gSupper gt 0 prn c i i null in region null in region Simulation Results 905 1000 simultaneous confidence bands fully contained the horizontal line at zero End Not run rMultinom Generate Multinomial Random Variables with Varying Probabilities samplesize bin 229 Description Given a matrix of multinomial probabilities where rows correspond to observations and columns to categories and each row sums to 1 generates a matrix with the same number of rows as has probs and with m columns The columns represent multinomial cell numbers and within a row the columns are all samples from the same multinomial distribution The code is a modification of that in the impute polyreg function in the MICE package Usage rMultinom probs m Arguments probs matrix of probabilities m number of samples for each row of probs Value an integer matrix having m columns See Also rbinom Examples set seed 1 w lt rMultinom rbind c 1 2 3 4 c 4 3 2 1 200 t apply w 1 table 200 samplesize bin Sample Si
211. ddmmmyy deff describe describe data frame describe default do dot chart Dotplot drawPlot Ecdf eip errbar event chart event history find matches first word fit mult impute format df 121 Useful for putting many special chars in graph titles Pierre Joyet pierre joyet bluewin ch Power of Cox interaction test More compactly store variables in a data frame and clean up problem data when e g Excel spreadsheet had a non numeric value in a numeric column Combine infrequent levels of a categorical variable Attach a comment attribute to an object comment fit lt Used old data comment fit prints comment Draws confidence bars on an existing plot using multiple confidence levels distinguished using color or gray scale Print the contents variables labels etc of a data frame Power of Cox 2 sample test allowing for noncompliance Vector of character strings from list of unquoted names Enhanced importing of comma separated files labels Like cut with better endpoint label construction and allows construction of quantile groups or groups with given n Snapshot graph of distributions of all variables in a data frame For continuous variables uses scat1d Quantify representation of new observations in a database SAS date7 output format for a chron object Kish design effect and intra cluster correlation Function to describe different classes of objects Invoke by saying describe object It calls
212. de we BHR E SSD Be RE EES 234 SCOre DINALY 2 a oA eee OS SAE hE eee be he eA eS 241 sedit agata acres ee ae ee cee ee ae Ae Atanas e oe A aoe ahd ae A eee ge 242 SHOW PCD aureos tio eat oe awh Gee ae Boe ge ahs ae A ea a ee 245 SIMPHIYIDIMS 4 4 6 en kU POL Re oe Bar eke ee BE Oe Be gate S 246 smem Sda tures eee eke Acca we Ot eee ee ee BE Re ete ee eA eek ee A 247 SOIVCE e o gt bs eG hed bea ia a Te BEAL fea de heed ee SARC Baye a Gea a Be 248 SOMEIS2 a4 exh 4 ene PP be ew eee ae Se Be ee bee oe Baws 249 SPOWEL foie etre det a Be ek Be ae Bete bt eh She Gt Et Be te od 250 SPSS PET ka be ee Ra ee eR TS eR a oe Eee we a a 255 SEC ic bee hha Ge Bee ho we a eo Soh he es Sie ee eA Be ee ee Re a 256 Stata pet sce seeds oi ass Whe ess a e oe ee Sep SE gaye bd b ea id et ao ae we es eh ae Se 297 SLOPE c oe nb EAH ASH LO REA RSE EA e a i e a ae 259 string boundIN DOX d re se EADS ee 8 SRO ORES ERAS SR Ow 4 260 string break line sas 54 Sek Se Gea Se aS ERS SB oe AR ea ace 261 SIEMEDIMNS 245 62th e ee ee 4 eee eee ewe ee 262 SUbplOt 2 4c ean eee ee ee A Sea RGR A SS Re RA Ba Dee ee al 263 SUMMANZE dn ae owt ee cen ee Se ek hs SS OE A 264 summary formula 2 a 269 SYMBDOMMEG eto m eae Sge se es Bee Ee els S RHEE SED oe BOO dnie e a 283 EYS ee e ee et be he hae ee he et he bee we Es See eee 284 tetest CUSTER vous eae tee es ete amp ade ee ne Bao he amp ee eee Y 285 tPAMSAGCE eiia ee Ge abada ae he Ra ee Bae ae Aw Ba ae ge 4 286
213. derbilt edu 164 mChoice See Also label Examples options digits 3 set seed 3 n lt 20 sex lt factor sample c m f n rep TRUE age lt rnorm n 50 5 treatment lt factor sample c Drug Placebo n rep TRUE Generate a 3 choice variable each of 3 variables has 5 possible levels symp lt c Headache Stomach Ache Hangnail Muscle Ache Depressed symptoml lt sample symp n TRUE symptom2 lt sample symp n TRUE symptom3 lt sample symp n TRUE chind symptoml symptom2 symptom3 1 5 Symptoms lt mChoice symptoml symptom2 symptom3 label Primary Symptoms Symptoms print Symptoms long TRUE format Symptoms 1 5 inmChoice Symptoms Headache levels Symptoms inmChoice Symptoms 3 inmChoice Symptoms c Headache Hangnail Note In this example some subjects have the same symptom checked multiple times in practice these redundant selections would be NAs mChoice will ignore these redundant selections meanage lt N lt numeric 5 for 7 in 125 4 meanage j lt mean age inmChoice Symptoms j N 3 lt sum inmChoice Symptoms j names meanage lt names N lt levels Symptoms meanage N Manually compute mean age for 2 symptoms mean age symptoml Headache symptom2 Headache symptom3 Headache mean age symptoml Hangnail symptom2 H
214. derbilt edu See Also describe html upData Examples set seed 1 dfr lt data frame x rnorm 400 y sample c male female 400 TRUE contents dfr dfr lt upData dfr labels c x Label for x y Label for y attr dfr x longlabel lt 40 cpower A very long label for x that can continue onto multiple long lines of text k lt contents dfr print k sort names prlevels FALSE Not run html k html contents dfr same result w lt html k file my html create my html don t display latex k contents latex default just the main information End Not run cpower Power of Cox log rank Two Sample Test Description Assumes exponential distributions for both treatment groups Uses the George Desu method along with formulas of Schoenfeld that allow estimation of the expected number of events in the two groups To allow for drop ins noncompliance to control therapy crossover to intervention and noncompliance of the intervention the method of Lachin and Foulkes is used Usage cpower tref n mc r accrual tmin noncomp c 0 noncomp i 0 alpha 0 05 nc ni pr TRUE Arguments tref time at which mortalities estimated n total sample size both groups combined If allocation is unequal so that there are not n 2 observations in each group you may specify the sample sizes in nc and ni mc tref year mortality control r reduction in mc by interv
215. describe runif 200 dig 2 Single variable continuous describe 67 get quantiles 05 10 dfr lt data frame x rnorm 400 y sample c male female 400 TRUE describe dfr Not run d lt sas get mydata special miss TRUE recode TRUE describe d describ ntire data frame attach d 1 describe relig Has special missing values D F M R T attr relig label is Religious preference relig Religious preference Format relig n missing D FMR T unique 4038 26345 33 7 2 8 O none 251 6 1 Jewish 372 9 2 Catholic 1230 30 3 Jehovah s Witnes 25 1 4 Christ Scientist 7 0 5 Seventh Day Adv 17 0 6 Protestant 2025 50 7 other 111 3 Method for describing part of a data frame describe death time agexsex rcs blood pressure describe age sex describe aget sex weights freqs weighted analysis fit lt lrm y agexsex log height describe formula fit describe y agexsex na action na delete report on number deleted for each variable options na detail response TRUE keep missings separately for each x describe y agexsex options na fun response quantile report on dist of y by x NA describe y agexsex same but use quantiles of y by x NA d lt describe my data frame dSage d c age sex d sort names d d2 lt d 20 30 page d2 Test date time formats a
216. diagnosis dx by sex and plot the results with a dot plot aE SR SE HE count lt rep 1 length dx d lt summarize count llist dx sex sum Dotplot dx count sex data d End Not run d lt list x 1 10 a factor rep c a b 5 b structure letters 1 10 label label for a x lt asNumericMatrix d origAttributes matrix2dataFrame x detach dfr Run summarize on a matrix to get column means lt c 1 19 NA lt 101 120 lt chind x y lt c rep 1 10 rep 2 10 summarize z g colMeans na rm TRUE stat name x Also works on an all numeric data frame summarize as data frame z g colMeans na rm TRUE stat name x Q NK X summary formula 269 summary formula Summarize Data for Making Tables and Plots Description summary formula summarizes the variables listed in an S Plus formula computing descriptive statistics including ones in a user specified function The summary statistics may be passed to print methods plot methods for making annotated dot charts and 1at ex methods for typeset ting tables using LaTeX summary formula has three methods for computing descriptive statis tics on univariate or multivariate responses subsetted by categories of other variables The method of summarization is specified in the parameter met hod see details below For the response and cross methods the statistics used to summarize the data may be specified in
217. dians or many other statistics By default quartiles are used for continuous predictors and all levels are used for categorical ones See DETAILS below There is also a plot method for plotting transformation estimates transformations for individ ual bootstrap re samples and pointwise confidence limits for transformations Unless you already have a par mfrow in effect with more than one row or column plot will try to fit the plots on one page A predict method computes predicted values on the original or transformed re sponse scale or a matrix of transformed predictors There is a Funct ion method for producing a list of S functions that perform the final fitted transformations There is also a print method for areg boot objects When estimated means or medians or other statistical parameters are requested for models fit ted with areg boot by summary areg boot or predict areg boot the smearing estimator of Duan 1983 is used Here we estimate the mean of the untransformed response by computing the arithmetic mean of ginverse Ip residuals where ginverse is the inverse of the nonparametric transformation of the response obtained by reverse linear interpolation lp is the linear predictor for an individual observation on the transformed scale and residuals is the en tire vector of residuals estimated from the fitted model on the transformed scales n residuals for n original observations The smearingEst function computes the gen
218. dicating if there is a match or not for its left operand A true vector element indicates no match in left operand false indicates a match Usage x Snin S table Arguments x a vector numeric character factor table a vector numeric character factor matching the mode of x Value vector of logical values with length equal to length of x See Also match in Examples Index Topic IO csv get 40 getZip 112 mdb get 162 Topic algebra solvet 245 Topic aplot envrt coords 31 labcurve 129 minor tick 167 pstamp 197 rlegend 216 scatld 231 show pch 242 subplot 260 Topic arith approxExtrap 4 Topic array print char matrix 190 reShape 213 solvet 245 Topic attribute label 138 valuetags 313 Topic category binconf 17 biVar 19 bpower 23 bystats 27 cut2 47 dataRep 57 describe 60 dropUnusedLevels 68 mApply 157 mChoice 158 mhgr 165 popower 187 reorr 198 samplesize bin 226 337 summarize 261 summary formula 266 varclus 314 wtd stats 319 xy group 323 Topic Character Snins 333 all is numeric 3 capitalize 29 Cs 39 escapeRegex 77 first word 99 format df 100 html 125 latex 142 makeNstr 156 nst 177 rcspline restate 207 sedit 239 string break line 258 translate 306 Topic chron trunc POSIXt 307 yearDays 332 Topic cluster dataRep 57 varclus 314 Topic datasets dataRep 57 Topic data
219. dingZ is the first or second type of spending function truncate value at which Z statistics truncated default is Inf Author s Frank E Harrell Jr References Reboussin DM DeMets DL Kim K M Lan KKG 1996 Programs for computing group sequen tial boundaries using the Lan DeMets method http www biostat wisc edu landemets Reboussin DM DeMets DL Kim K Lan KKG 2000 Computations for group sequential bound aries using the Lan DeMets spending function method Controlled Clinical Trials 21 190 207 See Also gbayes 158 list tree Examples Not run Get boundaries for O Brien Fleming spending function 5 looks alpha 05 b lt ldBands 5 pr FALSE plot b Same but times are irregular and information times are different than test times Use Pocock spending function b lt ldBands times alet Op 8 ee 6 BDSG information c 42 65 83 89 94 spending Pocock Get power calculations u lt ldBands times c 4 6 8 9 95 power 9 uSdrift standardize difference sqrt n per arm needed to provide power 9 obtain detectable standardized difference get sample size per arm two sample binomial get number of events per arm needed to detect a hazard ratio of 1 5 summary u n 50 summary u pl 4 p2 5 summary u hr 1 5 Se SE HOHE HE HE Asymmetric boundaries with different spending functions truncate b lt ldBands 5 sided 3 spending alphaxt phi phi 1 phi
220. e object caption trios vnames c labels names prn TRUE prUnits TRUE summary formula 271 rowlabel cdec 2 ncaption TRUE S3 method for class summary formula reverse print x digits prn any n N pctdig 0 npct c numerator both denominator none excludel TRUE vnames c labels names prUnits TRUE sep abbreviate dimnames FALSE prefix width max nchar lab min colwidth formatArgs NULL round NULL prtest c P stat df name prmsd FALSE long FALSE pdig 3 eps 0 001 S3 method for class summary formula reverse plot x vnames c labels names what c proportion which c both categorical continuous xlim if what proportion c 0 1 else c 0 100 xlab if what proportion Proportion lse Percentage pch c 16 1 2 17 15 3 4 5 0 excludel TRUE dotfont 1 main subtitles TRUE prtest c P stat df name pdig 3 eps 0 001 conType c dot bp raw cex means 0 5 S3 method for class summary formula reverse latex object title first word deparse substitute object digits prn any n N petdig 0 npct c numerator both denominator none npct size scriptsize Nsize scriptsize excludel TRUE vnames c labels names prUni
221. e an optional name to assign to the object and file name prefix if the argument name is not used Details Save creates a temporary version of the object under the name given by the user so that save will internalize this name Then subsequent Load or 1oad will cause an object of the original name to be created in the global environment The name of the R data file is assumed to be the name of the object or the value of name appended with rda For Save compression is used Author s Frank Harrell See Also save load Examples Not run d lt data frame x 1 3 y 11 13 options LoadPath data rda Save d creates data rda d rda Load d reads data rda d rda Save d D creates object D and saves it in D rda End Not run scatld One Dimensional Scatter Diagram Spike Histogram or Density Description scati1d adds tick marks bar codes rug plot on any of the four sides of an existing plot corre sponding with non missing values of a vector x This is used to show the data density Can also place the tick marks along a curve by specifying y coordinates to go along with the x values If any two values of x are within eps w of each other where eps defaults to 001 and w is the span of the intended axis values of x are jittered by adding a value uniformly distributed in jitfrac w jitfracxw where jitfrac defaults to 008 Specifying preserve TRUE invokes jitter2 with a differ
222. e data frame in the left panel of the Object Browser then in the right pane click twice slowly on a variable Use the left arrow and other keys to edit th name Click outside that name field to commit the change You can also rename columns while ina Data Sheet To instead use programming commands to change names use something like names FEV 6 lt smoke assumes you know the positions names FEV names FEV smoking lt smoke names FEV lt edit names FEV data frame create modify check 55 Se SE FE SR OSE OEE n mydframe xl lt NULL mydframe x2 lt NULL The last example is useful if you are changing many names But none of the interactiv approaches such as edit are handy if you will be re importing the dataset after it is updated in its original application This problem can be addressed by saving the new names in a permanent vector in Data w names lt names FEV Then if the data are re imported you can type ames FEV lt new names to rename the variables Step 4 Delete unneeded variables To delete some of the variables you can right click on variable names in the Object Browser s right pane then select Delete You can also set variables to have NULL values which causes the system to delete them We don t need to delete any variables from FEV but suppose we did need to delete some from mydframe
223. e due to the dev ask option If i an internal legend will be placed in the plot region based on legend point at If 1 a legend will be placed in the plot region using the locator option Legend will map points to events via column names by default and if line by is specified lines to groups based on levels of line by titl title for the legend default is title to be used for main plot Only used when legend location o titl cex size of text for legend title Only used when legend location o titl line line location of legend title dictated by mt ext function with outer FALSE option default is 1 0 Only used when legend location o point at location of upper left and lower right corners of legend area to be utilized for describing events via points and text point pch vector of pch values for points representing each event in the legend Default is point pch point text text to be used for describing events the default is setup for a data frame as it will print the names of the columns specified by subset c cex size of text for points and event descriptions Default is 2 5 which is setup for legend location o A much smaller cex is recommended possi bly 0 75 for use with legend location i orlegend location MN bty option to puta box around the legend s default is to have no box legend bty n Option legend bty o will produce a legend box line at if line b
224. e imputations SDs are computed from average variances across subjects match closest same as match weighted with small value of fweighted This example also shows problems with predicted mean matching almost always giving the same imputed values when there is only one predictor regression coefficients change over multiple imputations but predicted values are virtually 1 1 functions of each other set seed 23 x lt runif 200 y lt x runif 200 05 05 r lt resid lsfit x y rmse lt sqrt sum r 2 200 2 sqrt of residual MSE y 1 20 lt NA d lt data frame x y f lt aregImpute x y n impute 10 match closest data d As an aside here is how to create a completed dataset for imputation number 3 as fit mult impute would do automatically In this degenerate case changing 3 to 1 2 4 10 will not alter the results completed lt d imputed lt impute transcan f imputation 3 data d list out TRU pr FALSE check FALSE completed names imputed lt imputed completed sd lt sqrt mean apply f imputed y 1 var DI ss lt c 0 01 02 seq 05 1 length 20 sds lt ss sds 1 lt sd for i in 2 length ss f lt aregImpute x y n impute 10 fweighted ss i sds i lt sqrt mean apply f imputed y 1 var 20 binconf plot ss sds xlab Smoothing Parameter ylab SD of Imputed Values type
225. e is called to add a rug plot datadensity rug spike histogram datadensity hist or smooth density estimate density to the bottom or top of the ECDF If datadensity is not none the default is to place the additional infor mation on top of the x axis side 1 Use side 3 to place at the top of the graph passed to hist Spike a list of optional arguments for hist Spike other parameters passed to plot if add F For data frames other parameters to pass to Ecdf default For Ecdf formula if groups is not used you can also add data density information to each panel s ECDF by specifying the datadensity and optional frac side dens opts arguments minimum number of unique values before an ECDF is drawn for a variable in a data frame Default is 10 set to TRUE to draw the number of NAs in larger letters in the middle of the plot for Ecdf data frame By default variable labels are used to label x axes Set vnames names to instead use variable names method for computing the empirical cumulative distribution See wtd Ecdf The default is to use the standard i n method as is used by the non Trellis versions of Ecdf a function to transform the cumulative proportions for the Trellis type usage of Ecdf the usual Trellis Lattice parameters with groups causing Ecdf formula to overlay multiple ECDFs on one panel Ecdf 75 Value for Ecdf default an invisible list with elements x and y givin
226. e it is evaluated at an equally spaced set of x points over the observed range If fewer than 3 unique x values are in a curve those points are used and smoothing is not done Usage curveRep x y id k n 5 kxdist 5 k 5 p 5 forcel TRUE metric c euclidean manhattan smooth FALSE extrap FALSE pr FALSE S3 method for class curveRep print x e S3 method for class curveRep plot x curveSmooth x y id p NULL pr TRUE Arguments x id kn kxdist forcel metric smooth which 1 length res method c all lattice Ea m NULL probs c 5 25 75 nx NULL fill TRU idcol NULL freq NULL plotfreq FALSE xlim range x ylim range y xlab x ylab y a numeric vector typically measurement times For plot curveRep is an object created by curveRep a numeric vector of response values a vector of curve subject identifiers the same length as x and y number of curve sample size groups to construct curveRep tries to divide the data into equal numbers of curves across sample size intervals maximum number of x distribution clusters to derive using clara maximum number of x y profile clusters to derive using clara number of x points at which to interpolate y for profile clustering For curveSmooth is the number of equally spaced points at which to evaluate the lowess smooth and if p is omitted the smooth is ev
227. e job curveRep 45 Value a new data frame Author s Frank Harrell Vanderbilt University See Also sas get data frame cleanup import read csv strptime POSIXct Date Examples Not run dat lt csv get myfile csv Read a csv file with junk in the first row variable names in the second long variable labels in the third and junk in the 4th row dat lt csv get myfile csv vnames 2 labels 3 skip 4 End Not run curveRep Representative Curves Description curveRep finds representative curves from a relatively large collection of curves The curves usually represent time response profiles as in serial longitudinal or repeated data with possibly unequal time points and greatly varying sample sizes per subject After excluding records contain ing missing x or y records are first stratified into kn groups having similar sample sizes per curve subject Within these strata curves are next stratified according to the distribution of x points per curve typically measurement times per subject The clara clustering partitioning function is used to do this clustering on one two or three x characteristics depending on the minimum sample size in the current interval of sample size If the interval has a minimum number of unique values of one clustering is done on the single x values If the minimum number of unique x values is two clustering is done to create groups that are similar on both min
228. e than one was imported Value If there is more than one dataset in the transport file other than the PROC FORMAT file the result is a list of data frames containing all the non PROC FORMAT datasets Otherwise the result is the single data frame There is an exception if out is specified that causes separate R save files to be written and the returned value to be a list corresponding to the SAS datasets with key PROC CONTENTS information in a data frame making up each part of the list sasdsLabels returns a named vector of dataset labels with names equal to the dataset names Author s Frank E Harrell Jr See Also read xport label sas get Dates DateTimeClasses chron lookup xport contents describ Examples Not run SAS code to generate test dataset libname y SASV5XPT test2 xpt PROC FORMAT VALUE race l green 2 blue 3 purple RUN PROC FORMAT CNTLOUT format RUN x Name e g format unimportant data test LENGTH race 3 age 4 age 30 label age Age at Beginning of Study race 2 dl 3mar2002 d atl Bmar2002 9 31 02 dt t1 11 13 45 t output age 31 race 4 dl 3jun2002 d dtl 3 jun2002 9 42 07 dt CISTE Las Ls ty Save 233 output format dl mmddyyl0 dtl datetime tl time race race run data z LENGTH x3 3 x4 4 x5 5 x6 6 x7 7 x8 8 DO i 1 TO 100 x3 ranuni 3 x4 ranuni 5 x5 ranuni 7 x6 ranuni 9 I 3 r r r x7
229. e used in S Plus 5 or later The problem was caused by use of the label function to set a variable s class to labelled These classes are removed as the S version 4 language does not support multiple inheritance Failure to run data frames through one of the two functions when these conditions apply will result in simple numeric variables being set to factor in some cases Extraneous AsIs classes are also removed For S Plus a function export DataSt ripped is provided that allows exporting of data to other systems by removing attributes label imputed format units and comment It calls exportData after stripping these attributes Otherwise exportData will fail upData 313 The dataframeReduce function removes variables from a data frame that are problematic for certain analyses Variables can be removed because the fraction of missing values exceeds a thresh old because they are character or categorical variables having too many levels or because they are binary and have too small a prevalence in one of the two values Categorical variables can also have their levels combined when a level is of low prevalence Usage cleanup import obj labels lowernames FALSE force single TRUE force numeric TRUE rmnames TRUE big 1e20 sasdict pr datevars NULL datetimevars NULL dateformat SF fixdates c none year charfactor FALS Gl upData object rename drop labels units levels force single TRUE
230. e values specifying comparison groups strata the stratification variable conf int confidence level x an object created by mhgr or lrcum a frequency of true positive tests b frequency of false positive tests frequency of false negative tests d frequency of true negative tests dec number of places to the right of the decimal to print for Lrcum addtitional arguments to be passed to other print functions Details Uses equations 4 and 13 from Greenland and Robins Value a list of class mhgr or of class lrcum mhgr 169 Author s Frank E Harrell Jr f harrell vanderbilt edu References Greenland S Robins JM 1985 Estimation of a common effect parameter from sparse follow up data Biometrics 41 55 68 Altman DG Machin D Bryant TN Gardner MJ Eds 2000 Statistics with Confidence 2nd Ed Bristol BMJ Books 105 110 Simel DL Samsa GP Matchar DB 1991 Likelihood ratios with confidence sample size estima tion for diagnostic test studies J Clin Epi 44 763 770 See Also logrank Examples Greate Migraine dataset used in Example 28 6 in the SAS PROC FREQ guide d lt expand grid response c Better Same treatment c Active Placebo sex c female male dScount lt c 16 11 5 20 12 16 7 19 d Expand data frame to represent raw data r lt rep 1 8 dScount dad lt d r with d mhgr response Better treatment sex Discrete survival ti
231. e y axis See the body of the function for type specific default values for many of the parameters This function has not been tested for color output on Windows systems setps is a function that makes small postscript plots with minimal surrounding white space suit able for inclusion in books and reports Internally setps uses and defines the psfig func tion by Antonio Possolo antonio atc boeing com set ps is especially good for including plots in LaTeX setps creates a temporary function in the session database that when invoked will ps slide convert a completed postscript graphics file to a Adobe Acrobat pdf if you have Ghostscript in stalled and in your path so that the gs command is available in UNIX or gswin32c is avail able for Windows NT Invoke topdf by the command topdf or if you want to convert a graphic other than the last one created run topdf filename to convert filename ps to filename pdf Iftrellis TRUE setps invokes trellis device witha postscript device argument and it does not set any of the par parameters Arguments 3 4 5 7 9 and 10 to setps are ignored if trellis TRUE If options setpsPrefix name is defined the name string will be prefixed to the file name used by setps setpdf uses a similar option setpdfPrefix setps and setpdf set par mgp c 2 0 4 0 if trellis FALSE setpdf is similar to set ps but for making Adobe Acrobat PDF graphics files directly There are a few
232. eanup import understands specifying fixdates allows corrections of certain formatting inconsistencies before the fields are attempted to be con verted to dates the default is to assume that the dateformat is followed for all observation for datevars Currently fixdates year is imple mented which will cause 2 digit or 4 digit years to be shifted to the alternate number of digits when dateform is the default SF or is Sy Sm d Sm Sd ZSy or Sm Sda SY Two digits years are padded with 20 on the left Set dateformat to the desired format not the exceptional format charfactor set to TRUE to change character variables to factors if they have at least two characters in an observation but have fewer than n 2 unique values for upData one or more expressions of the form variable expression to derive new variables or change old ones For exportDataStripped optional arguments that are passed to exportData rename list or named vector specifying old and new names for variables Variables are renamed before any other operations are done For example to rename variables age and sex to respectively Age and gender specify rename list age Age sex gender or rename c age drop a vector of variable names to remove from the data frame units a named vector or list defining units attributes of variables in no specific order levels a named list defining levels attributes for factor variables in no specific orde
233. east one vector was given to the result is a list containing k matrices where k one plus the number of vectors in If x is a list or data frame the same type of object is returned If x is a vector and id is a data frame a data frame will be the result Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu See Also reshape as vector matrix dimnames outer table 218 reShape Examples set seed 1 Solder lt factor sample c Thin Thick 200 TRUE c Thin Thick Opening lt factor sample c S M L 200 TRUE ECAS TAM ELOY tab lt table Opening Solder tab reShape tab attach tab do further processing if R g lt crosstabs Solder Opening data solder subset skips gt 10 rowpct lt 100 attr g marginals N RowTotal compute row pects rowpct lt reShape rowpct note names Solder and Opening came originally from formula x given to crosstabs dotplot Solder rowpct groups Opening panel panel superpose data r An example where a matrix is created from irregular vectors follow lt data frame id c a a b b b d month c 1 2 T 25 3 2i 5 cholesterol c 225 226 320 319 318 270 follow attach follow reShape cholesterol id id colvar month detach follow Could have done reShape cholesterol triglyceride trig id id
234. ects returning the prior density at those values a function computing the posterior CDF of the treatment effect delta such as a function created by goayesMixPost with what cdf a true unknown single treatment effect to detect the variance of the statistic x e g s 52 1 n1 1 n2 Neither x nor v need to be defined to gbayesMixPost as they can be defined at run time to the function created by gbayesMixPost number of future observations in group 1 for obtaining a predictive distribution number of future observations in group 2 vector of 2 x axis limits Default is the mean of the posterior plus or minus 6 standard deviations of the posterior vector of 2 y axis limits Default is the range over combined prior and posterior densities label for x axis Default is z optional arguments passed to labcurve from plot gbayes the minimum worthwhile treatment difference to detech The default is zero for a plain uninteristing null hypothesis type I error or more accurately one minus the confidence level for a two sided confidence limit for the treatment effect upper limit of integration over the prior distribution multiplied by the normal likelihood for the treatment effect statistic Default is infinity argument to pass to prior from integrate through gbayes2 Inside of power the argument must be named prior aux if it exists You can pass multiple parameters by passing prior aux as a list and pulling off elements of
235. ed For predict areg boot statistic terms returns a matrix of transformed predictors statistic can also be any S function that computes a single value on a vector of values such as statistic var Note that in this case the function name is not quoted a single quantile of the original response scale to estimate when statistic quantile or for QUantile areg boot 2 vector of y axis limits set to FALSE to not plot any bootstrapped transformations Set it to an integer k to plot the first k bootstrap estimates 290 transace col boot color for bootstrapped transformations lwd boot line width for bootstrapped transformations conf int confidence level 0 1 for pointwise bootstrap confidence limits and for esti mated effects of predictors in summary areg boot The latter assumes nor mality of the estimated effects values a list of vectors of settings of the predictors for predictors for which you want to overide settings determined from probs The list must have named compo nents with names corresponding to the predictors Example values list x1 c 2 4 6 8 x2 c 1 0 1 specifies that summary is to estimate the effect on y of changing x1 from 2 to 4 2 to 6 2 to 8 and separately of changing x2 from 1 to 0 and 1 to 1 adj to a named vector of adjustment constants for setting all other predictors when examining the effect of a single predictor in summary The more nonlinear is the transformation of y the m
236. ed test data with specialized dates in columns 4 thru 10 the following code could be used as numeric dates testdata 4 10 This will convert the columns to numeric julian dates based on the function s default origin of January 1 1960 If original dates are in class dates or julian form no extra work is necessary In the survival analysis the data typically come in two columns one column containing survival time and the other containing censoring indicator or event code The event convert function converts this type of data into multiple columns of event times one column of each event type suitable for the event chart function Side Effects an event chart is created on the current graphics device If legend plot TRUE and legend location o a one page legend will precede the event chart Please note that par parameters on completion of function will be reset to par parameters existing prior to start of function Author s J Jack Lee and Kenneth R Hess Department of Biostatistics University of Texas M D Anderson Cancer Center Houston TX 77030 jjlee mdanderson org khess mdanderson org Joel A Dubin Department of Statistics University of Waterloo jdubin uwaterloo ca References Lee J J Hess K R Dubin J A 2000 Extensions and applications of event charts The American Statistician 54 1 63 70 event chart 89 Dubin J A Lee J J Hess K R 1997 The Utility of Event Charts Pr
237. eding imputation for the current multiple im putation repetition Neither of these bootstrap procedures assume normality or even symmetry of residuals For sometimes missing categorical variables optimal scores are computed by adding the best guess predicted mean score to random residuals off this score Then categories having scores closest to these predicted scores are taken as the random multiple imputations impcat tree or rpart are not currently allowed with n impute The literature recommends using n impute 5 or greater transcan provides only an approximation to multiple impu tation especially since it freezes the imputation model before drawing the multiple imputations rather than using different estimates of regression coefficients for each imputation For multiple imputation the aregImpute function provides a much better approximation to the full Bayesian approach while still not requiring linearity assumptions When you specify n impute to transcan you can use fit mult impute to re fit any model n impute times based on n impute completed datasets if there are any sometimes missing variables not specified to transcan some observations will still be dropped from these fits After fitting n impute models fit mult impute will return the fit object from the last imputation with coefficients replaced by the average of the n impute coefficient vec tors and with a component var equal to the imputation corrected
238. efaults to reverse automatically The method cross option allows for a multivariate dependent variable and for up to three independents Continuous independent variables those with at least continuous unique values are automatically divided into g quan tile groups The independents are cross classified and marginal statistics may optionally be computed The output of summary formula in this case is a data frame containing the independent variable combinations with levels of A11 corresponding to marginals and the corresponding summary statistics in the matrix S The output data frame is suitable for direct use in trellis The print and latex typesetting methods for this method allows for a special two way format if there are two right hand variables For method reverse setting overall TRUE makes a new column with overall statistics for the whole sample Formet hod cross overall TRUI the default results in all marginal statistics being computed For trellis displays usually multi panel dot plots these marginals just form other cate gories For response the default is overal1 TRUE causing a final row of global summary statistics to appear in tables and dot charts If test TRUE these marginal statistics are ignored in doing statistical tests specifies the threshold for when a variable is considered to be continuous when there are at least cont inuous unique values factor variables are always considered to
239. el prepanel xlab ylab see trellis args xlab and ylab get default values from label at tributes xscale allows one to use the default scales but specify only the x component of it for Dotplot method defaults to bars to draw error bar type plots See meaning of other val ues above method can be a function Specifying method quantile methodArgs list probs c 5 25 75 is the same as specifying method quantile without specifying probs methodArgs a list containing optional arguments to be passed to the function specified in method label curves set to FALSE to suppress invocation of Labcurve to label primary curves where they are most separated or to draw a legend in an empty spot on the panel You can also set label curves to a list of options to pass to labcurve These options can also be passed as to xYplot See the examples below abline a list of arguments to pass to panel abline foreach panel e g list a 0 b 1 col 3 to draw the line of identity using color 3 probs a vector of three quantiles with the quantile corresponding to the central line listed first By default probs c 5 25 75 You can also specify probs through methodArgs list probs nx number of target observations for each x group see cut 2 m argument nx defaults to the minimum of 40 and the number of points in the current stratum divided by 4 Set nx FALSE or nx 0 if x is already discrete and requires no grouping cap the half
240. el b lt B Label varclus Variable Clustering Description Does a hierarchical cluster analysis on variables using the Hoeffding D statistic squared Pearson or Spearman correlations or proportion of observations for which two variables are both positive as similarity measures Variable clustering is used for assessing collinearity redundancy and for separating variables into clusters that can be scored as a single variable thus resulting in data re duction For computing any of the three similarity measures pairwise deletion of NAs is done The clustering is done by hclust A small function naclus is also provided which depicts similarities in which observations are missing for variables in a data frame The similarity measure is the fraction of NAs in common between any two variables The diagonals of this s im matrix are the fraction of NAs in each variable by itself naclus also computes na per obs the number 318 varclus of missing variables in each observation and mean na a vector whose ith element is the mean number of missing variables other than variable i for observations in which variable i is missing The naplot function makes several plots see the which argument So as to not generate too many dummy variables for multi valued character or categorical predictors varclus will automatically combine infrequent cells of such variables using an auxiliary function combine levels that is defined here plotMul
241. els beside curves on top draws labels on top of the curves espe cially good when using keys arrow draws arrows connecting labels to the curves mouse or locator positions labels according to mouse clicks If keys is specified and is an integer vector or is lines method defaults to on top If keys is character method defaults to offset Set method none to suppress all curve labeling and key drawing which is useful when p1 TRUE and you only need labcurve to draw the curves and the rest of the basic graph For largest empty specifies the method determining the best rectangle among all those that qualify with respect to width and height Use method area the default to find the one having the largest area or met hod maxdim to use the last rectangle searched that had both the largest width and largest height over all previous rectangles This causes keys symbols or short text to be drawn on or beside curves and if keyloc is not equal to none a legend to be automatically drawn The legend links keys with full curve labels and optionally with colors and line types Set keys to a vector of character strings or a vector of integers specifying plotting character och values see points For the latter case the default behavior is to plot the symbols periodically at equally spaced x coordinates When keys is specified keyloc specifies how the legend is to be positioned for drawing using the key function in trellis
242. ent and 0 for a censored observa tion this indicator can be represented by any two numbers made explicit by the surv ind argument Two element vector representing respectively the number for an event as listed in surv col followed by the number for a censored observation Default is traditional survival data represention i e c 1 0 Subset of rows of original matrix or data frame data to place in event his tory graph Logical arguments may be used here e g treatment ar a if the data frame data has been attached to the search directory Column s in data representing the time when change of time dependent co variate or time dependent covariate function occurs There should be a unique non NA entry in the column for each such change along with corresponding cov cols column entry representing the value of the covariate or function at that change time Default is NULL meaning no time dependent covariate information will be presented in the graph Column s in data representing the level of the time dependent covariate or time dependent covariate function There should be a unique non NA column 94 num colors cut cov colors cens mult cens cens cens density end cens mark event history entry representing each change in the level along with a corresponding cov time cols column entry representing the time of the change Default is NULL meaning no time dependent covariate inform
243. ent logic of jittering Allows plotting random sub segments to handle very large x vectors see t frac jitter2 is a generic method for jittering which does not add random noise It retains unique values and ranks and randomly spreads duplicate values at equidistant positions within limits of enclosing values jitter2 is especially useful for numeric variables with discrete values like rating scales Missing values are allowed and are returned Currently implemented methods are scatld 235 jitter2 default for vectors and jitter2 data frame which returns a data frame with each numeric column jittered datadensity is a generic method used to show data densities in more complex situations In the Design library there is a datadensity method for use with plot Design Here another datadensity method is defined for data frames Depending on the which argument some or all of the variables in a data frame will be displayed with scat1d used to display continuous variables and by default bars used to display frequencies of categorical character or discrete numeric variables For such variables when the total length of value labels exceeds 200 only the first few characters from each level are used By default datadensity data frame will construct one axis i e one strip per variable in the data frame Variable names appear to the left of the axes and the number of missing values if greater than zero appear to the right of the axes An
244. ent of time for the third default is 1 spending function for the upper bounds if sided 3 Defaults to same spending function for lower bounds parameter for third and fourth spending functions if sided 3 Default is phi a value at which to truncate Z critical values so that early looks will have some power without really affecting the overall a level Default is no truncation A power for which to compute a drift parameter useful in sample size calcula tions set to FALSE to supress the actual output of the 1d98 program an object created by 1dBands or summary 1dBands x axis label y axis label an optional list with two components times and z values to add as points to the first plot default is lb causing both points and lines to be drawn for boundaries Use type 1 for example to suppress drawing circles at points an optional character vector to be used to label time points corresponding to those generated by 1dBands an object created by 1dBands standardized difference to detect probability of event in group 1 probability of event in group 2 to compare with p1 in order to obtain sample size for two sample binomial hazard ratio to detect to obtain needed number of events at end of study for either treatment arm using the logrank test number of events per treatment arm at end of study to obtain detectable hazard ratio mean of actual probabilities of events in two treatment arms standard deviation of an obser
245. ented by a specific obser vation of all the variables in points points to assign to successive elements of The default is 1 2 p where p is the number of elements If you specify one number for points that number will be duplicated i e equal weights are assumed na rm set to TRUE to remove NAs from consideration when processing each row of the matrix of variables in For fun max na rm TRUE is the default since score binary assumes that a hierarchical scale is based on available information Otherwise na rm FALSE is assumed For fun mean you may want to specify na rm TRUE retfactor applies if fun max in which case ret fact or TRUE makes score binary return a factor object since a hierarchical scale implies a unique choice Value a factor object if ret factor TRUE and fun max or a numeric vector otherwise Will not contain NAs if na rm TRUE unless every variable in a row is NA Ifa factor object is returned it has levels none followed by character string versions of the arguments givenin See Also any sum max factor 242 sedit Examples set seed 1 age lt rnorm 25 70 15 previous disease lt sample 0 1 25 TRUE Hierarchical scale highest of l age gt 70 2 previous disease score binary age gt 70 previous disease retfactor FALSE Same as above but return factor variable with levels none age gt 70 previous disease score binary age gt 70 previous disease Add
246. enting each event If similar events are listed in multiple columns e g regular visits or a recurrent event repeated pch values may be listed in the vector e g c 2 4 rep 183 3 If length point pch lt length subset c point pch will be re peated until lengths are equal a warning message will verify this condition point cex vector of size of points representing each event If length point cex lt length subset c point cex will be repeated until lengths are equal a warning message will verify this condition point col vector of colors of points representing each event If length point col lt length subset c point col will be repeated until lengths are equal a warning message will verify this condition point cex mult a single number may be non integer which is the base multiplier for the value of the cex of the plotted points when interest lies in a variable size allowed for certain points as a function of the quantity of the variable s in the dataset specified in the point cex mult var argument multiplied by original point cex value and then the value of interest for an individual from the point cex mult var argument used only when non NA ar guments are provided to point cex mult var default is 1 point cex mult var vector of variables to be used in determining what point cex mult is multiplied by for determining size of plotted points from possibly a subset of subset c variables when interes
247. ention accrual duration of accrual period tmin minimum follow up time noncomp c non compliant in control group drop ins noncomp i non compliant in intervention group non adherers alpha type I error probability A 2 tailed test is assumed nc number of subjects in control group ni number of subjects in intervention group nc and ni are specified exclusive of n pr set to FALSE to suppress printing of details cpower 41 Details For handling noncompliance uses a modification of formula 5 4 of Lachin and Foulkes Their method is based on a test for the difference in two hazard rates whereas cpower is based on testing the difference in two log hazards It is assumed here that the same correction factor can be approximately applied to the log hazard ratio as Lachin and Foulkes applied to the hazard difference Note that Schoenfeld approximates the variance of the log hazard ratio by 4 m where m is the total number of events whereas the George Desu method uses the slightly better 1 m1 1 m2 Power from this function will thus differ slightly from that obtained with the SAS samsizc program Value power Side Effects prints Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Peterson B George SL Controlled Clinical Trials 14 511 522 1993 Lachin JM Foulkes MA Biometrics 42 507 519 1986 Schoenfeld D Biometrics 39 499 503 1983 See A
248. environment which will take precedence To later make this function keep unused levels on subsetting issue opt ions drop unused levels FALSE or just remove this temporary function from the environment by issuing remove factor pos Globall 72 Usage dropUnusedLevels Author s Ecdf Frank Harrell and Jens Oehlschlaegel See Also factor factor Examples Not run x lt factor c a b c x 1 2 keeps level c dropUnusedLevels x 1 2 no c any more End Not run Ecdf Empi rical Cumulative Distribution Plot Description Computes coordinates of cumulative distribution function of x and by defaults plots it as a step function A grouping variable may be specified so that stratified estimates are computed and by default plotted If there is more than one group the labcurve function is used by default to label the multiple step functions or to draw a legend defining line types colors or symbols by linking them with group labels A weights vector may be specified to get weighted estimates Specify normwt to make weights sum to the length of x after removing NAs Other wise the total sample size is taken to be the sum of the weights Ecdf is actually a method and Ecdf default is what s called for a vector argument Ecdf data frame is called when the first argument is a data frame This function can automatically set up a matrix of ECDFs and
249. eral smearing estimate For efficiency smearingEst recognizes that quantiles are transformation preserving i e when one wishes to estimate a quantile of the untransformed distribution one just needs to compute the in verse transformation of the transformed estimate after the chosen quantile of the vector of residuals is added to it When the median is desired the estimate is ginverse Ip median residuals See the last example for how smearingEst can be used outside of areg boot Mean is a generic function that returns an S function to compute the estimate of the mean of a vari able Its input is typically some kind of model fit object Likewise Quant ile is a generic quantile function producing function Mean areg boot and Quantile areg boot create functions of a vector of linear predictors that transform them into the smearing estimates of the mean or quan tile of the response variable respectively Quantile areg boot produces exactly the same value as predict areg boot or smearingEst Mean approximates the mapping of linear predictors to means over an evenly spaced grid of by default 200 points Linear interpolation is used between these points This approximate method is much faster than the full smearing estima tor once Mean creates the function These functions are especially useful in nomogram Design see the example on hypothetical data Usage Gl transace x monotonic NULL categorical NULL binary NULL p
250. erpolation is used within each interval in which the function is monotonic and the result is a matrix with number of columns equal to the number of monotonic intervals If a requested y is not within any interval the extreme x that pertains to the nearest extreme y is returned Specifying what sample to the returned function will cause a vector to be returned instead of a matrix with elements taken as a random choice of the possible inverses james stein computes James Stein shrunken estimates of cell means given a response variable which may be binary and a grouping indicator km quick provides a fast way to invoke survfit kmin the survival package to get Kaplan Meier estimates for a single stratum for a vector of time points if t imes is given or to get a vector of survival time quantiles if q is given lm fit qr bare is a fast stripped down function for computing regression coefficients resid uals R and fitted values It uses the Fortran routines dqrls mat xv multiplies a matrix by a vector handling automatic addition of intercepts if the matrix does not have a column of ones If the first argument is not a matrix it will be converted to one Misc nomiss returns a data frame if its argument is one with rows corresponding to NAs removed or it returns a matrix with rows with any element missing removed outerText uses text to put test strings in left or right margins It temporarily sets par xpd NA if usin
251. ertical vs mgp axis also fixes a bug in axis 2 horizontal tick marks that causes it to assume las 1 mgp axis labels is used so that different spacing between tick marks and axis tick mark labels may be specified for x and y axes Use mgp axis labels default to set defaults Users can set values manually using mgp axis labels x y where x and y are 2nd value of par mgp to use Use mgp axis labels type w to retrieve values where w x y x and y xy to get 3 mgp values first 3 types or 2 mgp axis labels Usage mgp axis side at NULL mgp mgp axis labels type if side 1 side 3 x else y axistitle NULL mgp axis labels value type c xy x y x and y mgp axis 167 Arguments side at see par arguments passed through to axis mgp see par axistitle if specified will cause axist ite to be drawn on the appropriate axis as a title value vector of values to which to set system option mgp axis labels type see above Value mgp axis labels returns the value of mgp only the second element of mgp if type xy or a list with elements x and y if type x or y each list element being a 3 vector for the appropriate axis if value is not specified otherwise it returns nothing but the system option mgp axis labels is set mgp axis returns nothing Side Effects mgp axis labels stores the value in the system option mgp axis labels Autho
252. ervention group a function of a time vector which returns the survival probabilities for the control group at those times assuming that all patients are compliant a function of time which specifies the intervention control hazard ratio treat ment effect 252 group times surv test nsim alpha pr dropin dropout tmax qtmax mplot what dropsep lty col xlim ylim spower an object of class Quantile2 created by Quantile2 a Surv object or other two column matrix for right censored survival times group indicators have length equal to the number of rows in S a vector of two times a vector of two survival probabilities any function of a Surv object and a grouping variable which computes a chi square for a two sample censored data test The default is Logrank number of simulations to perform default 500 type I error default 05 set to FALSE to cause spower to suppress progress notes for simulations Set to FALSE to prevent Quantile2 from printing tmax when it calculates tmax a function of time specifying the probability that a control subject actually be comes an intervention subject at the corresponding time a function of time specifying the probability of an intervention subject dropping out to control conditions number of time points used for approximating functions default is 7500 maximum time point to use in the grid of m times Default is the time such that sc
253. es derives the Gaussian posterior and optionally the predictive distribution when both the prior and the likelihood are Gaussian and when the statistic of interest comes from a 2 sample problem This function is especially useful in obtaining the expected power of a statistical test averaging over the distribution of the population effect parameter e g log hazard ratio that is obtained using pilot data gbayes is also useful for summarizing studies for which the statistic of interest is approximately Gaussian with known variance An example is given for comparing two proportions using the angular transformation for which the variance is independent of unknown parameters except for very extreme probabilities A plot method is also given This plots the prior posterior and predictive distributions on a single graph using a nice default for the x axis limits and using the 1abcurve function for automatic labeling of the curves gbayesz2 uses the method of Spiegelhalter and Freedman 1986 to compute the probability of correctly concluding that a new treatment is superior to a control By this we mean that a l alpha normal theory based confidence interval for the new minus old treatment effect lies wholly to the right of delta w where delta w is the minimally worthwhile treatment effect which can be zero to be consistent with ordinary null hypothesis testing a method not always making sense This kind of power function is averaged over a prior distr
254. es of y as a function of x with the quantiles specified by the probs argument This cannot be used to draw a group containing n 1 nx applies ifm quantiles See xYplot probs 3 vector of probabilities with the central quantile first Default uses quartiles fill for method all by default if a sample size x distribution stratum did not have enough curves to stratify into k x y profiles empty graphs are drawn so that a matrix of graphs will have the next row starting with a different sample size range or x distribution See the example below idcol a named vector to be used as a table lookup for color assignments does not apply whenm quantile The names of this vector are curve ids and the values are color names or numbers freq a named vector to be used as a table lookup for a grouping variable such as treat ment The names are curve ids and values are any values useful for grouping in a frequency tabulation plotfreq set to TRUE to plot the frequencies from the freq variable as horizontal bars instead of printing them Applies only tomethod lattice By default the largest bar is 0 1 times the length of a panel s x axis Specify plot freq 0 5 for example to make the longest bar half this long xlim ylim xlab ylab plotting parameters Default ranges are the ranges in the entire set of raw data given to curveRep arguments passed to other functions Details In the graph titles for the default graphic
255. esponding y values curves is mandatory except when method mouse or locator in which case labels is mandatory Each list in curves may optionally have any of the parameters type lty lwd or col for that curve as defined below see one of the last examples Z a two element list specifying the coordinate of the center of the key e g Locator 1 to use the mouse for positioning labels For labcurve a vector of character strings used to label curves which may contain newline characters to stack labels vertically The default labels are taken from the names of the curves list Setting labels FALSE will sup press drawing any labels for Labcurve only For putKey and putKeyEmpty is a vector of character strings specifying group labels labcurve width height method keys keyloc type 135 for putKeyEmpty and largest empty x and y are same length vectors specifying points that have been plotted x can also be an object created by drawPlot For drawP lot isa series of invocations of Points and Curve see example Any number of point groups can be defined in this way For Abl ine these may be any arguments to abline For labcurve other parameters to pass to text For plot drawPlot other parameters to pass to set ps for largest empty specifies the minimum allowable width in x units and the minimum allowable height in y units offset the default offsets labels at largest gaps between curves and draws lab
256. esponse in which case the response variable may be mul tivariate and any number of statistics may be used to summarize them Here the responses are summarized separately for each of any number of independent variables Continuous independent variables see the cont inuous parameter below are automatically stratified into g see below quantile groups if you want to control the discretization for selected variables use the cut 2 function on them Otherwise the data are subsetted by all levels of discrete right hand side variables For multivariate responses subjects are considered to be missing if any of the columns is missing The method reverse option is typically used to make baseline character istic tables for example The single left hand side variable must be categorical e g treatment and the right hand side variables are broken down one at a summary formula overall continuous na rm na include nmin test 273 time by the dependent variable Continuous variables are described by three quantiles quartiles by default along with outer quantiles used only for scaling x axes when plotting quartiles all are used when plotting box percentile plots and categorical ones are described by counts and percentages If there is no left hand side variable summary assumes that there is only one group in the data so that only one column of summaries will appear If there is no dependent variable in formula method d
257. fault SF or is Sy Sm d Sm Sd Sy or Sm Sda SY Two digits years are padded with 20 on the left Set dateformat to the desired format not the exceptional format comment char a character vector of length one containing a single character or an empty string Use to turn off the interpretation of comments altogether autodates Set to true to allow function to guess at which variables are dates allow a vector of characters allowed by R that should not be converted to periods in variable names By default underscores in variable names are converted to periods as with R before version 1 9 charfactor set to TRUE to change character variables to factors if they have at least two characters in an observation but have fewer than n 2 unique values sep field separator defaults to comma skip number of records to skip before data start Required if vnames or labels is given vnames number of row containing variable names default is one labels number of row containing variable labels default is no labels arguments to pass to read csv other than skip and sep Details csv get reads comma separated text data files allowing optional translation to lower case for variable names after making them valid S names Original possibly non legal names are taken to be variable labels if labels is not specified Character or factor variables containing dates can be converted to date variables cleanup import is invoked to finish th
258. for class transcan predict object newdata iter max 50 eps 0 01 curtail TRUE type c transformed original inverse tolInverse check FALSE Function object transcan 297 S3 method for class transcan Function object prefix suffix where 1 invertTabulated x y freq rep 1 length x aty name value inverse c linearInterp sample tolInverse 0 05 rule 2 Varcov object S3 me S3 me S3 me S3 me Varcov ob Varcov ob Varcov ob Defaul Varcov ob t S3 method ject regcoef only FALS Gl hod for class lm ject hod for class glm JECU wise hod for class multinom ject hod for class fit mult impute Varcov object Arguments x formula fitter a matrix containing continuous variable values and codes for categorical vari ables The matrix must have column names dimnames If row names are present they are used in forming the names attribute of imputed values if imputed TRUE x may also be a formula in which case the model ma trix is created automatically using data in the calling frame Advantages of using a formula are that categorical variables can be determined automat ically by a variable being a factor variable and variables with two unique levels are modeled asis Variables with 3 unique values are considered to be categorical if a formula is specified For a formula
259. for improveProb x2 second predictor a probability for improveProb S a possibly right censored Surv object If S is a vector instead it is converted to a Surv object and it is assumed that no observations are censored outx set to T to exclude pairs tied on x1 or x2 from consideration method see above y a binary 0 1 outcome variable x the result from improveProb digits number of significant digits for use in printing the result of improveProb conf int level for confidence limits unused 206 Details Tcorrp cens If x1 x2 represent predictions from models these functions assume either that you are using a separate sample from the one used to build the model or that the amount of overfitting in x1 equals the amount of overfitting in x2 An example of the latter is giving both models equal opportunity to be complex so that both models have the same number of effective degrees of freedom whether a predictor was included in the model or was screened out by a variable selection scheme Value a vector of statistics a list of statistics for improveP rob Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Pencina MJ D Agostino Sr RB D Agostino Jr RB Vasan RS 2007 Evaluating the added predic tive ability of a new marker From area under the ROC curve to reclassification and beyond Stat in Med 26 See Also rcorr cens somers2 Surv val prob Ex
260. for printing and capturing ver batim text are the options to have in effect only for when print is executed De faults are current options For dvi these specify the paper width and height in inches if nomargins TRUE with defaults of 5 5 and 7 respectively set to TRUE to have dvi print to the S Plus session the LaTeX log file set to FALSE to not use multicolumn in header of table set to FALSE to use default LaTeX margins when making the dvi file If running under Windows and using MikTeX latex and yap must be in your system path and yap is used to browse dvi files created by latex You should install the geometry and ctable styles in MikTeX to make optimum use of latex On Mac OS X you may have to append the usr texbin directory to the system path Thanks to Kevin Thorpe kevin thorpe utoronto ca one way to set up Mac OS X is to install X11 and X11SDK if not already installed start X11 within the R GUI and issue the command Sys setenv latex 153 PATH paste Sys getenv PATH usr texbin sep To avoid any com plications of using X11 under MacOS users can install the TeXShop package which will associate dvi files with a viewer that displays a pdf version of the file after a hidden conversion from dvi to pdf System options can be used to specify external commands to be used Defaults are given by options xdvicmd xdvi oroptions xdvicmd yap options dvipscmd
261. frame pre test post test sex sample c male female 100 TRUE 76 eip Ecdf m group mS sex datadensity rug freqs lt sample 1 10 1000 TRUE Ecdf ch weights freqs weighted estimates Trellis Lattice examples fl region lt factor sample c Europe USA Australia 100 TRU year lt factor sample 2001 2002 1000 TRUE Ecdf ch regionxyear groups sex Key draw a key for sex at the default location Key locator 1l user specified positioning of key age lt rnorm 1000 50 10 Ecdf ch equal count age groups sex use overlapping shingles Ecdf ch sex datadensity hist side 3 add spike histogram at top eip Edit In Place Description Invokes edit on the object name and stores the resulting edited object in place of the original even if this is a remote place as long as the user has write access This is more useful in S Plus than in R Usage eip name Arguments name an object usually a function Value none Author s Frank Harrell See Also edit Examples Not run eip summary formula make temporary bug fix in central area End Not run equalBins 77 equalBins Multicolumn Formating Description Expands the width either supercolumns or the subcolumns so that the the sum of the supercolumn widths is the same as the sum of the subcolumn widths Usage equalBins widths subwidths Ar
262. function whose arguments are defined below This causes the entire analysis to be stratified by cross classifications of the combined list of stratification factors This stratification will be reflected as major column groupings in the resulting table or as more response columns for plotting If formula has no dependent variable method reverse is the only legal value and so method defaults to reverse in this case an object created by summary formula a numeric character category or factor vector for cumcategory Is converted to a categorical variable is needed logical If TRUE the result is coerced to the lowest possible dimension name or number of a data frame Default is the current frame a logical vector or integer vector of subscripts used to specify the subset of data to use in the analysis The default is to use all observations in the data frame function for handling missing data in the input data The default is a function defined here called na retain which keeps all observations for processing with missing variables or not function for summarizing data in each cell Default is to take the mean of each column of the possibly multivariate response variable You can specify fun to compute percentages 100 times the mean of a series of logical or binary variables User specified functions can also return a matrix For exam ple you might compute quartiles on a bivariate response The default is r
263. fy check 49 describe 60 format df 100 getHdata 110 html 125 latex 142 rcespline restate 207 sasxport get 227 spss get 252 stata get 254 summary formula 266 sys 281 units 308 INDEX Topic iplot labcurve 129 mgp axis 163 Topic iteration mApply 157 Topic list print char list 188 Topic loess wtd stats 319 Topic manip Snin 333 capitalize 29 csv get 40 data frame create modify check 49 dataRep 57 discrete 65 escapeRegex 77 first word 99 format df 100 html 125 inc dec 128 Lag 141 latex 142 makeNstr 156 mChoice 158 mdb get 162 nstr 177 partition 181 reorder factor 211 reShape 213 sasxport get 227 score binary 238 sedit 239 spss get 252 stata get 254 summarize 261 summary formula 266 trunc POSIXt 307 upData 309 varclus 314 wtd stats 319 Topic math find matches 95 impute 127 Topic methods aregImpute 9 dropUnusedLevels 68 339 Ecdf 69 format df 100 html 125 impute 127 latex 142 redun 209 transcan 291 Topic misc HmiscOverview 117 label 138 valuetags 313 Topic models abs error pred 2 areg 5 aregImpute 9 dataRep 57 describe 60 impute 127 na delete 173 na detail response 174 na keep 176 respline plot 205 redun 209 transcan 291 Topic multivariate areg 5 aregImpute 9 curveRep 42 find matches 95 pel 182 redun 209 rm boot 218 summarize 261 transace 283 transcan 291
264. g R sepUnitsTrans converts character vectors containing values suchas c 3 days 3day 4month 2 years 2weeks 7 to numeric vectors here c 3 3 122 730 14 7 ina flexible fashion The user can specify a vector of units of measurements and conversion factors The units with a conversion factor of 1 are taken as the target units and if those units are present in the char acter strings they are ignored The target units are added to the resulting vector as the units attribute trap rule computes the area under a curve using the trapezoidal rule assuming x is sorted trellis strip blank sets up Trellis or Lattice graphs to have a clear background on the strips for panel labels under unix is a scalar logical value that is TRUE if you are running Linux or Unix R is a logical value set to TRUE if running R FALSE for S Plus SV4 is a logical value set to TRUE if running version 4 of the S language under S Plus FALSI otherwise Gl unPaste provides a version of the S Plus unpaste that works for R and S Plus whichClosePW is a very fast function using weighted multinomial sampling to determine which element of a vector is closest to each element of another vector whichClosest quickly finds the closest element without any randomness xless is a function for Linux Unix users to invoke the system xless command to pop up a window to display the result of printing an object For Windows xless use
265. g the coordinates of the cdf If there is more than one group a list of such lists is returned An attribute N is in the returned object It contains the elements n and m the number of non missing and missing observations respectively Side Effects plots Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also wtd Ecdf label table cumsum labcurve xYplot histSpike Examples set seed 1 ch lt rnorm 1000 200 40 Ecdf ch xlab Serum Cholesterol scatld ch add rug plot histSpike ch add TRUE frac 15 add spike histogram Better add a data density display automatically Ecdf ch datadensity density label ch lt Serum Cholesterol Ecdf ch other ch lt rnorm 500 220 20 Ecdf other ch add TRUE lt y 2 Fl sex lt factor sample c female male 1000 TRU Ecdf ch gq c 25 5 75 show quartiles Ecdf ch group sex label curves list method arrow Example showing how to draw multiple ECDFs from paired data pre test lt rnorm 100 50 10 post test lt rnorm 100 55 10 x lt c pre test post test g lt c rep Pre length pre test rep Post length post test Ecdf x group g xlab Test Results label curves list keys 1 2 keys 1 2 causes symbols to be drawn periodically on top of curves Draw a matrix of ECDFs for a data frame m lt data
266. gImpute by default uses predictive mean matching with optional weighted probability sampling of donors rather than using only the closest match Predictive mean matching works for binary categorical and continuous variables without the need for iterative maximum likelihood fitting for binary and categorical variables and without the need for computing residuals or for curtailing imputed values to be in the range of actual data Predictive mean matching is especially attractive when the variable being imputed is also being transformed automatically See Details below for more information about the algorithm A regression method is also available that is similar to that used in transcan This option should be used when mechanistic missingness requires the use of extrapolation during imputation aregIimpute 13 A print method summarizes the results and a plot method plots distributions of imputed values Typically fit mult impute will be called after aregImpute If a target variable is transformed nonlinearly i e if nk is greater than zero and t linear is set to FALSE and the estimated target variable transformation is non monotonic imputed values are not unique When type regression arandom choice of possible inverse values is made Usage aregImpute formula data subset n impute 5 group NULL nk 3 tlinear TRUE type c pmm regression match c weighted closest fweighted 0 2 curtail TRUE boot method c s
267. ge effects of predictors in predicting the response variable after untransforming it As an example at each bootstrap repetition the estimated transformed value of one of the predictors is computed at the lower quartile median and upper quartile of the raw value of the predictor These transformed x values are then multipled by the least squares estimate of the partial regression coefficient for that transformed predictor in predicting transformed y Then these weighted transformed x values have the weighted transformed x value corresponding to the lower quartile subtracted from them to esti mate an x effect accounting for nonlinearity The last difference computed is then the standardized effect of raising x from its lowest to its highest quartile Before computing differences predicted values are back transformed to be on the original y scale in a way depending on stat istic and q The sample standard deviation of these effects differences is taken over the bootstrap samples and this is used to compute approximate confidence intervals for effects and approximate P values both assuming normality predict does not re insert NAs corresponding to observations that were dropped before the fit when newdata is omitted statistic fitted estimates the same quantity as statistic median if the residu als on the transformed response have a symmetric distribution The two provide identical estimates when the sample median of the residuals is exactl
268. ght TRUE Default is mark ahead A numeric argument which specifies the absolute distance to be placed between event history 95 the individual right censored survival bars and the mark as defined in the above cens mark argument Default is 0 5 that is a half of day if survival time is measured in days but may very well need adjusting depending on the maximum survival time observed in the dataset cens mark cutoff A negative number very close to 0 by default cens mark cutoff 1e 8 to ensure that the censoring marks get plotted correctly See event history code in order to see its usage This argument typically will not need adjustment cens mark cex Numeric argument defining the size of the mark defined in the cens mark argument above See more information by viewing the cex argument for the S Plus par function Default is cens mark cex 1 0 x lab Single label to be used for entire x axis Defaultis time under observation y lab Single label to be used for entire y axis Default is estimated survival probability title Title for the event history graph Defaultis event history graph This allows arguments to the plot function call within the event history function So for example the axes representations can be manipulated with appropriate arguments or particular areas of the event history graph can be zoomed See the details section for more comments about zooming Details In order to foc
269. gth of the colors vector should correspond to the value provided to the num colors argument or the number of ordered points 1 as defined in the cut cov ar gument with cut cov superceding num colors if is null cut cov FALSE The function as currently written allows for as much as twenty distinct colors This argument effectively feeds into the col argument for the S Plus polygon function Default is colors 1 See the col argument for the both the S Plus par function and polygon function for more information This will provide the shading density at the end of the individual bars for those who are censored For more information on shading density see the density argument in the S Plus polygon function Default is cens density 10 This is a multiplier that extends the length of the longest surviving individual bar or bars if a tie exists if right censored presuming that no event times eventually follow this final censored time Default extends the length 5 percent beyond the length of the observed right censored survival time mark right A logical argument that states whether an explicit mark should be placed to the right of the individual right censored survival bars This argument is most useful for large sample sizes where it may be hard to detect the special shading via cens density particularly for the short term survivors Character argument which describes the censored mark that should be used if cens mark ri
270. guments widths widths of the supercolumns subwidths list of widths of the subcolumns for each supercolumn Details This determins the correct subwidths of each of various columns in a table for printing The correct width of the multicolumns is deterimed by summing the widths of it subcolumns Value widths of the the columns for a table Author s Charles Dupont See Also nebar stringDimse Examples meols lt c Group 1 Group 2 mwidth lt nchar mcols type width spancols lt c 3 3 ccols lt c a deer ad cat help bob cwidth lt nchar ccols type width subwidths lt partition vector cwidth spancols equalBins mwidth subwidths 78 errbar errbar Plot Error Bars Description Add vertical error bars to an existing plot or makes a new plot with error bars Usage errbar x y yplus yminus cap 0 015 main NULL sub NULL xlab as character substitute x ylab if is factor x is character x else as character substitute y add FALSE lty 1 xlim NULL ylim NULL lwd 1 pch 16 Type rep 1 length y axes FALSE ann par ann panel first NULL panel last NULL asp NA Arguments x vector of numeric x axis values for vertical error bars or a factor or character variable for horizontal error bars x representing the group labels y vector of y axis values yplus vector of y axis values the tops of
271. hat returns a list with components P the computed P value st at the test statis tic either chi square or F df degrees of freedom testname test name statname statistic name an optional component latexstat LaTeX rep resentation of statname an optional component plotmathstat for R the plotmath representation of st atname as a character string and an op tional component note that contains a character string note about the test e g test not done because n lt 5 conTest is applied to continu ous variables on the right hand side of the formula when met hod reverse The default uses the spearman2 function to run the Wilcoxon or Kruskal Wallis test using the F distribution catTest a function of a frequency table an integer matrix that returns a list with the same components as created by conTest By default the Pearson chi square test is done without continuity correction the continuity correction would make the test conservative like the Fisher exact test ordTest a function of a frequency table an integer matrix that returns a list with the same components as created by conTest By default the Proportional odds likelihood ratio test is done for summary formula these are optional arguments for cut 2 when vari ables are automatically categorized For plot methods these arguments are passed to dotchart2 For Key and Key2 these arguments are passed to key text or mtitle For print methods these are opti
272. have installed S sty in a location that the system latex command automatically accesses Set type verbatim to instead use the LaTeX verbatim environment other arguments are accepted and ignored except that latex passes arguments to format df e g col just and other formatting options like dec rdec and cdec For latexVerbatim these arguments are passed to the print function Ignored for latexTranslate For latexTabular if these extra arguments are specified x is run through format df with those arguments specify additional input and translated strings over the usual defaults If pb TRUE latexTranslate also translates to math mode using left right set to TRUE to have latexTranslate put names for greek letters in math mode and add backslashes a vector of character strings specifying column headings for latexTabular defaulting to x s colnames a character strings specifying column alignments for latexTabular default ing to paste rep c ncol x collapse to center You may specify align c c and other LaTeX tabular formatting a character strings specifying alignment for column headings defaulting to cen tered set to FALSE to use default LaTeX font in LatexTabular instead of hel vetica horizontal space e g extra left margin for verbatim text Default is none Use e g hspace 10ex to add 10 extra spaces to the left of the text for S Plus only is the length of the output page
273. he dataset rather than fetching the data themselves Specify what description to browse a data description file if available Specify what al11 to retrieve the data and see the metadata and description where URL containing the data and metadata files Details For S Plus Hmisc defines a function download file that is used by getHdata This is a stripped down version of the R download file function that uses the system wget executable for fetching files from the Internet For Unix and Linux systems wget will be pre installed usu ally For windows S Plus systems get wget from ftp sunsite dk projects wget windows Once you unzip the file from there move wget exe to the same Windows directory that contains ftp exe Value getHdata without a file argument returns a character vector of dataset base names When a dataset is downloaded the data frame is placed in search position one and is not returned as value of getHdata Author s Frank Harrell See Also download file cleanup import data restore load getZip 115 Examples Not run getHdata download list of available datasets getHdata prostate downloads load or data restore runs cleanup import for S Plus 6 getHdata valung contents open browser options browser whatever after downloading valung html result of html contents getHdata support all download and open one browser window datadensity support attach support
274. he shrinkage factor is 1 G katai R2 Where R2 is the apparent R squared for predicting the variable n is the number of non missing values and k is the effective number of degrees of freedom aside from intercepts A heuristic estimate is used fork A 1 sum max 0 Bi 1 m m where A is the number of d f required to represent the variable being predicted the Bi are the number of columns required to represent all the other variables and m is the number of all other variables Division by m is done because the transformations for the other variables are fixed at their current transformations the last time they were being predicted The m term comes from the number of coefficients estimated on the right hand side whether by least squares or canonical variates If a shrinkage factor is negative it is set to 0 The shrinkage factor is the ratio of the adjusted R squared to the ordinary R squared The adjusted R squared is 1 R2 n 1 n k 1 304 transcan which is also set to zero if it is negative If shrink FALSE and the adjusted R squares are much smaller than the ordinary R squares you may want to run transcan with shrink TRUE Canonical variates are scaled to have variance of 1 0 by multiplying canonical coefficients from cancor by J n 1 When specifying a non Design library fitting function to fit mult impute e g 1m glm running the result of fit mult impute through that fit s summ
275. her numeric julian date event chart 83 or an S Plus dates object if n specifed variable is numeric e g systolic blood pressure level although not a julian date y jitter logical flag which takes effect only if the argument y var is utilized Due to potential ties in y var variable y jitter when TRUE will jitter the data to allow discrimination between observations at the possible cost of producing slightly inaccurate dates or covariate values if FALSE the default no jittering will be performed The y jitter algorithm assumes a uniform distribution of observations across the range of y var The algorithm is as follows size jitter lt diff range y var 2 length y var 1 y jitter factor The default of y jitter factor is 1 The entire product is then used as an argument into runif y var lt y var runif length y var size jitter size jitter y jitter factor an argument used with the y jitter function to scale the range of added noise Default is 1 y renum logical flag If TRUE subset observations are listed on y axis from 1 to length subset r if FALSE default subset observations are listed on y axis in original form As an example if subset r 301 340 and y renum TRUE y axis will be shown as 1 through 40 However if y renum FALSE y axis will be shown as 301 through 340 The above examples assume the following argu ment NA rm is set to FALSE
276. here will be very small differences due to interpolation of transformations meanr lt Mean f create function of lp computing mean response medr lt Quantile f default quantile is 5 nomogram f ols fun list Mean meanr Median medr Create S functions that will do the transformations This is a table look up with linear interpolation g lt Function f plot blood pressure g blood pressure blood pressure produces the central curve in the last plot done by plot f End Not run Another simulated example where y has a log normal distribution with mean x and variance 1 Untransformed y thus has median exp x and mean exp x 5sigma 2 exp x 5 First generate data from the model y exp x epsilon epsilon Gaussian 0 1 set seed 139 n lt 1000 x lt rnorm n y lt exp x rnorm n f lt areg boot y x B 20 plot f note log shape for y linear for x Good XS lt a 27 0 2 d lt data frame x xs predict f d fitted predict f d median almost same median residual 001 exp xs population medians predict f d mean exp xs 5 population means 294 transcan Show how smearingEst works res lt c 1 0 1 define residuals yes LES ytrans lt log y ys lt seq 1 15 length 50 trans approx lt list x log ys y ys options digits 4 smearingEst ytrans exp res fitted ignores re
277. his is best done without attaching the data frame Note that plot data frame plots inverted CDFs for continuous variables and dot plots showing frequency distributions of categorical ones summary FEV basic summary function summary data frame plot FEV plot data frame datadensity FEV rug plots and freq bar charts for all var hist data frame FEV for variables having gt 2 values by FEV FEVSsmoke summary use basic summary function with stratification Step 7 Do detailed analyses involving individual variables Analyses based on the formula language can use data so attaching the data frame may not be required This saves memory Here we use th Hmisc summary formula function to compute 5 statistics on height stratified separately by age quartile and by sex options width 80 summary height age sex data FEV fun function y c smean sd y smedian hilow y conf int 5 This computes mean height S D median outer quartiles fit lt lm height agexsex data FEV summary fit For this analysis we could also have attached the data frame in search position 2 For other analyses it is mandatory to attach the data frame unless FEVS prefixes each variable name Important DO NOT USE attach FEV 1 or Se SR SR SEE data frame create modify check attach FEV pos 1 if you are only analyzing and not changing the variables
278. history Dubin J A Muller H G and Wang J L 2001 Event history graphs for censored survival data Statistics in Medicine 20 2951 2964 See Also plot polygon event chart par Examples Code to produce event history graphs for SIM paper before generating plots some pre processing needs to be performed per subject and sort by time under observation in order to get dataset in proper form for event history function need to create one lin with thos xperiencing event coming before thos require survival data heart creation of event history version of heart dataset heart one lt matrix nrow length unique heart id for i in 1 length unique heart id if length heart id heart id i _ 1 tied with censoring time call heart one ncol 8 heart one i lt lse if length heartSid heartSid i 2 as numeric unlist heart heartSid i heart one i lt heart one 3 heart one 3 0 lt 2 if is factor heartStransplant heart one 7 lt heart one 7 1 getting back to correct transplantation coding heart one order unlist heart one 2 heart one lt as data fram names heart one lt names heart back to usual censoring indicator heart one 3 heart one 3 2 lt 0 note transplant says 0 for no transplants and event 1 is death while event
279. hood to be used in deriving the simultaneous confidence band Note that this approach does assume homoscedasticity cor pattern More generally than using an equal correlation structure you can specify a func tion of two time vectors that generates as many correlations as the length of these vectors For example cor pattern function timel time2 2 abs timel time2 10 would specify a dampening serial correlation pattern cor pattern can also be a list containing vectors x a vector of absolute time differences and y a corresponding vector of correlations To estimate the correlation function as a function of absolute time differences within subjects specify cor pattern estimate The products of all possible pairs of residuals or at least up to ncor of them within subjects will be related to the absolute time difference The correlation function is estimated by computing the sample mean of the products of stan dardized residuals stratified by absolute time difference The correlation for a zero time difference is set to 1 regardless of the Lowess estimate NOTE This approach fails in the presence of large subject effects correcting for such effects removes too much of the correlation structure in the residuals ncor the maximum number of pairs of time values used in estimating the correlation function if cor pattern estimate CI other arguments to pass to smoother if plot individual TRUI 224 obj2 cont int
280. hose reciprocal is multiplied to original units of the x axis For exam ple if the original data frame is in units of days x scale 365 will result in units of years notwithstanding leap years Default is 1 single label to be used for entire x axis Default will be On Study Date if x julian FALSE and Time on Study ifx julian TRUE column or data frame variable name for plotting unique lines by unique values of vector e g specify c arm to plot unique lines by treatment arm Can take at most one column or variable name Default is NA which produces identical lines for each patient vector of line types corresponding to ascending order of line by values If line by is specified the vector should be the length of the number of unique values of line by If line by is NA only line 1ty 1 will be used The default is 1 vector of line widths corresponding to ascending order of line by values If line by is specified the vector should be the length of the number of unique values of line by If line by is NA only line 1lwd 1 will be used The default is 1 vector of line colors corresponding to ascending order of line by values If line by is specified the vector should be the length of the number of unique values of line by If line by is NA only line col 1 will be used The default is 1 a 2xk matrix with k number of pairs of additional line segments to add For ex ample if it is of interest to
281. hout the whiskers with any number of user specified corners corresponding to dif ferent quantiles but it also draws box percentile plots similar to those drawn by Jeffrey Banfield s umsfjban bill oscs montana edu bpplot function To quote from Banfield box percentile plots supply more information about the univariate distributions At any height the width of the irregular box is proportional to the percentile of that height up to the 50th percentile and above the 50th percentile the width is proportional to 100 minus the percentile Thus the width at any given height is proportional to the percent of observations that are more extreme in that direction As in boxplots the median 25th and 75th percentiles are marked with line segments across the box panel bpplot isa generalization of bpplot and panel bwplot in that it works with trellis making the plots horizontal so that category labels are more visable it allows the user to specify the quantiles to connect and those for which to draw reference lines and it displays means by default using dots bpplt draws horizontal box percentile plot much like those drawn by panel bpplot but tak ing as the starting point a matrix containing quantiles summarizing the data bpp1t is primarily intended to be used internally by plot summary formula reverse but when used with no arguments has a general purpose to draw an annotated example box percentile plot with the default quantiles
282. ial proportion with discus sion Statistical Science 16 101 133 2001 Examples binconf 0 10 10 include x TRUE include n TRUE binconf 46 50 method all1 22 bi Var biVar Bivariate Summaries Computed Separately by a Series of Predictors Description biVar is a generic function that accepts a formula and usual data subset and na action parameters plus a list stat info that specifies a function of two variables to compute along with information about labeling results for printing and plotting The function is called separately with each right hand side variable and the same left hand variable The result is a matrix of bivariate statistics and the stat info list that drives printing and plotting The plot method draws a dot plot with x axis values by default sorted in order of one of the statistics computed by the function spearman2 computes the square of Spearman s rho rank correlation and a generalization of it in which x can relate non monotonically to y This is done by computing the Spearman multiple rho squared between rank x rank x 2 and y When x is categorical a different kind of Spearman correlation used in the Kruskal Wallis test is computed and spearmanz2 can do the Kruskal Wallis test This is done by computing the ordinary multiple R 2 between k 1 dummy variables and rank y where x has k categories x can also be a formula in which case each predictor is correlated separately with y u
283. ian priors is used as the prior distribution 2 this prior is used in forming the statistical test or credible interval 3 no prior is used for the distribution of delta for computing power but instead a fixed single delta is given as in traditional frequentist hypothesis tests and 4 the test statistic has a Gaussian likelihood with known variance and mean equal to the specified delta gbayesMixPowerNP is handy where you want to use an earlier study in testing for treatment effects in a new study but you want to mix with this prior a non informative prior The mixing probability mix can be thought of as the applicability of the previous study As with gbayes2 power here means the probability that the new study will yield 108 gbayes a left credible interval that is to the right of delta w gbayeslPowerNP is a special case of gbayesMixPowerNP when the prior is a single Gaussian Usage gbayes mean prior var prior ml m2 stat var stat nl n2 cut prior cut prob prior 0 025 S3 method for class gbayes plot x xlim ylim name stat z gbayes2 sd prior delta w 0 alpha 0 05 upper Inf prior aux gbayesMixPredNoData mix NA dO NA vO NA d1l NA v1 NA what c density cdf gbayesMixPost x NA v NA mix 1 dOQ NA vO NA d1 NA v1 NA what c density cdf gbayesMixPowerNP pcdf delta v delta w 0 mix interval nsim 0 alpha 0 05 gbayeslPowerNP d0 v0 delta v delta w 0
284. ibution for the unknown treatment effect This procedure is applicable to the situation where a prior distribution is not to be used in constructing the test statistic or confidence interval but is only used for specifying the distribution of delta the parameter of interest Even though gbayes2 assumes that the test statistic has a normal distribution with known variance which is strongly a function of the sample size in the two treatment groups the prior distribution function can be completely general Instead of using a step function for the prior distribution as Spiegelhalter and Freedman used in their appendix gbayes2 uses the built in integrate func tion for numerical integration gbayes2 also allows the variance of the test statistic to be general as long as it is evaluated by the user The conditional power given the parameter of interest delta is 1 pnorm delta w delta sd z where z is the normal critical value corre sponding to 1 alpha 2 gbayesMixPredNoData derives the predictive distribution of a statistic that is Gaussian given delta when no data have yet been observed and when the prior is a mixture of two Gaussians gbayesMixPost derives the posterior density or cdf of delta given the statistic x when the prior for delta is a mixture of two Gaussians and when x is Gaussian given delta gbayesMixPowerNP computes the power for a test for delta gt delta w for the case where 1 a Gaussian prior or mixture of two Gauss
285. ical window e g motif SE OE SE te He To produce simple calendar event chart with internal legend postscript examplel ps horizontal TRUE event chart cdcaids subset c c infedate diagdate dethdate censdate x lab observation dates y lab patients sorted by AIDS diagnosis date titl AIDS data calendar event chart 1 point pch c 1 2 15 0 point cex c 1 1 0 8 0 8 legend plot TRUE legend location i legend cex 1 0 legend point text c transfusion AIDS diagnosis death censored legend point at list c 7210 8100 c 35 27 legend bty o He To produce simple interval event chart with internal legend postscript example2 ps horizontal TRUE event chart cdcaids subset c c infedate diagdate dethdate censdate x lab time since transfusion in days y lab patients sorted by AIDS diagnosis date titl AIDS data interval event chart 1 point pch c 1 2 15 0 point cex c 1 1 0 8 0 8 legend plot TRUE legend location i legend cex 1 0 legend point text c transfusion AIDS diagnosis death censored x reference infedate x julian TRUE legend bty 0 legend point at list c 1400 1950 c 7 1 To produce simple interval event chart with internal legend but now with flexible diagdate symbol size based on viral load variable postscript example2a ps horizontal TRUE
286. ick marks to an existing plot All minor tick marks that will fit on the axes will be drawn Usage minor tick nx 2 ny 2 tick ratio 0 5 Arguments nx number of intervals in which to divide the area between major tick marks on the X axis Set to 1 to suppress minor tick marks ny same as nx but for the Y axis tick ratio ratio of lengths of minor tick marks to major tick marks The length of major tick marks is retrieved from par tck Side Effects plots Misc 171 Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also axis Examples plot runif 20 runif 20 minor tick Misc Miscellaneous Functions Description This documents miscellaneous small functions in Hmisc that may be of interest to users clowess runs lowess butif the iter argument exceeds zero sometimes wild values can result in which case lowess is re run with iter 0 confbar draws multi level confidence bars using small rectangles that may be of different colors get Latest Source fetches and sources the most recent source code for functions in packages in the Vanderbilty University CVS repository inverseFunction generates a function to find all inverses of a monotonic or nonmonotonic function that is tabulated at vectors x y typically 1000 points If the original function is mono tonic simple linear interpolation is used and the result is a vector otherwise linear int
287. ict dataRep ee T EJ a formula with no left hand side Continuous numeric variables in need of rounding should appear in the formula as e g roundN x 5 to have a tol erance of e g 2 5 in matching Factor or character variables as well as numeric ones not passed through roundN are matched on exactly a numeric vector or an object created by dataRep the object created by dataRep or predict dataRep na action standard modeling arguments Default na action is na delete i e ob servations in the original dataset having any variables missing are deleted up front rounding constant tolerance is actually tol 2 as values are rounded to the nearest tol a 2 vector specifying a lower and upper limit to curtail values of x before round ing set to TRUE to see all unique combinations and frequency count a data frame containing all the variables given to dataRep but not necessarily in the same order or having factor levels in the same order set to FALSE to suppress printing newdata and the count of matching obser vations plus the worst case marginal frequency 62 deff prpct set to FALSE to not print percentiles and percents unused Value dataRep returns a list of class dataRep containing the collapsed data frame and frequency counts along with marginal distribution information predict returns an object of class predict dataRep containing information determined by matching observations in newdata wi
288. idual data point symbols used for the groups By default the key is positioned at the upper right corner of the graph If Key locator 1 is specified the key will appear so that its upper left corner is at the coordinates of the mouse click Usage plsmo x y method c lowess supsmu raw xlab ylab add FALSE lty 1 nlev col par col lwd par lwd iter if length unique y gt 2 3 else 0 bass 0 f 2 3 trim fun group prefix xlim ylim label curves TRUE datadensity FALSE lines TRUE subset TRU grid FALSE To use panel function xyplot formula y x conditioningvars groups panel panel plsmo type b label curves TRUE lwd superpose lineSlwd lty superpose lineSlty pch superpose symbolSpch cex superpose symbolS cex font superpose symbolSfont col NULL ses Gl 188 Arguments x y method xlab ylab add lity col lwd iter bass trim fun group prefix xlim ylim label curves datadensity lines subset grid plsmo vector of x values NAs allowed vector of y values NAs allowed lowess the default supsmu or raw to not smooth at all x axis label iff add F Defaults of label x or argument name y axis label like xlab Set to T to call lines instead of plot Assumes axes already labeled line type default 1 2 3 corresponding to group color for each curve
289. imple approximate bayesian burnin 3 x FALSE pr TRUE plotTrans FALSE tolerance NULL S3 method for class aregImpute print x digits 3 ses S3 method for class aregImpute plot x nclass NULL type c ecdf hist datadensity c hist none rug density diagnostics FALSE maxn 10 Arguments formula data subset n impute group an S model formula You can specify restrictions for transformations of vari ables The function automatically determines which variables are categorical i e factor category or character vectors Binary variables are automat ically restricted to be linear Force linear transformations of continuous variables by enclosing variables by the identify function I It is recommended that factor or as factor do not appear in the formula but instead vari ables be converted to factors as needed and stored in the data frame That way imputations for factor variables done using impute transcan for example will be correct an object created by aregImpute For aregImpute set x to TRUE to save the data matrix containing the final number n impute imputations in the result This is needed if you want to later do out of sample imputation Cate gorical variables are coded as integers in this matrix These may be also be specified You may not specify na actionasna retain is always used number of multiple imputa
290. in books and reports with nice defaults minimal wasted space Internally uses psfig function by Antonio Possolo antonio atc boeing com setps works with Ghostscript to convert ps to pdf Set Trellis graphics to use blank conditioning panel strips line thickness 1 for dot plot reference lines setTrellis 3 optional arguments Show colors corresponding to col 0 1 99 Show all plotting characters specified by pch Just type show pch to draw the table on the current device Use LaTeX to compile and dvips and ghostview to display a postscript graphic containing psfrag strings Version of solve with argument tol passed to qr Somers rank correlation and c index for binary y Spearman rank correlation coefficient spearman x y Spearman 1 d f and 2 d f rank correlation test Spearman multiple d f p adjusted p Wilcoxon Kruskal Wallis test for multiple predictors Simulate power of 2 sample test for survival under complex conditions Also contains the Gompertz2 Weibull2 Lognorm 2 functions Enhanced importing of SPSS files using read spss function src name source name s with memory store an object permanently easy interface to assign function Shortest unique identifier match Terry Therneau therneau mayo edu More easily subset a data frame Substitute one var for another when observations NA Generate a data frame containing stratified summary statistics Useful for passing to trellis General table mak
291. info see spearman2 formula or chiSquare code data subset na action the usual options for models Default for na action is to retain all values NA or not so that NAs can be deleted in only a pairwise fashion exclude imputed set to FALSE to include imputed values created by impute in the calcula tions other arguments that are passed to the function used to compute the bivariate Statistics or to dotchart2 for plot na rm logical delete NA values x a numeric matrix with at least 5 rows and at least 2 columns if y is absent For spearmanz2 the first argument may be a vector of any type including character or factor The first argument may also be a formula in which case all predictors are correlated individually with the response variable x may be a formula for soearman2 in which case spearman2 formula is invoked Each predictor in the right hand side of the formula is separately correlated with the response variable For print or plot x is an object produced by biVar For spearman and spearman test x is a numeric vector as is y For chiSquare x is a formula a numeric vector for numeric variables specifies the order of the Spearman rho 2 to use The default is p 1 to compute the ordinary rho 2 Use p 2 to compute the quadratic rank generalization to allow non monotonicity p is ignored for cate gorical predictors minlev minimum relative frequency that a level of a categorical predictor should have before it is p
292. ing and plotting functions for summarizing data X Y Frequency plot with circles area prop to frequency HmiscOverview 125 sys Execute unix or dos depending on what s running tex Enclose a string with the correct syntax for using with the LaTeX psfrag package for postscript graphics transace ace packaged for easily automatically transforming all variables in a matrix transcan automatic transformation and imputation of NAs for a series of predictor variables trap rule Area under curve defined by arbitrary x and y vectors trellis strip blank t test cluster using trapezoidal rule To make the strip titles in trellis more visible you can make the backgrounds blank by saying trellis strip blank Use before opening the graphics device 2 sample t test for cluster randomized observations uncbind Form individual variables from a matrix upData Update a data frame change names labels remove vars etc units Set or fetch units attribute units of measurement for var varclus Graph hierarchical clustering of variables using squared Pearson or Spearman correlations or Hoeffding D as similarities Also includes the naclus function for examining similarities in patterns of missing values across variables xy group Compute mean x vs function of y by groups of x xYplot Like trellis xyplot but supports error bars and multiple response variables that are connected as separate lines win slide Setup win graph or win printer
293. ing the main summary variable By default the name of the X argument is used Set stat name to NULL to suppress this name replacement type Specify type matrix to store the summary variables if there are more than one in a matrix subset a logical vector or integer vector of subscripts used to specify the subset of data to use in the analysis The default is to use all observations in the data frame x a data frame for asNumericMat rix ora numeric matrix format rix2dataFrame at List containing attributes of original data frame that survive subsetting Defaults to object origAttributes in the global environment created by the last call to asNumericMatrix restoreAll set to FALSE to only restore attributes Label units and levels instead of all attributes 266 Value summarize For summarize a data frame containing the by variables and the statistical summaries the first of which is named the same as the X variable unless st at name is given If type matrix the summaries are stored in a single variable in the data frame and this variable is a matrix asNumericMatrix returns a numeric matrix and stores an object origAttributes in the global environment with original attributes of component variables plus an indicator for whether a variable was converted from character to factor to allow it to be made numeric matrix2dataFrame returns a data frame Author s Frank Harrell Department of Biostatistics Vanderbi
294. ing to man age many small external files When file latex keeps track of LaTeX styles that are called for by creating or modifying an object latexStyles in GlobalTemp in R or in frame 0 in S Plus latexStyles is a vector containing the base names of all the unique LaTeX styles called for so far in the current session See the end of the examples section for a way to use this object to good effect For dvips file is the name of an output postscript file append defaults to FALSE Set to TRUE to append output to an existing file label a text string representing a symbolic label for the table for referencing in the LaTeX label and ref commands label is only used if caption is given rowlabel If x has row dimnames rowlabel is a character string containing the column heading for the row dimnames The default is the name of the argument for x rowlabel just If x has row dimnames specifies the justification for printing them Possible values are 1 r c The heading rowlabe1 itself is left justified if rowlabel just 1 otherwise it is centered latex cgroup n cgroup rgroup n rgroup cgroupTexCmd rgroupTexCmd 149 a vector of character strings defining major column headings The default is to have none a vector containing the number of columns for which each element in cgroup is a heading For example specify cgroup c Major 1 Major 2 n cgroup c 3 3 if Major 1 istospancolum
295. iption This help file contains a template for importing data to create an S Plus data frame correcting some problems resulting from the import and making the data frame be stored more efficiently modifying the data frame including better annotating it and changing the names of some of its variables and checking and inspecting the data frame for reasonableness of the values of its variables and to describe patterns of missing data Various built in functions and functions in the Hmisc library are used At the end some methods for creating data frames from scratch within S Plus are presented The examples below attempt to clarify the separation of operations that are done on a data frame as a whole operations that are done on a small subset of its variables without attaching the whole data frame and operations that are done on many variables after attaching the data frame in search posi tion one It also tries to clarify that for analyzing several separate variables using S Plus commands that do not support a dat a argument it is helpful to attach the data frame in a search position later than position one It is often useful to create modify and process datasets in the following order Import external data into a data frame if the raw data do not contain column names provide these during the import if possible Make global changes to a data frame e g changing variable names Change attributes or values of variables within a da
296. irst 6 months then the probability increases linearly up to 15 at 5 years Assume there is a linearly increasing chance of dropout up to 3 at 5 years Assume that the treatment has no effect for the first 9 months then it has a constant effect hazard ratio of 75 First find the right Weibull distribution for compliant control patients Sc lt Weibull e 1 3 Ot 355 7 sc Inverse cumulative distribution for case where all subjects are followed at least a years and then between a and b years the density rises as time a d is a b a x u 1 d 1 rcens lt function n 1 5 1 runif n 5 To check this type hist rcens 10000 nclass 50 Put it all together f lt Quantile2 sc hratio function x ifelse x lt 75 1 75 dropin function x ifelse x lt 5 0 15 x 5 5 5 dropout function x 3 x 5 par mfrow c 2 2 par mfrow c 1 1 to make legends fit plot f all label curves list keys lines reontrol lt function n f n control rinterv lt function n f n intervention spss get 255 set seed 211 spower rcontrol rinterv rcens nc 350 ni 350 test logrank nsim 50 normally nsim 500 or more par mfrow c 1 1 Not run Do an unstratified logrank test library survival From SAS STAT PROC LIFETEST manual p 1801 days lt c 179 256 262 256 255 224 225 287 319 264 237 156 270 257 242 157 249
297. is a vector the id and colvar variables are used to restructure it into a matrix with NAs for elements that corresponded to combinations of id and colvar values that did not exist in the data When more than one vector is given multiple matrices are created This is useful for restructuring irregular serial data into regular matrices It is also useful for converting data produced by expand grid into a matrix see the last example The number of rows of the new matrices equals the number of unique values of id and the number of columns equals the number of unique values of colvar When the first argument is a vector and the id is a data frame even with only one variable reShape will produce a data frame and the unique groups are identified by combinations of the values of all variables in id Ifa data frame const ant is specified the variables in this data frame are assumed to be constant within combinations of id variables if not an arbitrary observation in constant will be selected for each group A row of constant corresponding to the target id combination is then carried along when creating the data frame result A different behavior of reShape is achieved when base and reps are specified In that case x must be a list or data frame and those data are assumed to contain one or more non repeating measurements e g baseline measurements and one or more repeated measurements represented by variables named by pasting together the characte
298. is limits used if plot individual TRUE It is also passed along for later use by plot rm boot Forplot rm boot ylim can be specified to override the value stored in the object stored by rm boot The default is the actual range of y in the input data times a sequence of times at which to evaluated fitted values and confidence limits Default is 100 equally spaced points in the observed range of t ime absorb subject effects If TRUE adjusts the response vector y before re sampling so that the subject specific effects in the initial model fit are all zero Then in re sampling subject effects are not used in the models This will downplay one of the sources of vari ation This option is used mainly for checking for consistency of results as the re sampling analyses are simpler when absort subject effects TRUE rho The log likelihood function that is used as the basis of simultaneous confidence bands assumes normality with independence within subject To check the ro bustness of this assumption if rho is not zero the log likelihood under mul tivariate normality within subject with constant correlation rho between any two time points is also computed If the two log likelihoods have the same ranks across re samples alllowing the correlation structure does not matter The agreement in ranks is quantified using the Spearman rank correlation coefficient The plot method allows the non zero intra subject correlation log likeli
299. ist labels TRUE plotmathTranslate x data frame labelled object reLabelled object label 143 Arguments x any object for plotmathTranslate is a character string self lgoical where to interact with the object or its components units set to TRUE to append the units attribute if present to the returned la bel The units are surrounded by brackets For labelPlotmath is a character string containing the units of measurement plot set to TRUE to return a label suitable for R s plotmath facility returns an expression instead of a character string if R is in effect If units is also TRUE and if both label and units attributes are present the units will appear after the label but in smaller type and will not be surrounded by brackets default if x does not have a label attribute and default a character string is specified the label will be taken as default grid Currently R s Lattice and grid functions do not support plotmath ex pressions for xlab and ylab arguments When using lattice functions in R set the argument grid to TRUE so that labelPlotmath can return an ordinary character string instead of an expression label a character string containing a variable s label plotmath set to TRUE to have labelMathplot return an expression for plotting using R s plotmath facility If R is not in effect an ordinary character string is returned value the l
300. it logical representation of a short integer value Rick Becker Match each case on one continuous variable Fast matrix vector handling intercept s and NAs mem types quick summary of memory used during session Version of axis that uses appropriate mgp from mgp axis labels and gets around bug in axis 2 that causes it to assume las 1 Used by survplot and plot in Design library and other functions in the future so that different spacing between tick marks and axis tick mark labels may be specified for x and y axes ps slide win slide gs slide set up nice defaults for mgp axis labels Otherwise use mgp axis labels default to set defaults Users can set values manually using mgp axis labels x y where x and y are 2nd value of par mgp to use Use mgp axis labels type w to HmiscOverview minor tick mtitle nomiss panel bpplot panel plsmo pel plotCorrPrecision plsmo popower prn p sunflowers ps slide pstamp putKey putKeyEmpty rcorr rcorr cens rcorrp cens rcspline eval respline plot rcspline restate recode reShape rm boot rMultinom samplesize bin sas get sasxport get Save scatld 123 D9 79 IN 9 retrieve values where w x y x and y xy to get 3 mgp values first 3 types or 2 mgp axis labels Add minor tick marks to an existing plot Add outer titles and subtitles to a multiple plot layout Return a matrix after excluding any row with an NA P
301. itive scale with weights l age gt 70 2 previous disease score binary age gt 70 previous disease fun sum Additive scale equal weights score binary age gt 70 previous disease fun sum points c 1 1 Same as saying points 1 Union of variables to create a new binary variable score binary age gt 70 previous disease fun any sedit Character String Editing and Miscellaneous Character Handling Functions Description This suite of functions was written to implement many of the features of the UNIX sed program entirely within S PLUS function sedit The substring location function returns the first and last position numbers that a sub string occupies in a larger string The subst ring2 lt function does the opposite of the builtin function subst ring Itis named subst ring2 because for S Plus 5 x there is a built in function subst ring but it does not handle multiple replacements in a single string replace substring wild edits character strings in the fashion of change xxxxANYTHINGyyyy to aaaaANYTHINGbbbb if the ANYTHING passes an optional user specified test function Here the yyyy string is searched for from right to left to handle balanc ing parentheses etc numeric string and all digits are two examples of test functions to check respectively if each of a vector of strings is a legal numeric or if it contains only the digits 0 9 For the case where old S or x or for replace substring wild with the s
302. izes the x random method can be fairly conservative If not all subjects have the same number of observations after deleting observations containing missing values and if boot strap type x fixed bootstrapped residual vectors may have a length m that is different from the number of original observations n If m gt n for a bootstrap repetition the first n elements of the randomly drawn residuals are used If m lt n the residual vector is appended with a random sample with replacement of length n m from itself A warning message is issued if this happens If the number of time points per subject varies the bootstrap results for x fixed can still be invalid as this method assumes that a vector over subjects of all residuals can be added to the original yhats and varying number of points will cause mis alignment For boot strap type x random in the presence of significant subject effects the analysis is approximate as the subjects used in any one bootstrap fit will not be the entire list of subjects The average over subjects used in the bootstrap sample intercept is used from that bootstrap sample as a predictor of average subject effects in the overall sample Once the bootstrap coefficient matrix is stored by rm boot plot rm boot can be run multiple times with different options e g different confidence levels See boot cov inthe Design library for a general approach to handling repeated measurement data for ordinary
303. l adj 0 par mfrow c 1 1 combine 37 Create a subplot within another plot see also subplot plot 1 10 1 10 tmp lt cnvrt coords c 1 4 6 9 c 6 9 1 4 par plt c tmpS dev x 1 2 tmpSdev y 1 2 new TRUE hist rnorm 100 par fig c tmp dev x 3 4 tmp devS y 3 4 new TRUE hist rnorm 100 par old par combine Element Merging Description Merges an object by the names of its elements Inserting elements in value into x that do not exists in x and replacing elements in x that exists in value with value elements if protect is false Usage combine x value protect FALSE combine x protect FALSE lt value Arguments x named list or vector value named list or vector protect logical should elements in x be kept instead of elements in value currently does nothing included if ever want to make generic Author s Charles Dupont See Also names 38 contents Examples x lt 1 5 names x lt LETTERS x y lt 6 10 names y lt LETTERS y 2 x c A 1 B 2 C 3 D 4 E 5 y c D 6 E 7 F 8 G 9 H 10 combine x y c A 1 B 2 C 3 D 6 E 7 F 8 G 9 H 10 combine x y protect TRUE Cc A 1 B 2 C 3 D 4 E 5 F 8 G 9 H 10 contents Metadata for a Data Frame Description contents isa generic method for which contents data frame is currently the only metho
304. la y age sex Frequencies of Responses 01 10 8 Frequencies of Missing Values Due to Each Variable y age sex 0 2 0 Statistics on Response by Missing Non Missing Status of Predictors age NA age NA sex NA Any NA No NA N 2 0 18 000 20 00 2 0 18 000 Mean 0 5 0 444 0 45 0 5 0 444 options na action na keep describe y agexsex Statistics on Response by Missing Non Missing Status of Predictors na keep N 2 0 18 000 20 00 options na fun response table describe y agexsex age NA age NA sex NA Any NA No NA age NA age NA sex NA Any NA No NA 2 0 18 000 Mean O65 0 444 0 45 0 5 0 444 179 bouilt in function table Statistics on Response by Missing Non Missing Status of Predictors 0 1 10 11 He 10 1 1 8 9 1 8 na keep Do nothing na action Description Does not delete rows containing NAs but does add details concerning the distribution of the re sponse variable if options na detail response TRUE This na action is primarily for use with describe formula Usage na keep mf Arguments mf a model frame Value the same model frame with the na action attribute Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also na omit na delete model frame default na detail response naresid naprint describe 180 nstr Examples options na action na keep na detail response T
305. length does not count the column for N ncaption set to FALSE to not have latex summary formula response put sam ple sizes in captions i a vector of integers or character strings containing variable names to subset on Note that each row subsetted on in an summary formula reverse object subsets on all the levels that make up the corresponding variable automatically j a vector of integers representing column numbers middle bold setto TRUE to have LaTeX use bold face for the middle quantile for method reverse outer size the font size for outer quantiles for reverse tables insert bottom set to FALSE to suppress inclusion of definitions placed at the bottom of LaTeX tables for method reverse dcolumn see latex na group set to TRUE to have missing stratification variables given their own category NA shortlabel set to FALSE to include stratification variable names and equal signs in labels for strata levels dotchart set to TRUE to output a dotchart in the latex table being generated Value summary formula returns a data frame or list depending on method plot summary formula reverse returns the number of pages of plots that were made Side Effects plot summary formula reverse creates a function Key and Key2 in frame 0 that will draw legends 278 summary formula Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Harrell F
306. les Dupont See Also bind Examples ll ray a lt list a matrix 1 25 ncol 5 b matrix 1 10 ncol 5 c 5 simplifyDims a smean sd 247 smean sd Compute Summary Statistics on a Vector Description A number of statistical summary functions is provided for use with summary formula and summarize as well as tapply and by themselves smean cl normal computes 3 sum mary variables the sample mean and lower and upper Gaussian confidence limits based on the t distribution smean sd computes the mean and standard deviation snean sd1 computes the mean plus or minus a constant times the standard deviation smean cl boot is a very fast im plementation of the basic nonparametric bootstrap for obtaining confidence limits for the population mean without assuming normality These functions all delete NAs automatically smedian hilow computes the sample median and a selected pair of outer quantiles having equal tail areas Usage smean cl normal x mult qt l conf int 2 n 1 conf int 95 na rm TRUI Gl lt smean sd x na rm TRUI smean sdl x mult 2 na rm TRUE Gl lt smean cl boot x conf int 95 B 1000 na rm TRUE reps FALS smedian hilow x conf int 95 na rm TRUE Arguments x for summary functions smean smedian hilow a numeric vector from which NAs will be removed automatically na rm defaults to TRUE unlike built in S Plus functions so that by default NAs are
307. linear models binary and ordinal models and survival models using the unconditional bootstrap boot cov does not handle bootstrapping residuals Value an object of class rm boot is returned by rm boot The principal object stored in the returned object is a matrix of regression coefficients for the original fit and all of the bootstrap repetitions object Coef along with vectors of the corresponding 2 log likelihoods are sums of squared errors The original fit object from 1m fit qr is stored in fit For this fit a cell means model is used for the id effects 226 rm boot plot rm boot returns a list containing the vector of times used for plotting along with the overall fitted values lower and upper simultaneous confidence limits and optionally the pointwise confi dence limits Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu References Feng Z McLerran D Grizzle J 1996 A comparison of statistical methods for clustered data analysis with Gaussian error Stat in Med 15 1793 1806 Tibshirani R Knight K 1997 Model search and inference by bootstrap bumping Technical Report Department of Statistics University of Toronto http www stat stanford edu tibs Presented at the Joint Statistical Meetings Chicago August 1996 Efron B Tibshirani R 1993 An Introduction to the Bootstrap New York Chapman and Hall Diggle PJ Verbyla AP 1
308. linical trial with exponential survival so we can compare power simulations of logrank Cox test with cpower Hazard ratio is constant and patients enter the study uniformly with follow up ranging from 1 to 3 years Drop in probability is constant at 1 and drop out probability is constant at 175 Two year survival of control patients in absence of drop in is 8 mortality 2 Note that hazard rate is log 8 2 Total sample size both groups combined is 1000 o mortality reduction by intervention if no dropin or dropout is 25 254 spower This corresponds to a hazard ratio of 0 7283 computed by cpower cpower 2 1000 2 25 accrual 2 tmin 1 noncomp c 10 noncomp i 17 5 ranfun lt Quantile2 function x exp log 8 2 x hratio function x 0 7283156 dropin function x 1 dropout function x 175 reontrol lt function n ranfun n what control rinterv lt function n ranfun n what int rceens lt function n runif n 1 3 set seed 11 So can reproduce results spower rcontrol rinterv rcens nc 500 ni 500 test logrank nsim 50 normally use nsim 500 or 1000 Simulate a 2 arm 5 year follow up study for which the control group s survival distribution is Weibull with l year survival of 95 and 3 year survival of 7 All subjects are followed at least one year and patients enter the study with linearly increasing probability after that Assume there is no chance of dropin for the f
309. ll Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu References Whitehead J 1993 Sample size calculations for ordered categorical data Stat in Med 12 2257 2271 Julious SA Campbell MJ 1996 Letter to the Editor Stat in Med 15 1065 1066 Shows accuracy of formula for binary response case See Also bpower cpower Examples her th For a study of back pain none mild moderate severe expected proportions averaged over 2 treatments each of the 4 categories p lt Cy 627 w4y63 popower p 1 2 1000 posamsize p 1 2 popower p 1 2 3148 OR 1 2 total n 1000 ar that will be in print char list prints a list of lists in a visually readable format Description print char list Takes a list that is composed of other lists and matrixs and prints it in a visually readable format Usage print char list x hsep c vsep c csep c print it TRI 192 print char list Arguments x list object to be printed tg place for extra arguments to reside hsep character used to separate horizontal fields vsep character used to separate veritcal feilds csep character used where horizontal and veritcal separators meet print it should the value be cated out or returned as a string rowname halign horizontal justification of row names rowname valign verical justification of row names colname halign horizo
310. ll vanderbilt edu Richard M Heiberger Department of Statistics Temple University Philadelphia PA rmh astro ocis temple edu Examples first word expr expression y x log w format df Format a Data Frame or Matrix for LaTeX or HTML Description format df does appropriate rounding and decimal alignment and outputs a character matrix containing the formatted data If x is a data frame then do each component separately If x is a matrix but not a data frame make it a data frame with individual components for the columns If a component x x is a matrix then do all columns the same Usage format df x digits dec NULL rdec NULL cdec NULL numeric dollar dcolumn na blank FALSE na dot FALS Gl blank dot FALSE col jJust NULL cdot FALSE dcolumn FALSE matrix sep scientific c 4 4 math row names FALSE math col names FALSE double slash FALSE Arguments x a matrix usually numeric or data frame digits causes all values in the table to be formatted to digits significant digits dec is usually preferred dec If dec is a scalar all elements of the matrix will be rounded to dec decimal places to the right of the decimal dec can also be a matrix whose elements correspond to x for customized rounding of each element A matrix dec must have number of columns equal to number of columns of input x A scalar dec is expanded to a vector cdec with n
311. lotted on the right breaks the breakpoints used Side Effects a plot is produced on the current graphics device Author s Pat Burns Salomon Smith Barney London pburns dorado sbi com See Also hist histogram Examples options digits 3 set seed 1 histbackback rnorm 20 rnorm 30 fool lt list x rnorm 40 y rnorm 40 histbackback fool age lt rnorm 1000 50 10 sex lt sample c female male 1000 TRUE 120 HmiscOverview histbackback split age sex agef lt age sex female agem lt age sex male histbackback list Female agef Male agem probability TRUE xlim c 06 06 HmiscOverview Overview of Hmisc Library Description The Hmisc library contains many functions useful for data analysis high level graphics utility op erations functions for computing sample size and power translating SAS datasets into S imputing missing values advanced table making variable clustering character string manipulation conver sion of S objects to LaTeX code recoding variables and bootstrap repeated measures analysis Most of these functions were written by F Harrell but a few were collected from statlib and from s news other authors are indicated below This collection of functions includes all of Harrell s submissions to statlib other than the functions in the Design and display libraries A few of the functions do not have Help documentation To make
312. lotting data with time to event outcomes Which arise in a variety of studies including randomized clinical trials and non randomized cohort studies This function can use as input a matrix or a data frame although greater utility and ease of use will be seen with a data frame Usage event chart data subset r 1 dim data 1 subset c 1 dim data 2 sort by NA sort ascending TRUE sort na last TRUE sort after subset TRUE y var NA y var type n y jitter FALSE y jitter factor 1 y renum FALSE NA rm FALSE x reference NA now max data subset c na rm TRUE now line FALSE now line lty 2 now line lwd 1 now line col 1 pty m date orig c 1 1 1960 titl Event Chart y idlabels NA y axis auto y axis custom at NA y axis custom labels NA y julian FALSE y lim extend c 0 0 y lab ifelse is na y idlabels as character y idlabels x axis all TRUE x axis auto xX axis custom at NA x axis custom labels NA x julian FALSE x lim extend c 0 0 x scale 1 x lab ifelse x julian Follow up Time Study Date line by NA line lty 1 line lwd 1 line col 1 line add NA line add lty NA line add lwd NA line add col NA point pch l length subset c point cex rep 0 6 length subset c point col rep 1 length subset c point cex m
313. ls from ols xYplot resid fit fitted fit method smean sdl smean sdl is defined with summary formula in Hmisc Plot y vs a timeDate variable x xYplot y numericScale x label Label for X country For this example could omit label and specify y numericScale x country xlab Label for X Here is an example of using xYplot with several options to change various Trellis parameters xYplot y x z groups v pch c 1 2 3 layout c 3 1 3 panels side by side ylab Y Label xlab X Label main list Main Title cex 1 5 par strip text list cex 1 2 strip function strip default style 1 scales list alternating FALSE Dotplot examples s lt summarize y llist month year smedian hilow conf int 5 yearDays 335 setTrellis blank conditioning panel backgrounds Dotplot month Cbind y Lower Upper year data s or Chind groups year data s Display a 5 number 5 quantile summary 2 intervals dot median Note that summarize produces a matrix for y and Cbhind y trusts the first column to be the point estimat here the median s lt summarize y llist month year quantile probs c 5 05 25 75 95 type matrix Dotplot month Cbind y year data s Use factor year to make actual years appear in conditioning title strips Plot proportions and their Wilson confidence limits set seed 3 d lt expand grid continent c
314. lso spower ciapower bpower Examples In this example 4 plots are drawn on one page one plot for each combination of noncompliance percentage Within a plot the o 5 year mortality in the control group is on the x axis and separate curves are drawn for several reductions in mortality with the intervention The accrual period is 1 5y with all patients followed at least 5y and some 6 5y par mfrow c 2 2 oma c 3 0 3 0 morts lt seq 10 25 length 50 red lt c 10 15 20 25 for noncomp in c 0 10 15 1 42 Cs if noncomp gt 0 nc i lt nc c lt noncomp else nc i lt 25 nc c lt 15 z lt paste Drop in nc c Non adherence nc i sep plot 0 0 xlim range morts ylim c 0 1 xlab 5 year Mortality in Control Patients ylab Power type n title z cat z n ity lt 0 for r in red lty lt lty 1 power lt morts i lt 0 for m in morts i lt i 1 EJ power i lt cpower 5 14000 m 100 r 1 5 5 nc c nc i pr FALS lines morts power lty lty if noncom p 0 legend 18 55 rev paste red reduction sep lty 4 1 bty n mtitle Power vs Non Adherence for Main Comparison 1ll alpha 05 2 tailed Total N 14000 cex 1 8 Point sample size requirement vs mortality reduction Root finder uniroot assumes needed sample size is between 1000 and 40000 Hesi
315. lt matrix runif 500 ncol 2 y lt matrix runif 2000 ncol 2 w lt find matches x y maxmatch 5 tol c 02 03 wSmatches 1 5 wSdistance 1 5 Find first x with 3 or more y matches num match lt apply wSmatches 1 function x sum x gt 0 j lt 1 length num match num match gt 2 1 x j y wSmatches j summary w find matches 101 For many applications would do something like this attach df1 x lt cbhind age sex Just do as matrix dfl if dfl has no factor objects attach df2 y lt cbind age sex mat lt find matches x y tol c 5 0 exact match on sex 5y on age Demonstrate matchCases xcase a C11 Sy Sy 2 xcontrol lt 1 6 idcase lt gt of MATS PBT teh DN PaGont rol lt ebay bY pet pd he Ye BE ycase lt GTI 337 99y L22 ycontrol lt c 11 22 33 44 55 66 matchCases xcase ycase idcase xcontrol ycontrol idcontrol tol 1 If y is a binary response variable the following code will produce a Mantel Haenszel summary odds ratio that utilizes the matching Standard variance formula will not work here because a control will match more than one case WARNING The M H procedur xemplified here is suspect because of the small strata and widely varying number of controls per case x lt 5 Gils 2 84 3739 67 gt Ty Lay Ly y lt O CO p20 2 0 LA O04 Gyo 1 Tz case lt c rep TRUE 8 rep FALSE 8 id lt 1 length x
316. lt University f harrell vanderbilt edu See Also label cut2 llist by Examples Not run s lt summarize ap gt 1 llist size cut2 sz g 4 bone stat name Proportion dotplot Proportion siz bone data s7 End Not run set seed 1 temperature lt rnorm 300 70 10 month lt sample 1 12 300 TRUE year lt sample 2000 2001 300 TRUE g lt mApply temperature month mean na rm TRUE mean function x c Mean mean x na rm TRUE Median median x na rm TRUE summarize temperature month g mApply temperature month g w lt summarize temperature month mean na rm TRUE if R library lattice xyplot temperature month data w plot mean tempera w lt summarize temperature llist year month quantile probs c 5 25 75 na rm TRUE ture by month type matrix xYplot Cbind temperature 1 temperature 1 month year data w mApply temperature llist year month quantile probs c 5 25 75 na rm TRUE Compute the median and outer quartiles The outer quartiles are displayed using error bars set seed 111 summarize 267 dfr lt expand grid month 1 12 year c 1997 1998 reps 1 100 attach dfr y lt abs month 6 5 2 runif length month year 1997 s lt summarize y llist month year smedian hilow conf int 5 s mApply y llist month year smedian hilow conf int 5 xYpl
317. ltas J lt F pi x lt c rnorm n0O 0 rnorm nl dy w lt rceorr cens x y C j lt w C Index se j lt w S D 2 low lt C 1 96xse hi lt C 1 96x se print cbind C low hi errbar deltas C Ct l1 96 se C 1 96 se xlab True Difference in Mean X ylab ROC Area and Approx 0 95 CI title paste nl nl nO n0 sep rcorrp cens 205 abline h 5 v 0 col gray true lt 1 pnorm 0 deltas sqrt 2 lines deltas true col blue par mfrow c 1 1 rcorrp cens Rank Correlation for Paired Predictors with a Possibly Censored Re sponse Description Computes U statistics to test for whether predictor X1 is more concordant than predictor X2 ex tending rcorr cens For method 1 estimates the fraction of pairs for which the x1 difference is more impressive than the x2 difference For method 2 estimates the fraction of pairs for which x1 is concordant with S but x2 is not For binary responses the function improveP rob provides several assessments of whether one set of predicted probabilities is better than another using the methods describe in Pencina et al 2007 This is a distinct improvement over comparing ROC areas sensitivity or specificity Usage Gl reorrp cens xl x2 S outx FALSE method 1 improveProb xl x2 y S3 method for class improveProb print x digits 3 conf int 95 Arguments x1 first predictor a probability
318. lue This assumes that the original variables are accessible i e they have been attached and that you want the im puted variables to have the same names are the original variables If n impute was specified to transcan you must tell impute which imputation to use predict computes predicted variables and imputed values from a matrix of new data This matrix should have the same column variables as the original matrix used with t ranscan and in the same order unless a formula was used with transcan Function is a generic function generator Function transcan creates S functions to trans form variables using transformations created by t ranscan These functions are useful for getting predicted values with predictors set to values on the original scale Varcov methods are defined here so that imputation corrected variance covariance matrices are readily extracted from fit mult impute objects and so that fit mult impute can eas ily compute traditional covariance matrices for individual completed datasets Specific Varcov methods are defined for 1m glm and multinon fits 296 The subscript function preserves attributes transcan The invert Tabulated function does either inverse linear interpolation or uses sampling to sample qualifying x values having y values near the desired values The latter is used to get inverse values having a reasonable distribution e g no floor or ceiling effects when the transformation has a flat or
319. m xample to get Cox Mantel relative risk and CL From Stokes ME Davis CS Koch GG Categorical Data Analysis Using the SAS System 2nd Edition Sectino 17 3 p 596 599 Input data in Table 17 5 d lt expand grid treatment c A P center 1 3 dShealed2w lt gt C 15 15 717 125 Ty 3 dShealed4w lt GCL 175 27 13717 27 dSnotHealed4w lt c 2 7 10 15 16 18 d Reformat to the way most people would collect raw data d1 lt d rep 1 6 d healed2w dlStime lt 2 disy lt 1 d2 lt d rep 1 6 dShealed4w d2Stime lt 4 d2sy lt 1 d3 lt d rep 1 6 dSnotHealed4w d3Stime lt 4 d3Sy lt 0 d lt rbind dl d2 d3 d healed2w lt d healed4w lt d notHealed4w lt NULL 170 minor tick d Finally duplicate appropriate observations to create 2 and 4 week risk sets Healed and not healed at 4w need to be in the 2 week risk set as not healed d2w lt subset d time 4 d2wStime lt 2 d2w y lt 0 d24 lt rbind d d2w with d24 table y treatment time center Matches Table 17 6 with d24 mhgr y treatment interaction center time sep Get cumulative likelihood ratios and their 0 95 confidence intervals based on the following two tables Disease Disease Test 39 3 20 5 Test 2 1 17 22 t5 treum e3920 c 3 5 c 21 22 c 17 15 minor tick Minor Tick Marks Description Adds minor t
320. mChoice as double x drop FALSE S3 method for class mChoice print x quote FALSE max levels NULL width getOption width S3 method for class mChoice as character x S3 method for class mChoice summary object ncombos 5 minlength NULL drop TRU Gl S3 method for class summary mChoice print x prlabel TRUE S3 method for class mChoice x GArop FALSE match mChoice x table nomatch NA incomparables FALS Gl inmChoice x values is mChoice x S3 method for class mChoice Summary na rm Arguments na rm Logical remove NA s from data mChoice table nomatch 163 a vector mChoice of values to be matched against value to return if a value for x does not exist in table incomparables sort label sort levels add none drop object ncombos width quote max levels minlength sep prilabel values Value logical whether incomparable values should be compaired a series of vectors Gl By default choice codes are sorted in ascending numeric order Set sort FALS to preserve the original left to right ordering from the input variables a character string labe1 attribute to attach to the matrix created by mChoice set sort levels alphabetic to sort the columns of the matrix created by mChoice alphabetically by category rather than b
321. mand load Permdata so that the next time you invoke R the permanent objects will be available 260 string bounding box Side Effects uses assign and attach functions store with no arguments also stores a function Last in Data tempnnnn which will cause Data tempnnnn to be removed when the S session ends For S Plus store causes creation of a system option named store temp which contains the name of the temporary directory created See Also assign Last attach search Examples Not run attach database 1 his database takes precedenc store new objects to go under database in memory this doesn t work in R ct E lt lm y x store f store f under name f in Data or GlobalEnv uses assign with immediate T store f final model immediately store f under final model in Data store store future objects in Data tempnnnn x x lt runif 1000 will disappear at session end unless store x his statement appears gt store in Data stores x y Z store x y z in Data under names x y zZ storeTemp x put x as name x in frame 0 for R attach GlobalTemp and store it there storeTemp x X same as previous but under the name X End Not run string bounding box Determine Diamentions of Strings Description This determins the number of rows and maximum number of columns of each string in a vector Usage string bounding box string
322. mary method prints a frequency distribution of the number of matches per observation in x the median of the minimum distances for all matches per x as a function of the number of matches and the frequency of selection of duplicate observations as those having the smallest distance The print method prints the entire mat ches and distance components of the result from find matches matchCases finds all controls that match cases on a single variable x within a tolerance of tol This is intended for prospective cohort studies that use matching for confounder adjustment even though regression models usually work better Usage find matches x y tol rep 0 ncol y scale tol maxmatch 10 find matches 99 S3 method for class find matches summary object S3 method for class find matches print x digits matchCases xcase ycase idcase names ycase xcontrol ycontrol idcontrol names ycontrol tol NULL maxobs max length ycase length ycontrol 10 maxmatch 20 which c closest random Arguments X a numeric matrix or the result of find matches y a numeric matrix with same number of columns as x xcase xcontrol vectors not necessarily of the same length specifying a numeric variable used to match cases and control ycase ycontrol vectors or matrices not necessarily having the same number of rows specifying a variable to carry along from cases and matching controls If you instead want to
323. mate that was used see label This label can be retrieved for other purposes by using e g label obj where obj was the vector returned by smearingEst 292 transace Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu References Harrell FE Lee KL Mark DB 1996 Stat in Med 15 361 387 Duan N 1983 Smearing estimate A nonparametric retransformation method JASA 78 605 610 Wang N Ruppert D 1995 Nonparametric estimation of the transformation in the transform both sides regression model JASA 90 522 534 See avas ace for primary references See Also avas ace ols validate predab resample label nomogram Examples xtrans lt transace cbind age sex blood pressure race code binary sex monotonic age categorical race code Generate random data from the model y exp xl epsilon 3 where x1 and epsilon are Gaussian 0 1 set seed 171 to be able to reproduc xampl xl lt rnorm 200 x2 lt runif 200 a variable that is really unrelated to y x3 lt factor sample c cat dog cow 200 TRUE also unrelated to y y lt exp xl rnorm 200 3 f lt areg boot y x1 x2 x3 B 40 f plot f Note that the fitted transformation of y is very nearly log y the appropriate one the transformation of xl is nearly linear and the transformations of x2 and x3 are essentially
324. me histograms subplot hist rnorm 100 15 55 subplot hist runif 100 main xlab ylab 11 51 hadj 0 vadj 0 subplot hist rexp 100 1 3 20 60 hadj 1 vadj 1 size c 0 5 2 subplot hist rt 100 3 c 12 16 c 57 59 pars list lwd 3 ask FALSE tmp lt rnorm 25 qqnorm tmp qqline tmp tmp2 lt subplot hist tmp xlab ylab main cnvrt coords 0 1 0 9 plt Susr vadj 1 hadj 0 abline v 0 col red wrong way to add a reference line to histogram right way to add a reference line to histogram op lt par no readonly TRUE par tmp2 abline v 0 col green par op summarize Summarize Scalars or Matrices by Cross Classification Description summarize isa fast version of summary formula formula method cross overall FALS for producing stratified summary statistics and storing them in a data frame for plotting especially with trellis xyplot and dotplot and Hmisc xYplot Unlike aggregate summarize ac cepts a matrix as its first argument and a multi valued FUN argument and summarize also labels the variables in the new data frame using their original names Unlike methods based on tapply Fl summarize 265 summarize stores the values of the stratification variables using their original types e g a nu meric by variable will remain a numeric variable in the collapsed data frame summarize also retains label attributes for variables
325. mma which will yield those estimates It creates an S Plus function to evaluate survival probabilities from this Weibull distribution Weilbu112 is useful in creating functions to pass as the first argument to Quantile2 The Lognorm2 and Gompertz2 functions are similar to Weibul12 except that they produce survival functions for the log normal and Gompertz distributions Usage spower rcontrol rinterv rcens nc ni test logrank nsim 500 alpha 0 05 pr TRUE Quantile2 scontrol hratio dropin function times 0 dropout function times 0 m 7500 tmax qtmax 001 mplot 200 pr TRUE S3 method for class Quantile2 print x S3 method for class Quantile2 plot x what c survival hazard both drop hratio all dropsep FALSE lty 1 4 col 1 xlim ylim NULL label curves NULL logrank S group Gompertz2 times surv Lognorm2 times surv Weibull2 times surv Arguments reontrol rinterv rcens nc ni scontrol hratio a function of n which returns n random uncensored failure times for the con trol group spower assumes that non compliance dropin has been taken into account by this function similar to rcont rol but for the intervention group a function of n which returns n random censoring times It is assumed that both treatment groups have the same censoring distribution number of subjects in the control group number in the int
326. mporary Key and sKey functions in the session frame Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu Madeline Bauer Department of Infectious Diseases 332 University of Southern California School of Medicine mbauer usc edu See Also xYplot xyplot panel xyplot summarize label labcurve errbar dotplot reShape setps cut2 panel abline Examples Plot 6 smooth functions Superpose 3 panel 2 Label curves with p 1 2 3 where most separated d lt expand grid x seq 0 2 pi length 150 p 1 3 shift c 0 pi xYplot sin x shift p x shift groups p data d type 1 Use a key instead use 3 line widths instead of 3 colors Put key in most empty portion of each panel xYplot sin x shift p x shift groups p data d type 1 keys lines lwd 1 3 col 1 Instead of implicitly using labcurve put a single key outside of panels for S Plus or at lower left corner for R xYplot sin x shift p x shift groups p data d type 1 label curves FALSE lwd 1 3 col 1 lty 1 3 Key Bubble plots x lt y lt 1 8 x 2 lt NA units x lt em 2 z lt 101 108 p lt factor rep c a b 4 g lt c rep 1 7 2 data frame p X Y Z 9 xYplot y x p groups g size z Key other list title g cex title 1 2 draw key for colors sKey 2 85 other list title Z Values cex title 1 2 draw key for
327. ncy counts are rep resented as the heights of thermometers by default you can also specify symbol circle to the function There is an option to include marginal frequencies which are plotted on a halved scale so as to not overwhelm the plot If you do not ask for marginal frequencies to be plotted using marginals T symbol freq will ask you to click the mouse where a reference symbol is to be drawn to assist in reading the scale of the frequencies label attributes if present are used for x and y axis labels Otherwise names of calling argu ments are used Usage symbol freq x y symbol c thermometer circle marginals FALSE orig scale FALSE inches 0 25 width 0 15 subset srtx 0 Arguments x first variable to cross classify y second variable symbol specify thermometer the default or circle marginals set to TRUE to add marginal frequencies scaled by half to the plot orig scale set to TRUE when the first two arguments are numeric variables this uses their original values for x and y coordinates inches see symbols 284 sys width see thermometers option in symbols subset the usual subsetting vector srtx rotation angle for x axis labels other arguments to pass to symbols Author s Frank Harrell See Also symbols Examples Not run getHdata titanic attach titanic age tertile lt cut2 titanicSage g 3 symbol freq age tertile pclass marginals T srtx
328. nd if given must be a data frame a model frame is gen erated automatically from the previous formula The na action is handled automatically and the levels for factor variables must be the same and in the same order as were used in the original variables specified in the formula given to transcan 302 fit reps dtrans derived type object prefix suffix where transcan set to TRUE to save all fit objects from the fit for each imputation in fit mult impute Then the object returned will have a component fits which is a list whose ith element is the ith fit object provides an approach to creating derived variables from a single filled in dataset The function specified as dt rans can even reshape the imputed dataset An ex ample of such usage is fitting time dependent covariates in a Cox model that are created by start stop intervals Imputations may be done on a one record per subject data frame that is converted by dt rans to multiple records per sub ject The imputation can enforce consistency of certain variables across records so that for example a missing value of sex will not be imputed as male for one of the subject s records and female as another An example of how dtrans might be specified is dtrans function w wSage lt wSyears wSmonths 12 w where months might have been imputed but years was never missing an expression containing S expressions for computing derived variables tha
329. need to be in a directory in your TEXINPUTS path When you use dcolumn TRUE numeric dollar should be set to FALSE logical default dcolumn Set to TRUE to place dollar signs around numeric values when dcolumn FALSE This assures that latex will use minus signs rather than hyphens to indicate negative numbers Set to FALSE when dcolumn TRUE as dcolumn sty automatically uses minus signs math row names logical set true to place dollar signs around the row names math col names na dot blank dot col just logical set true to place dollar signs around the column names Set to TRUE to use periods rather than NA for missing numeric values This works with the SAS convention that periods indicate missing values Set to TRUE to use periods rather than blanks for missing character values This works with the SAS convention that periods indicate missing values Input vector col just must have number of columns equal to number of columns of the output matrix When NULL the default the col just at tribute of the result is set to 1 for character columns and to r for numeric columns The user can override the default by an argument vector whose length is equal to the number of columns of the result matrix When format df is called by latex default the col just is used as the cols argument to the format df matrix sep scientific double slash Value 105 tabular ae
330. ng in PC graphics packages mgp axis labels c 4 1 2 override 2nd mgp parameters for x and y axes mgp axis labels type x retrieve 3 mgp parameters for x axis 200 pstamp setps myfile equiv to setps myfile type char setps myfile trellis TRUE other args for Trellis plotting commands dev off topdf topdf created by setps makes Ghostscript create myfile pdf setpdf myfile plotting commands dev off Put math and Greek symbols in a graph setps test x lt seq 0 15 length 100 plot x dchisq x 5 xlab tex SxS ylab tex Sf x type 1 title tex Density Function of the chi_ 5 2 S Distribution dev off To process this file in LaTeX do something like documentclass article usepackage scanall psfrag begin document begin figure includegraphics test ps caption This is an example end figure end document End Not run pstamp Date Time Directory Stamp the Current Plot Description Date time stamp the current plot in the extreme lower right corner Optionally add the current working directory and arbitrary other text to the stamp Usage pstamp txt pwd FALSE time TRUE Arguments txt an optional single text string pwd set to TRUE to add the current working directory name to the stamp time set to FALSE to use the date without the time TCOIT 201 Details Certain functions a
331. nnet library of the MASS package which is assumed to have been installed by the user to fit a polytomous logistic model to the current working transformations of all the other variables using conditional mean imputation for missing predictors Multiple imputations are made by drawing multinomial values from the vector of predicted probabilities of category membership for the missing categorical values mincut If imputed TRUE there are categorical variables andimpcat tree mincut specifies the lowest node size that will be allowed to be split by tree The default is 40 inverse By default imputed values are back solved on the original scale using inverse linear interpolation on the fitted tabulated transformed values This will cause distorted distributions of imputed values e g floor and ceiling effects when the estimated transformation has a flat or nearly flat section To instead use the invert Tabulated function see above with the sample option specify inverse sample tolInverse the multiplyer of the range of transformed values weighted by f req and by the distance measure for determining the set of x values having y values within a tolerance of the value of aty in invertTabulated For predict transcan inverse and tolInverse are obtained from options that were specified to transcan by default Otherwise if not specified by the user these default to the defaults used to invert Tabulated pr For t ranscan
332. none 176 na delete Ee subtitle for lower right of figure default is today s date in format 23Jan91 for UNIX or R Thu May 30 09 08 13 1996 format for Windows Set to to suppress lower right title cex m character size for main default is 1 75 cex l character size for subtitles other arguments passed to mtext Value nothing Side Effects plots Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also par mtext title unix pstamp Examples Set up for 1 plot on figure give a main title use date for lr plot runif 20 runif 20 mtitle Main Title Set up for 2 x 2 matrix of plots with a lower left subtitle and overall title par mfrow c 2 2 oma c 3 0 3 0 plot runif 20 runif 20 plot rnorm 20 rnorm 20 plot exp rnorm 20 exp rnorm 20 mtitle Main Title 11 n 20 na delete Row wise Deletion na action Description Does row wise deletion as na omit but adds frequency of missing values for each predictor to the na action attribute of the returned model frame Optionally stores further details if options na detail response TRUBE na detail response 177 Usage na delete frame Arguments frame a model frame Value a model frame with rows deleted and the na action attribute added Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vande
333. ns 1 3 and Major 2 is to span columns 4 6 rowlabe1 does not count in the column numbers You can omit n cgroup if all groups have the same number of columns a vector of character strings containing headings for row groups n rgroup must be present when rgroup is given The first n rgroup 1 rows are sectioned off and rgroup 1 is used as a bold heading for them The usual row dimnames which must be present if rgroup is are indented The next n rgroup 2 rows are treated likewise etc integer vector giving the number of rows in each grouping If rgroup is not specified n rgroup is just used to divide off blocks of rows by horizontal lines If rgroup is given but n rgroup is omitted n rgroup will default so that each row group contains the same number of rows A character string specifying a LaTeX command to be used to format column group labels The default bfseries sets the current font to bold It is pos sible to supply a vector of strings so that each column group label is formatted differently Please note that the first item of the vector is used to format the title even if a title is not used Currently the user needs to handle these issue Mul tiple effects can be achieved by creating custom LaTeX commands for example providecommand redscshape color red scshape creates a LaTeX command called redscshape that formats the text in red small caps A character string specifying a LaTeX command to be used to format
334. nt code 2 92 event history Arguments data2 a matrix or dataframe with at least 2 columns by default the first column con tains the event time and the second column contains the k event codes e g l dead O censord event time the column number in data contains the event time event code the column number in data contains the event code Details In the survival analysis the data typically come in two columns one column containing survival time and the other containing censoring indicator or event code The event convert function converts this type of data into multiple columns of event times one column of each event type suitable for the event chart function Author s J Jack Lee and Kenneth R Hess Department of Biostatistics University of Texas M D Anderson Cancer Center Houston TX 77030 jjlee mdanderson org khess mdanderson org Joel A Dubin Department of Statistics University of Waterloo jdubin uwaterloo ca See Also event history Date code Date Examples To convert coded time to event data then draw an event chart s ry time lt 5 6 3 1 2 cens ind lt 1 0 1 1 0 surv data lt cbind surv time cens ind event data lt event convert surv data event chart cbind rep 0 5 event data x julian TRUE x reference 1 event history Produces event history graph for survival data event history Description 93 Produces an event hist
335. ntaining the following names elements mean prior var prior mean pc var post andif n1 is specified mean pred and var pred Note that mean pred is identi cal to mean post gbayes2 returns a single number which is the probability of correctly reject ing the null hypothesis in favor of the new treatment gbayesMixPredNoData returns a function that can be used to evaluate the predictive density or cumulative distribution gbayesMixPost returns a function that can be used to evaluate the posterior density or cdf gobayesMixPowerNP returns a vector containing two values if nsim 0 The first value is the critical value for the test statistic that will make the left credible interval gt delta w and the second value is the power If nsim gt 0 it returns the power estimate and confidence limits for it if nsim gt 0 The examples show how to use these functions Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu References Spiegelhalter DJ Freedman LS Parmar MKB 1994 Bayesian approaches to randomized trials JRSS A 157 357 416 Results for gbayes are derived from Equations 1 2 3 and 6 Spiegelhalter DJ Freedman LS 1986 A predictive approach to selecting the size of a clinical trial based on subjective clinical opinion Stat in Med 5 1 13 Joseph Lawrence and Belisle Patrick 1997 Bayesian sample size determination for normal means and diffe
336. ntal justification of column names colname valign verical justification of column names text halign horizontal justification of cell text text valign vertical justification of cell text rowname width minimumwidth of row name strings rowname height minimum height of row name strings min colwidth minimum column width max rowheight maximum row height abbreviate dimnames should the row and column names be abbreviated page width width of the page being printed on colname width minimum width of the column names colname height minimum height of the column names prefix width maximum width of the rowname columns superprefix width maximum width of the super rowname columns Value String that formated table of the list object Author s Charles Dupont print char matrix 193 print char matrix Function to print a matrix with stacked cells Description Prints a dataframe or matrix in stacked cells Line break charcters in a matrix element will result in a line break in that cell but tab characters are not supported Usage print char matrix x file col name align cen col txt align right cell align cen hsep vsep csep row names TRUE col names FALSE append FALSE top border TRUE left border TRUE Arguments x a matrix or dataframe file name of file if file output is desired If left empty output will be to
337. nusedLevels 2 2 eee 71 Eddi 5 tice ems a BAe eae eS SL eee Sa ee amp oo ee ae 72 EIP son p a gt ae he esd is aS RY ae Sh Re Sy A He eS ae esd Bh AS Ba he a BB hae ae 76 equalBiMS 2 4 04 4 2 pee Sila a E eae OEMS ES RSE EES 76 CIMDAL arenans eos Ae eae SA ee oe BE ee a A ee ee v7 ESCAPCREPOK s os ek aoe eR AS Rae ap a eG a de Bl aks aes 79 Event CHATE eais e eon a e era 4 oA RAS EEE eee eS eS e a ees 80 EVENECONVER soc s 44S GR ee Da e ER eS ae EE a a ee e e a 91 event HIStory s ege 485 hk PaaS waned we Se ee Rae EER RS eee RS 92 fi ndmatch s pii eke ek eR SS as e a E a Ae ad a 98 fitst Wotd ssw PAS e e Sowa Deb wee ee See ba eR EA e 102 format df ga posse pad aea ay p bees be ee Gdn eee eee be ew He 103 formatipval 2 466455 6854 ho be oS ERKE ROSS EEE ARES eG Sw 105 BDAYVES 4 oie ood Tg aa eR oe lk ee Mons Sh he ge Ae a Oe a ace ee ech S 106 pethdata ee we RA Ow EE DRE eA be eR eae oe ae ee 113 SCIAIP ns one ee OA OR RA eR eS ER ew ee Eee ee 115 hdquanwle sone eR ROS ee ee ep e a A Be SG a ag a 116 hist dataframe y 3 ca he awe WR eee ee ae hea eee 117 histback back se ponu Sea ano He AE eee ee Bee the he eG ES em 118 R topics documented 3 PIDSCOVERVIEW fos SA ae e ae Be a a ale we ale 120 NOCH cca don ey 2 eve Sa ba Fao doy bbe ee PE Eee Po eee A 126 hth se Sab eR eee Piha eee hee ee Gs be ee eee 128 IMPUS e 4 oa e e RA a Bw Se E e ek a eee a kG weet 129 INC deCG Ae eo eet bh hee ee ee Vee eb oe eR
338. nvoke store with arguments so that the object is copied to permanent storage Another function stores is useful for storing a series of temporary objects in Data with one call store and stores are not available For R See Details below for a method of approximating the use of store in R storeTemp stores an object in frame 0 for S Plus or in a temporary environment GlobalTemp in R attaching that environment if it is not already attached so that the objects are easily available Usage store object name as character substitute object where if under unix SV4 Data else _Data stores storeTemp object name deparse substitute object Arguments object object to store omit to set search list position one to a temporary directory created by store name name under which to store the object Default is name of object in call to store where directory in which to store object Default is Data underneath current direc tory for UNIX or position 2 in the search list for Windows For R the default is GlobalEnv a list of objects to store in Data or GlobalEnv permanently using names which are the same as the argument names Details To almost mimic the functionality of store or stores in R you can do the following Use save x y z file Permdata to save permanent objects in permdata When you exit R do not save the workspace Then all temporary objects will disappear In your Rprofile put the com
339. o represent the maximum frequency No jittering is done by hist Spike histSpike can also graph a kernel density estimate for x or add a small density curve to any of 4 sides of an existing plot When y or curve is specified the density or spikes are drawn with respect to the curve rather than the x axis Usage scatld x side 3 frac 0 02 jitfrac 0 008 tfrac ps ifelse preserve 0 001 lwd 0 1 col par col y NULL curve NULL bottom align FALSE preserve FALSE fill 1 3 limit TRUE nhistSpike 2000 nint 100 type c proportion count density grid FALSE jitter2 x Default S3 method jitter2 x fill 1 3 limit TRUE eps 0 presorted FALSE 236 scatld S3 method for class data frame FLEECE Xp nena datadensity object S3 method for class data frame datadensity object group which c all continuous categorical method cat c bar freq col group 1 10 n unique 10 show na TRUE nint 1 naxes q bottom align nint gt l1 cex axis sc 5 3 cex var sc 8 3 imgp NULL tck sc 009 002 ranges NULL labels NULL sc a b means default to a if number of axes lt 3 b if gt 50 use linear interpolation within 3 50 histSpike x side 1 nint 100 frac 05 minf NULL mult width 1 type c proportion count density xlim range x ylim c 0 max f xlab deparse substitute x ylab s
340. objects prefaces the print with the object s label and units if there If the variable is also given a units attribute using the units function subsetting the variable using Labelled will also retain the units attribute label can optionally append a units attribute to the string and it can optionally return a string or expression for R s plotmath facility suitable for plotting labelPlotmath is a function that also has this function when the input arguments are the label and units rather than a vector having those attributes When plot math mode is used to construct labels the label or units may contain math expressions but they are typed verbatim if they contain percent signs blanks or underscores Label actually Label data frame is a function which generates S Plus source code that makes the labels in all the variables in a data frame easy to edit llist is like list except that it preserves the names or labels of the component variables in the variables Label attribute This can be useful when looping over variables or using sapply or lapply By using 11ist instead of 1ist one can annotate the output with the current variable s name or label 11ist also defines a names attribute for the list and pulls the names from the arguments expressions for non named arguments 142 label plotmathTranslate is a simple function that translates certain character strings to character strings that can
341. oceedings of the Biometrics Section American Statistical Association Dubin J A Muller H G Wang J L 2001 Event history graphs for censored survival data Statis tics in Medicine 20 2951 2964 Goldman A I 1992 EVENTCHARTS Visualizing Survival and Other Timed Events Data The American Statistician 46 1 13 18 See Also event history Date Examples The sample data set is an augmented CDC AIDS dataset ASCII which is used in the examples in the help file This dataset is described in Kalbfleisch and Lawless JASA 1989 Here we have included only children 4 years old and younger We have also added a new field dethdate which represents a fictitious death date for each patient There was no recording of death date on the original dataset In addition we have added a fictitious viral load reading copies ml for each patient at time of AIDS diagnos noting viral load was also not part of the original dataset All dates are julian with julian 0 being January 1 1960 and julian 14000 being 14000 days beyond January 1 1960 i e May 1 1998 cdcaids lt data frame age c 4 2 1 1 2 2 2 4 2 1 1 3 2 1 3 271 2 4 2 2 1 4 2 4 1 4 2 1 1 3 3 1 3 infedate c 7274 7727 7949 8037 7765 8096 8186 7520 8522 8609 8524 8213 8455 8739 8034 8646 8886 8549 8068 8682 8612 9007 8461 8888 8096 9192 9107 9001 9344 9155 8800 8519 9282 8673 diagdate c 8100 8158 8251 8343 84
342. oef 1 x rm BP could also have used coef instead of coef l to include intercept cat attr w latex sep n Ei nk 4 xtrans lt eval attr w function This is an S function of a single argument lines x coef 1 xtrans x type 1 Plots fitted transformation xtrans lt rcsplineFunction knots coef xtrans lines x xtrans x col blue 212 redun x lt blood pressure xx simple lt cbhind x pmax x knots 1 0 3 pmax x knots 2 0 3 pmax x knots 3 0 3 pmax x knots 4 0 3 pred value lt coef 1 xx simple w plot x pred value type 1 same as above redun Redundancy Analysis Description Uses flexible parametric additive models see areg and its use of regression splines to determine how well each variable can be predicted from the remaining variables Variables are dropped in a stepwise fashion removing the most predictable variable at each step The remaining variables are used to predict The process continues until no variable still in the list of predictors can be predicted with an R or adjusted R of at least r2 or until dropping the variable with the highest R adjusted or ordinary would cause a variable that was dropped earlier to no longer be predicted at least at the r2 level from the now smaller list of predictors Usage redun formula data NULL subset NULL r2 0 9 type c ordinary adjusted nk 3 tlinear TRUE
343. oefficient 1 alpha binconf 21 method character string specifing which method to use The all method only works when x and n are length 1 The exact method uses the F distribution to com pute exact based on the binomial cdf intervals the wilson interval is score test based and the asymptotic is the text book asymptotic normal interval Following Agresti and Coull the Wilson interval is to be preferred and so is the default include x logical flag to indicate whether x should be included in the returned matrix or data frame include n logical flag to indicate whether n should be included in the returned matrix or data frame return df logical flag to indicate that a data frame rather than a matrix be returned Value a matrix or data frame containing the computed intervals and optionally x and n Author s Rollin Brant Modified by Frank Harrell and Brad Biggerstaff Centers for Disease Control and Prevention National Center for Infectious Diseases Division of Vector Borne Infectious Diseases P O Box 2087 Fort Collins CO 80522 2087 USA bkb5 cdc gov References A Agresti and B A Coull Approximate is better than exact for interval estimation of binomial proportions American Statistician 52 119 126 1998 R G Newcombe Logit confidence intervals and the inverse sinh transformation American Statisti cian 55 200 202 2001 L D Brown T T Cai and A DasGupta Interval estimation for a binom
344. of imputing NAs you may want to set shrink TRUE to avoid overfitting when developing a prediction equation to predict each variables from all the others see details below method for initializing scorings of categorical variables Default is mode to use a dummy variable set to 1 if the value is the most frequent value this is the default Use random to use a random 0 1 variable Set to asis to use the original integer codes as starting scores number of residuals to store if n impute is specified If the dataset has fewer than nres observations all residuals are saved Otherwise a random sample of the residuals of length nres without replacement is saved The default for nres is higher if boot method approximate bayesian an integer or logical vector specifying the subset of observations to fit These may be used if x is a formula The default na actionis na retain defined by transcan which keeps all observations with any NA values For impute transcan data is a data frame to use as the source of variables to be imputed rather than using where in For fit mult impute data is mandatory and is a data frame containing the data to be used in fitting the model but before imputations are applied Variables omitted from data are assumed to be available from frame 1 and do not need to be imputed Set to TRUE to get additional information printed when impcat tree such as the predicted probabilities of
345. of iterations to perform for transcan or predict For predict only one iteration is used if there are no NA values in the data or if imp con was used convergence criterion for transcan and predict eps is the maximum change in transformed values from one iteration to the next If for a given it eration all new transformations of variables differ by less than eps with or without negating the transformation to allow for flipping from the transfor mations in the previous iteration one more iteration is done for transcan During this last iteration individual transformations are not updated but coeffi cients of transformations are This improves stability of coefficients of canonical variates on the right hand side eps is ignored when rhsImp random for t ranscan causes imputed values on the transformed scale to be truncated so that their ranges are within the ranges of non imputed transformed values For predict curtail defaults to TRUE to truncate predicted transformed values to their ranges in the original fit xt for transcan set to TRUE to impute NA values on the original scales with constants medians or most frequent category codes Set to a vector of constants to instead always use these constants for imputation These imputed values are ignored when fitting the current working transformation for a single variable default is FALSE to use ordinary least squares or canonical variate estimates For the purposes
346. of the pch argument for such plots is 3 plus sign if you want to emphasize the interval more than the point estimate When the x variable contains a total of 5 columns the 2nd and 5th columns are treated as the 2nd and 3rd are treated above and the 3rd and 4th columns define an inner line segment that will have twice the thickness of the outer segments In addition tick marks separate the outer and inner segments This type of display an example of which appeared in The Elements of Graphing Data by Cleveland is very suitable for displaying two confidence levels e g 0 9 and 0 99 or the 0 05 0 25 0 75 0 95 sample quantiles for example For this display the central point displays well with a default circle symbol setTrellis sets nice defaults for Trellis graphics assuming that the graphics device has already been opened if using postscript etc By default it sets panel strips to blank and reference dot lines to thickness 1 instead of the Trellis default of 2 numericScale is a utility function that facilitates using xYplot to plot variables that are not considered to be numeric but which can readily be converted to numeric using as numeric A good example of this is timeDate variables in S Plus 5 and 6 numericScale converts the variable into an ordinary numeric variable If it is a timeDate variable two attributes are added to the resulting variable scales major and scales minor These are each lists with elements at to specify a ve
347. on knots for splines contrast matrix for cate gorical variables fillin the initial estimates for missing values NA if variable never missing ranges the matrix of ranges of the transformed variables min and max in first and sec ond row scale a vector of scales used to determine convergence for a transformation formula the formula if x was a formula and optionally a vector of shrinkage factors used for predicting each variable from the others For asis variables the scale is the average absolute difference about the median For other variables it is unity since canonical variables are standardized For xcoef row i has the coefficients to predict transformed variable i with the column for the coefficient of variable i set to NA If imputed TRUE was given an optional element imputed also appears This is a list with the vector of imputed values on the original scale for each variable containing NAs Matrices rather than vectors are returned if n impute is given If trantab TRUE the trantab element also appears as described above Ifn impute gt 0 transcan also returns a list residuals that can be used for future multiple imputation impute returns a vector the same length as var of class impute with NA values imputed predict returns a matrix with the same number of columns or variables as were in x fit mult impute returns a fit object that is a modification of the fit object created by fitting the completed dat
348. on fits that had a sum of squared errors obtained by predicting the original y vector from the original time vector and id vector that was less that or equal to the conf int quantile of all bootstrapped models plus the original model This will show how the point by point max and min were computed to form the simultaneous confidence band col pointwise band objective add ncurves mu mu lti lti method color for the pointwise confidence band Default is 2 which defaults to red for default Windows S PLUS setups the default is to use the 2 log of the Gaussian likelihood for computing the simultaneous confidence region If neither cor pattern nor rho was spec ified to rm boot the independent homoscedastic Gaussian likelihood is used Otherwise the dependent homoscedastic likelihood is used according to the spec ified or estimated correlation pattern Specify object ive sse to instead use the sum of squared errors set to TRUE to add curves to an existing plot If you do this titles and subtitles are omitted when using individual boot TRUE or curves in simultaneous band TRUI you can plot a random sample of ncurves of the fitted curves instead of plot ting up to B of them set to TRUE to draw multiple simultaneous confidence bands shaded with differ ent colors Confidence levels vary over the values in the mult i conf vector specifies the method of shading when mult i TRUE Default is to use
349. on on the S Plus default at the start of the session Step 1 Create initial draft of data frame We usually begin by importing a dataset from another application ASCII files may be imported using the scan and read table functions SAS datasets may be imported using the Hmisc sas get function which will carry more attributes from SAS than using File Import from the GUI menus But for most applications especially Excel File Import will suffice If using the GUI it is often best to provide variable names during the import process using the Options tab rather than renaming all fields later Of course if the data to be imported already have field names e g in Excel let S Plus use those automatically If using S Plus 4 x on Windows NT you can use a command to execute File Import e g import data FileName windows temp fev asc FileType ASCII DataFrame FEV Here we name the new data frame FEV rather than fev because we wanted to distinguish a variable in the data frame named fev from the data frame name For S Plus 6 x the command will look instead like the following FEV lt importData tmp fev asc Step 2 Clean up data frame make it be mor efficiently stored Unless using sas get to import your dataset 54 data frame create modify check sas get already stores data efficiently it is usually a good idea to run
350. on to print the fitted function Recodes variables Reshape a matrix into 3 vectors reshape serial data Bootstrap spline fit to repeated measurements model with simultaneous confidence region least squares using spline function in time Generate multinomial random variables with varying prob Sample size for 2 sample binomial problem Rick Chappell chappell stat wisc edu Convert SAS dataset to S data frame Enhanced importing of SAS transport dataset in R Enhancement of save Add 1 dimensional scatterplot to an axis of an existing plot 124 score binary sedit setpdf setps setTrellis show col show pch showPsfrag solvet somers2 spearman spearman test spearman2 spower spss get src store strmatch subset substi summarize summary formula symbol freq HmiscOverview like bar codes FEH Martin Maechler maechler stat math ethz ch Jens Oehlschlaegel Akiyoshi oehl psyres stuttgart de Construct a score from a series of binary variables or expressions A set of character handling functions written entirely in S sedit does much of what the UNIX sed program does Other functions included are substring location substring lt replace string wild and functions to check if a string is numeric or contains only the digits 0 9 Adobe PDF graphics setup for including graphics in books and reports with nice defaults minimal wasted space Postscript graphics setup for including graphics
351. onal arguments to print char matrix For latex methods these are passed to latex default One of the most important of these is file Specifying file will cause LaTeX code to just be printed to standard output rather than be stored in a per manent file object an object created by summary formula quant vector of quantiles to use for summarizing data with method reverse This must be numbers between 0 and 1 inclusive and must include the numbers 0 5 0 25 and 0 75 which are used for printing and for plotting quantile intervals The outer quantiles are used for scaling the x axes for such plots Specify outer quantiles as O and 1 to scale the x axes using the whole observed data ranges instead of the default a 0 95 quantile interval Box percentile plots are drawn using all but the outer quantiles vnames By default tables and plots are usually labeled with variable labels see the label and sas get functions To use the shorter variable names specify vnames name pch vector of plotting characters to represent different groups in order of group levels For method response the characters correspond to levels of the stratify variable if superposeStrata TRUE and if no strata are used or if superposeStrata FALSE the pch vector corresponds to the which argument for method response superposeStrata If stratify was used set superposeStrata FALSE to make separate dot charts for each level of the st ratification va
352. one of the following Describe all variables in a data frame generalization of SAS UNIVARIATE Describe a variable generalization of SAS UNIVARIATE Assists with batch analyses Dot chart for one or two classification variables Enhancement of Trellis dotplot allowing for matrix X var auto generation of Key function superposition Simple mouse driven drawing program including a function for fitting Bezier curves Empirical cumulative distribution function plot Edit an object in place may be dangerous e g eip sqrt will replace the builtin sqrt function Plot with error bars Charles Geyer U Chi mod FEH Plot general event charts Jack Lee jjlee mdanderson org Ken Hess Joel Dubin Am Statistician 54 63 70 2000 Event history chart with time dependent cov status Joel Dubin jdubin uwaterloo ca Find matches with tolerances between columns of 2 matrices Find the first word in an S expression R Heiberger Fit most regression models over multiple transcan imputations compute imputation adjusted variances and avg betas Format a matrix or data frame with much user control 122 ftupwr ftuss gbayes getHdata gs slide hdquantile histbackback hist data frame histSpike hoeffd impute interaction is present james stein labcurve label Lag latex ldBands list tree Load mask matchCases matxv mem mgp axis mgp axis labels HmiscOverview R Heiberger and FE Harrell Power of 2 sam
353. ontrol time is qtmax survival probability corresponding to the last time point used for approximating survival and hazard functions Default is 001 For qtmax of the time for which a simulated time is needed which corresponds to a survival probability of less than qtmax the simulated value will be tmax number of points used for approximating functions for use in plotting default is 200 equally spaced points optional arguments passed to the scont rol function when it s evaluated by Quantile2 a single character constant may be abbreviated specifying which functions to plot The default is both meaning both survival and hazard functions Spec ify what drop to just plot the dropin and dropout functions what hratio to plot the hazard ratio functions or a11 to make 4 separate plots showing all functions 6 plots if dropsep TRUE set dropsep TRUE to make plot Quantile2 separate pure and contami nated functions onto separate plots vector of line types vector of colors optional x axis limits optional y axis limits label curves optional list which is passed as the opt s argument to labcurve spower 253 Value spower returns the power estimate fraction of simulated chi squares greater than the alpha critical value Quantile2 returns an S Plus function of class Quantile2 with attributes that drive the plot method The major attribute is a list containing several lists Each of these sub lists contains
354. ontrol labelling with argument custom If custom is chosen user must specify location and text of labels using y axis custom at and y axis custom labels arguments respec tively listed below This argument will not be utilized if y idl abels is spec ified y axis custom at user specified vector of y axis label locations Must be used when y axis custom will not be used otherwise y axis custom labels user specified vector of y axis labels Must be used when y axis custom will not be used otherwise y julian logical flag which will only be considered ify axis auto and is na y var amp y var type d If FALSE default will convert julian numeric dates or S Plus dates objects into mm dd yy format for the y axis labels If TRUE dates will be printed in julian numeric format y lim extend two dimensional vector representing the number of units that the user wants to increase ylim on bottom and top of y axis respectively Default c 0 0 This argument will not take effect if the Goldman chart is utilized y lab single label to be used for entire y axis Default will be the variable name or column number of y idlabels if non missing and blank otherwise x axis all logical flag If TRUE default lower and upper limits of x axis will be based on all observations rows in matrix or data frame If FALSE lower and upper limits will be based only on those observations specified by subset r
355. ooled with other categories see combine levels in spearman2 and chiSquare in which case it also applies to the response The default minlev 0 causes no pooling what specifies which statistic to plot Possibilities include the column names that appear with the print method is used sort set sort FALSE to suppress sorting variables by the statistic being plotted main main title for plot Default title shows the name of the response variable 24 bi Var xlab x axis label Default constructed from what vnames set to labels to use variable labels in place of names for plotting If a variable does not have a label the name is always used Details Uses midranks in case of ties as described by Hollander and Wolfe P values for Spearman Wilcoxon or Kruskal Wallis tests are approximated by using the t or F distributions Value spearman2 default the function that is called for a single x i e when there is no formula returns a vector of statistics for the variable biVar spearman2 formula and chiSquare return a matrix with rows corresponding to predictors Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Hollander M and Wolfe D A 1973 Nonparametric Statistical Methods New York Wiley Press WH Flannery BP Teukolsky SA Vetterling WT 1988 Numerical Recipes in C Cam bridge Cambridge University Press See Also combine levels
356. optional group variable can be used for stratification where the different strata are depicted using different colors If the q vector is specified the desired quantiles over all groups are displayed with solid triangles below each axis When the sample size exceeds 2000 this value may be modified using the nhist Spike argument datadensity calls hist Spike instead of scat1d to show the data density for numeric vari ables This results in a histogram like display that makes the resulting graphics file much smaller In this case datadensity uses the minf argument see below so that very infrequent data values will not be lost on the variable s axis although this will slightly distort the histogram histSpike is another method for showing a high resolution data distribution that is particularly good for very large datasets say n gt 1000 By default hist Spike bins the continuous x variable into 100 equal width bins and then computes the frequency counts within bins if n does not exceed 10 no binning is done If add FALSE the default the function displays either proportions or frequencies as in a vertical histogram Instead of bars spikes are used to depict the frequencies If add FALSE the function assumes you are adding small density displays that are intended to take up a small amount of space in the margins of the overall plot The frac argument is used as with scat1d to determine the relative length of the whole plot that is used t
357. or an object created by hoeffd y a numeric vector or matrix which will be concatenated to x ignored Details Uses midranks in case of ties as described by Hollander and Wolfe P values are approximated by linear interpolation on the table in Hollander and Wolfe which uses the asymptotically equivalent Blum Kiefer Rosenblatt statistic For P lt 0001 or gt 0 5 P values are computed using a well fitting linear regression function in log P vs the test statistic Ranks but not bivariate ranks are computed using efficient algorithms see reference 3 Value a list with elements D the matrix of D statistics n the matrix of number of observations used in analyzing each pair of variables and P the asymptotic P values Pairs with fewer than 5 non missing values have the D statistic set to NA The diagonals of n are the number of non NAs for the single variable corresponding to that row and column Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Hoeffding W 1948 A non parametric test of independence Ann Math Stat 19 546 57 Hollander M and Wolfe D A 1973 Nonparametric Statistical Methods pp 228 235 423 New York Wiley Press WH Flannery BP Teukolsky SA Vetterling WT 1988 Numerical Recipes in C Cam bridge Cambridge University Press See Also Fear varclus 128 html Examples Ks eZ Ly Op Ly 2 y lt c 4 Tae Oye Lp 4
358. or a vector and the output is cleaned up if simp1ify TRUE It uses code adapted from Tony Plate tplate blackmesacapital com to operate on grouped submatrices As mApply can be much faster than using by it is often worth the trouble of converting a data frame to a numeric matrix for processing by mApply asNumericMatrix will do this and matrix2dataFrame will convert a numeric matrix back into a data frame Usage mApply X INDEX FUN simplify TRUE keepmatrix FALSE Arguments X a vector or matrix capable of being operated on by the function specified as the FUN argument INDEX list of factors each of same number of rows as X has FUN the function to be applied In the case of functions like oe optional arguments to FUN simplify set to FALSE to suppress simplification of the result in to an array matrix etc keepmatrix set to TRUE to keep result as a matrix even if simplify is TRUE in the case of only one stratum mChoice 161 Value For mApp Ly the returned value is a vector matrix or list If FUN returns more than one number the result is an array if simp1lify TRUE and is a list otherwise If a matrix is returned its rows corre spond to unique combinations of INDEX If INDEX is a list with more than one vector FUN returns more than one number and simplify FALSE the returned value is a list that is an array with the first dimension corresponding to the last vector in INDEX the second dimension
359. or bpower if n is given n1 and n2 are set ton 2 alpha type I error fraction fraction of observations in group 1 power the desired probability of detecting a difference nsim number of simulations of binomial responses Details For bpower sim all arguments must be of length one Value for bpower the power estimate for bsamsize a vector containing the sample sizes in the two groups for ballocation a vector with 4 fractions of observations allocated to group 1 optimiz ing the four criteria mentioned above For bpower sim a vector with three elements is returned corresponding to the simulated power and its lower and upper 0 95 confidence limits AUTHOR Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu 28 bpplot References Fleiss JL Tytun A Ury HK 1980 A simple approximation for calculating sample sizes for com paring independent proportions Biometrics 36 343 6 Brittain E Schlesselman JJ 1982 Optimal allocation for the comparison of proportions Biomet rics 38 1003 9 Gordon I Watson R 1996 The myth of continuity corrected sample size formulae Biometrics 52 71 6 See Also samplesize bin chisq test binconf Examples bpower 1 odds ratio 9 n 1000 alpha c 01 05 bpower sim 1 odds ratio 9 n 1000 bsamsize 1 05 power 95 ballocation 1 5 n 100 Plot power vs n for various odds ratios base prob 1 n lt seq 10 1000 by
360. or density when there is a mixture of two priors with mixing probability 0 5 The first prior is almost non informative normal with mean 0 and variance 10000 and the second has mean 2 and variance 0 3 The test statistic has a value of 3 with variance 0 4 f lt gbayesMixPost 3 4 mix 0 5 d0 0 v0 10000 d1l 2 vl1l 0 3 args f Plot this density delta lt seq 2 6 length 150 plot delta f delta type 1 Add to the plot the posterior density that used only the almost non informative prior lines delta f delta mix 1 lty 2 The same but for an observed statistic of zero lines delta f delta mix 1 x 0 lty 3 Derive the CDF instead of the density g lt gbayesMixPost 3 4 mix 0 5 d0 0 v0 10000 dl 2 v1 0 3 what cdf Had mix 0 or 1 gbayeslPowerNP could have been used instead of gbayesMixPowerNP below Compute the power to detect an effect of delta 1 if the variance of the test statistic is 0 2 gbayesMixPowerNP g 1 0 2 interval c 10 12 Do the same thing by simulation gbayesMixPowerNP g 1 0 2 interval c 10 12 nsim 20000 Compute by what factor the sample size needs to be larger the variance needs to be smaller so that the power is 0 9 ratios lt seq l 4 length 50 pow lt single 50 for i in 1 50 pow i lt gbayesMixPowerNP g 1 0 2 ratios i interval c 10 12 2 Solve for ratio using reverse linear interpolation app
361. ord data cens mark cens mark cex Diy sOy 0 85 draw age stratified event history graph for Stanford heart data stratified by age status horiz TRU colors 1 len 20 E seq 0 r heart one 4 lt 0 survtime col heart one 2 surv col heart one 3 covtime cols cbhind rep 0 dim heart one 1 heart one 1 cov cols cbhind rep 0 dim heart one 1 heart one 7 num colors 2 colors c 6 10 x lab time under observation n in days title Figure 5a nStanford data n age lt 48 cens mark right TRUE cens mark cens mark ahead 40 0 cens mark cex 0 85 xlim c 0 1900 event history data heart one subset rows heart one 4 gt 0 survtime col heart one 2 surv col heart one 3 covtime cols cbhind rep 0 dim heart one 1 heart one 1 cov cols cbhind rep 0 dim heart one 1 heart one 7 num colors 2 colors c 6 10 x lab time under observation n in days title Figure 5b nStanford data n age gt 48 cens mark right TRUE cens mark cens mark ahead 40 0 cens mark cex 0 85 xlim c 0 1900 dev off par omi omi 98 find matches we will not show liver cirrhosis data manipulation as it was a bit detailed however here is th event history code to produce Figure 7 Plate 1 Figure 7 Plate 1 prothrombin ehg with color Not run second arg lt 1 second arg is for shading
362. ordered concordant number of relevant pairs for which x and S are concordant uncertain number of pairs of non missing observations for which censoring prevents clas sification of concordance of x and S rcorrcens formula returns an object of class biVar which is documented with the biVar function Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu 204 rcorr cens References Newson R Confidence intervals for rank statistics Somers D and extensions Stata Journal 6 309 334 2006 See Also somers2 biVar rcorrp cens Examples set seed 1 x lt round rnorm 200 y lt rnorm 200 rcorr cens x y outx TRUE can correlate non censored variables library survival age lt rnorm 400 50 10 bp lt rnorm 400 120 15 bp 1 lt NA d time lt rexp 400 cens lt runif 400 5 2 death lt d time lt cens d time lt pmin d time cens rcorr cens age Surv d time death r lt rcorrcens Surv d time death age bp r plot r Show typical 0 95 confidence limits for ROC areas for a sample size with 24 events and 62 non events for varying population ROC areas Repeat for 138 events and 102 non events set seed 8 par mfrow c 2 1 for i in 1 2 nl lt c 24 138 i nO lt c 62 102 i y lt c rep 0 n0 rep 1 n1 deltas lt seq 3 3 by 25 C lt se lt deltas j lt 0 for d in de
363. ore the adjustment settings will matter Default values are the medians of the values defined by values or probs You only need to name the predictors for which you are overriding the default settings Example adj to c x2 0 x5 10 will set x2 to 0 and x5 to 10 when assessing the impact of variation in the other predictors newdata a data frame or list containing the same number of values of all of the predictors used in the fit For factor predictors the levels attribute do not need to be in the same order as those used in the original fit and not all levels need to be represented If newdata is omitted you can still obtain linear predictors on the transformed response scale and fitted values on the original response scale but not terms type specifies how Function is to return the series of functions that define the trans formations of all variables By default a list is created with the names of the list elements being the names of the variables Specify type individual to have separate functions created in the session frame frame 0 the de fault or in location defined by where if where is specified For the latter method the names of the objects created are the names of the corresponding variables prefixed by prefix and with suffix appended to the end If any of frame where prefix or suffix is specified type is automatically setto individual ytype By default the first function created by Function is the y transformation
364. oreign library to import SAS datasets SAS date time and date time variables are converted respectively to Date POSIX or chron objects in R variable names are converted to lower case SAS labels are associated with variables and by default integer valued variables are converted from storage mode double to integer Ifthe user ran PROC FORMAT CNTLOUT in SAS and included the resulting dataset in the SAS version 5 transport file variables having customized formats that do not include any ranges i e variables having standard PROC FORMAT VALUE label formats will have their for mat labels looked up and these variables are converted to S factors For those users having access to SAS method csv is preferred when importing several SAS datasets Run SAS macro exportlib sas available from http biostat mc vanderbilt edu twiki pub Main Hmisc exportlib sas to convert all SAS datasets in a SAS data library from any engine supported by your system into CSV files If any customized formats are sasxport get 231 used it is assumed that the PROC FORMAT CNTLOUT dataset is in the data library as a regular SAS dataset as above SASdsLabels reads a file containing PROC CONTENTS printed output to parse dataset labels assuming that PROC CONTENTS was run on an entire library Usage sasxport get file force single TRUE method c read xport dataload csv formats NULL allow NULL out NULL keep N
365. ory graph for right censored survival data including time dependent covari ate status as described in Dubin Muller and Wang 2001 Effectively a Kaplan Meier curve is produced with supplementary information regarding individual survival information censoring information and status over time of an individual time dependent covariate or time dependent co variate function for both uncensored and censored individuals Usage event history data Arguments data survtime col surv col surv ind subset rows covtime cols cov cols survtime col surv ind c 1 0 covtime cols NULL surv col subset rows NULL NULL cov cols 1 cut cov NULL colors density 10 mult end cens 1 05 mark right FALSE cens mark cens mark ahead 0 5 cens mark cutoff cens mark cex 1 x lab time under observation y lab estimated survival probability title event history graph num colors cens cens 1 1e 08 A matrix or data frame with rows corresponding to units often individuals and columns corresponding to survival time event censoring indicator Also multiple columns may be devoted to time dependent covariate level and time change Column in data representing minimum of time to event or right censoring time for individual Column in data representing event indicator for an individual Though tradi tionally such an indicator will be 1 for an ev
366. ot Cbind y Lower Upper month groups year data s keys lines method alt Can also do s lt summarize y llist month year quantile probs c 5 25 75 stat name c y Q1 Q3 xYplot Cbind y Q1 Q3 month groups year data s keys lines To display means and bootstrapped nonparametric confidence intervals use for example s lt summarize y llist month year smean cl boot xYplot Cbind y Lower Upper month year data s For each subject use the trapezoidal rule to compute the area under the time response curve using the Hmisc trap rule function x lt cbhind time c 1 2 4 7 1 3 5 10 response c 1 3 2 4 1 3 2 4 subject lt c rep 1 4 rep 2 4 trap rule x 1 4 1 x 1 4 2 summarize x subject function y trap rule y 1 yI 2 Not run Another approach would be to properly re shape the mm array below This assumes no missing cells There are many other approaches mApply will do this well while allowing for missing cells m lt tapply y list year month quantile probs c 25 5 75 mm lt array unlist m dim c 3 2 12 dimnames list c lower median upper c 1997 1998 as character 1 12 aggregate will help but it only allows you to compute one quantile at a time see also the Hmisc mApply function dframe lt aggregate y list Year year Month month quantile probs 5 Comput xpected life length by race
367. ot returns an invisible vector that is the frequency table of the number of missing variables per observation plotMult Sim invisibly returns the limits of similarities used in constructing the y axes of each subplot For simi larity ccbothpos the hclust object is NULL na pattern creates an integer vector of frequencies Side Effects plots Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Sarle WS The VARCLUS Procedure SAS STAT User s Guide 4th Edition 1990 Cary NC SAS Institute Inc Hoeffding W 1948 A non parametric test of independence Ann Math Stat 19 546 57 See Also hclust plclust hoeffd rcorr cor model matrix locator na pattern Examples set seed 1 xl lt rnorm 200 x2 lt rnorm 200 x3 lt x1 x2 rnorm 200 x4 lt x2 rnorm 200 x lt cbind x1 x2 x3 x4 v lt varclus x similarity spear spearman is the default anyway v invokes print varclus print round v sim 2 plot v plot varclus age sys bp dias bp country 1 abbrev TRUE the 1 causes k dummies to be generated for k countries plot varclus age factor disease code 1 use varclus data fracmiss maxlevels minprev to analyze all useful variables see dataframeReduce for details about arguments df lt data frame a c 1 2 3 b c 1 2 3 c c 1 2 NA d c 1 NA 3 e c 1 NA 3 f
368. otting categorical variables Limits for continuous variables are determined by the outer quantiles specified in quant x axis label set to TRUE to add to an existing plot a main title For method reverse this applies only to the plot for cate gorical variables summary formula subtitles caption title 277 set to FALSE to suppress automatic subtitles character string containing LaTeX table captions name of resulting LaTeX file omitting the tex suffix Default is the name of the summary object If capt ion is specied t it le is also used for the table s symbolic reference label trios If for method response you summarized the response s by using three quantiles specify t rios TRUE or trios v to group each set of three statis tics into one column for latex output using the format a B c where the outer quantiles are in smaller font scriptsize For trios TRUE the overall column names are taken from the column names of the original data matrix To give new column names specify t rios v where v is a vector of column names of length m 3 where m is the original number of columns of summary statistics rowlabel see latex default under the help file Latex cdec number of decimal places to the right of the decimal point for latex This value should be a scalar which will be properly replicated or a vector with length equal to the number of columns in the table For response tables this
369. ouse but ton where upper left corner of statistics should appear Alternative is 11 to place below the graph on the lower left or the actual x and y coordinates Use none to suppress statistics Value list with components knots x xbeta lower upper which are respectively the knot locations design matrix linear predictor and lower and upper confidence limits Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also lrm cph respline eval plot supsmu coxph fit lrm 2t Examples rcspline plot cad dur tvdlm m 150 rcspline plot logl0 cad dur 1l tvdlm m 150 210 rcspline restate rcespline restate Re state Restricted Cubic Spline Function Description This function re states a restricted cubic spline function in the un linearly restricted form Coef ficients for that form are returned along with an S functional representation of this function and a LaTeX character representation of the function rcsplineFunction is a fast function that creates a function to compute a restricted cubic spline function with given coefficients and knots without reformatting the function to be pretty i e into unrestricted form Usage respline restate knots coef type c ordinary integral x X Llx nchar x norm 2 columns 65 before amp amp after begin nbegin 0 digits max 8 OptionsSdigits rcesplineFunction knots
370. output n refers to the minimum sample size x refers to the sequential x distribution cluster and c refers to the sequential x y profile cluster Graphs from method lattice are produced by xyplot and in the panel titles distribution refers to the x distribution stratum and cluster refers to the x y profile cluster 48 Value curveRep a list of class curveRep with the following elements res ns nomit missfreq nceuts kn kxdist k p X y id a hierarchical list first split by sample size intervals then by x distribution clus ters then containing a vector of cluster numbers with id values as a names attribute a table of frequencies of sample sizes per curve after removing NAs total number of records excluded due to NAs a table of frequencies of number of NAs excluded per curve cut points for sample size intervals number of sample size intervals number of clusters on x distribution number of clusters of curves within sample size and distribution groups number of points at which to evaluate each curve for clustering input data after removing NAs curveSmooth returns a list with elements x y id Note The references describe other methods for deriving representative curves but those methods were not used here The last reference which used a cluster analysis on principal components motivated curveRep however Author s Frank Harrell Department of Biostatistics Vanderbilt University f har
371. ox smooth xout x y data density on curve scatld x curve smooth same effect as previous command histSpike x curve smooth add TRUE same as previous but with histogram histSpike x curve smooth type density add TRUE same but smooth density over curve plot x lt rnorm 250 y lt 3 x rnorm 250 2 scatld x tfrac 0 dots randomly spaced from axis scatld y 4 frac 03 bars outside axis scatld y 2 tfrac 2 same bars with smaller random fraction x lt c 0 3 rep 4 3 5 rep 7 10 9 plot x jitter2 x original versus jittered values abline 0 1 unique values unjittered on abline points x 0 1 jitter2 x limit FALSE col 2 allow locally maximum jittering points x 0 2 jitter2 x fill 1 col 3 abline h seq 0 5 9 1 lty 2 fill 3 3 instead of 1 3 x lt rnorm 200 0 2 1 y lt x 2 x2 lt round xtrnorm 200 2 2 x3 lt round xtrnorm 200 4 4 dfram lt data frame y x x2 x3 plot dfram x2 dframSy jitter2 via scatld scatld dfram x2 y dfram y preserve TRUE col 2 scatld dfram x2 preserve TRUE frac 0 02 col 2 scatld dframSy 4 preserve TRUE frac 0 02 col 2 pairs jitter2 dfram pairs for jittered data frame This gets reasonable pairwise scatter plots for all combinations of variables where continuous variables with unique values are not jittered at all thus all relations between continuous variables are shown as they are
372. p x st lt spearman2 group x list P st PP stat SCTE J AE st fe dtl dft2 Jy testname if st df1 1 Wilcoxon else Kruskal Wallis statname F latexstat F_ df plotmathstat F df catTest function tab st lt if is matrix tab nrow tab lt 2 ncol tab lt 2 list p value NA statistic NA parameter NA else chisq test tab correct FALSE list P stSp value stat stSstatistic df stSparameter testname Pearson statnam Chi square latexstat chi 2 _ df plotmathstat chi df 2 Fr ordTest function group x f lt lrm x group stats list P stats P stat stats Model L R df stats d f testname Proportional odds likelihood ratio statnam Chi square latexstat chi 2 _ df plotmathstat chi df 2 Fori ode S3 method for class summary formula response x i Jj drop FALSE S3 method for class summary formula response print x vnames c labels names prUnits TRUE abbreviate dimnames FALSE prefix width min colwidth formatArgs NULL S3 method for class summary formula response plot x which 1 vnames c labels names xlim xlab pch c 16 1 2 17 15 3 4 5 0 SuperposeStrata TRUE dotfont 1 add FALSE reset par TRUE main subtitles TRUE S3 method for class summary formula response latex object title first word deparse substitut
373. p function Can be a vector a numeric character or factor categorical variable used for stratifying esti mates If group is present as many ECDFs are drawn as there are non missing group levels 74 Ecdf label curves applies if more than one group exists Default is TRUE to use labcurve to xlim subtitles datadensity side frac dens opts n unique na big vnames method fun data groups subset prepanel panel label curves where they are farthest apart Set label curves toa list to specify options to labcurve e g label curves list method arrow cex 8 These option names may be abbreviated in the usual way arguments are abbreviated Use for example label curves list keys 1 5 to draw symbols periodically as in pch 1 5 see points on the curves and au tomatically position a legend in the most empty part of the plot Set Label curves FALSE to suppress drawing curve labels The col 1ty and type parameters are au tomatically passed to labcurve although you can override them here You can set label curves list keys lines to have different line types defined in an automatically positioned key x axis limits Default is entire range of x set to FALSE to suppress putting a subtitle at the bottom left of each plot The subtitle indicates the numbers of non missing and missing observations which are labeled n m If datadensity is not none either scatldor hist Spik
374. pass to latex default Default is the first word of the character string version of the first calling argument caption to pass to latex default Default is the heading attribute from the object produced by bystats rowlabel to pass to latex default Default is the byvarnames at tribute from the object produced by bystats For bystats2 the default is woe an object created by bystats or bystats2 an object created by bystats or bystats2 for bystats a matrix with row names equal to the classification labels and column names N Missing funlab where funlab is determined from fun A row is added to the end with the summary statistics computed on all observations combined The class of this matrix is bystats For bystats returns a 3 dimensional array with the last dimension corresponding to statistics being computed The class of the array is bystats2 Side Effects latex produces a tex file 32 capitalize Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also interaction cut cut2 latex print char matrix translate Examples Not run bystats sex 2 county city bystats death race bystats death cut2 age g 5 race bystats cholesterol cut2 age g 4 sex fun median bystats cholesterol sex fun quantile bystats cholesterol sex fun function x c Mean mean x Median median x latex bystats death race nmiss FALSE
375. ple binomial test using Fleiss Tytun Ury Sample size for 2 sample binomial test using Both by Dan Heitjan dheitjan biostats hmc psu edu Bayesian posterior and predictive distributions when both the prior and the likelihood are Gaussian Fetch and list datasets on our web site Sets nice defaults for graph sheets for S Plus 2000 for copying graphs into Microsoft applications Harrell Davis nonparametric quantile estimator with s e Back to back histograms Pat Burns Salomon Smith Barney London pburns dorado sbi com Matrix of histograms for all numeric vars in data frame Use hist data frame data frame name Add high resolution spike histograms or density estimates to an existing plot Hoeffding s D test omnibus test of independence of X and Y Impute missing data generic method More flexible version of builtin function Tests for non blank character values or non NA numeric values James Stein shrinkage estimates of cell means from raw data Optimally label a set of curves that have been drawn on an existing plot on the basis of gaps between curves Also position legends automatically at emptiest rectangle Set or fetch a label for an S object Lag a vector padding on the left with NA or Convert an S object to LaTeX R Heiberger amp FE Harrell Lan DeMets bands for group sequential tests Pretty print the structure of any data object Alan Zaslavsky zaslavsk hcp med harvard edu Enhancement of load 8 b
376. plied to v by levels of x other arguments passed to FUN Value a new factor vector See Also factor requirePackage 215 Examples lt factor c a b b c lt 63 1y 1 5 reorder factor x v uses FUN mean Z3 lt x A levels w class w requirePackage require a packge and error if package is not installed Description require a package If package doesn t exist then throw an error Usage requirePackage package character only FALSE Arguments package character vector containing the names of packages to load character only a logical indicating whether package can be assumed to be character string arguments to be passed to require Author s Charles Dupont See Also library require Examples Not run requirePackage methods End Not run 216 reShape reShape Reshape Matrices and Serial Data Description If the first argument is a matrix reShape Strings out its values and creates row and column vectors specifying the row and column each element came from This is useful for sending matrices to Trellis functions for analyzing or plotting results of table or crosstabs or for reformatting serial data stored in a matrix with rows representing multiple time points into vectors The number of observations in the new variables will be the product of the number of rows and number of columns in the input matrix If the first argument
377. pss get file lowernames FALSE datevars NULL use value labels TRUE to data frame TRUE max value labels Inf force single TRUE allow NULL charfactor FALSE Arguments file input SPSS save file May be a file on the WWW indicated by file starting with http 256 src lowernames set to TRUE to convert variable names to lower case datevars vector of variable names containing dates to be converted to R internal format use value labels see read spss to data frame see read spss default is TRUE for spss get max value labels see read spss force single set to FALSE to prevent integer valued variables from being converted from storage mode double to integer allow a vector of characters allowed by R that should not be converted to periods in variable names By default underscores in variable names are converted to periods as with R before version 1 9 charfactor set to TRUE to change character variables to factors if they have at least two characters in an observation but have fewer than n 2 unique values Value a data frame or list Author s Frank Harrell See Also read spss cleanup import sas get Examples Not run w lt spss get tmp my sav datevars c birthdate deathdate End Not run src Source a File from the Current Working Directory Description src concatenates s to its argument quotes the result and sources in the file It sets options
378. r The values in this list may be character vectors redefining levels in order or another list see merge levels if using S Plus moveUnits set to TRUE to look for units of measurements in variable labels and move them toa units attribute If an expression in a label is enclosed in parentheses or brackets it is assumed to be units if moveUnits TRUE upData 315 fracmiss the maximum permissable proportion of NAs for a variable to be kept Default is to keep all variables no matter how many NAs are present maxlevels the maximum number of levels of a character or categorical or factor variable before the variable is dropped minprev the minimum proportion of non missing observations in a category for a binary Value variable to be retained and the minimum relative frequency of a category before it will be combined with other small categories a new data frame Autho r s Frank Harrell Vanderbilt University See Also sas get data frame describe label read csv strptime POSIXct Date Examples Not run dat lt read table myfile asc da ct ct lt cleanup import dat End Not run dat lt data frame a 1 3 d c 01 02 2004 1 3 04 cleanup import dat datevars d dateformat m d y fixdates year dat lt data frame a 1 3 7 y c a b1 b2 z 1 3 dat2 lt upData dat x x 2 x x 5 m x 10 rename c a x drop z labels c x X y
379. r imputing diastolic bp in this case At present predict does not work with multiple imputation When calling fit mult impute with glm as the fitter argument if you need to pass a family argument to glm do it by quoting the family e g family binomial fit mult impute will not work with proportional odds models when regression imputation was used as opposed to predictive mean matching That s because regression imputation will create values of the response variable that did not exist in the dataset altering the intercept terms in the model You should be able to use a variable in the formula given to fit mult impute asa numeric vari able in the regression model even though it was a factor variable in the invocation of transcan Use for example fit mult impute y codes x lrm trans thanks to Trevor Thompson trevor hpS eushc org Value For transcan a list of class transcan with elements call with the function call iter number of iterations done rsq rsq adj containing the R squares and adjusted R squares achieved in predicting each variable from all the others transcan 305 categorical the values supplied for categorical asis the values supplied for asis coef the within variable coefficients used to compute the first canonical variate xcoef the possibly shrunk across variables coefficients of the first canonical variate that predicts each variable in turn parms the parameters of the transformati
380. r is the recommended basis for choosing the num ber of knots or linearity set to FALSE to allow a target variable variable being imputed to have a non linear left hand side transformation when nk is 3 or greater The default is pmn for predictive mean matching which is a more nonpara metric approach that will work for categorical as well as continuous predictors Alternatively use regression when all variables that are sometimes miss ing are continuous and the missingness mechanism is such that entire intervals of population values are unobserved See the Details section for more infor mation For the plot method specify type hist to draw histograms of imputed values with rug plots at the top or type ecd the default to draw empirical CDFs with spike histograms at the bottom Defaults to mat ch weighted to do weighted multinomial probability sam pling using the tricube function similar to lowess as the weights The argument of the tricube function is the absolute difference in transformed predicted values of all the donors and of the target predicted value divided by a scaling factor The scaling factor in the tricube function is fweighted times the mean ab solute difference between the target predicted value and all the possible donor predicted values Set mat ch closest to find as the donor the observation having the closest predicted transformed value even if that same donor is found repeatedly Smoothing pa
381. r s Frank Harrell See Also par Examples Not run mgp axis mgp axis mgp axis mgp axis mgp axis options plot labels type x get default value for x axis labels type y get value for y axis labels type xy get 2nd element of both mgps labels type x and y get a list with 2 elements labels c 3 5 0 type x set mgp axis labels retrieve axes FALSE mgp axis l X Label mgp axis 2 Y Label End Not run 168 mhgr mhgr Miscellaneous Functions for Epidemiology Description The mhgr function computes the Cochran Mantel Haenszel stratified risk ratio and its confidence limits using the Greenland Robins variance estimator The 1rcum function takes the results of a series of 2x2 tables representing the relationship between test positivity and diagnosis and computes positive and negative likelihood ratios with all their deficiencies and the variance of their logarithms Cumulative likelihood ratios and their confidence intervals assuming independence of tests are computed assuming a string of all positive tests or a string of all negative tests The method of Simel et al as described in Altman et al is used Usage mhgr y group strata conf int 0 95 S3 method for class mhgr print x lrcum a c d conf int 0 95 S3 method for class lrcum print x dec 3 Arguments y a binary response variable group a variable with two uniqu
382. r strings in the vector base with the integers 1 2 reps The input data are rearranged by repeating each value of the baseline variables reps times and by transposing each observation s values of one of the set of repeated measurements as reps observations under the variable whose name does not have an integer pasted to the end if x has a row names attribute those observation identifiers are each repeated reps times in the output object See the last example Usage reShape x id colvar base reps times l reps timevar seqno constant NULL Arguments x a matrix or vector or when base is specified a list or data frame other optional vectors if x is a vector id A numeric character category or factor variable containing subject identifiers or a data frame of such variables that in combination form groups of interest Required if x is a vector ignored otherwise reShape 217 colvar A numeric character category or factor variable containing column identifiers colvar is using a time of data collection variable Required if x is a vector ignored otherwise base vector of character strings containing base names of repeated measurements reps number of times variables named in base are repeated This must be a constant times when base is given times is the vector of times to create if you do not want to use consecutive integers beginning with 1 timevar specifies the name of the time variable to create if t imes i
383. r table show pch 242 chisq test 2 25 199 chiSquare biVar 19 chron 163 229 ciapower 30 38 250 clara 42 43 45 cleanup import 42 50 111 162 163 253 255 cleanup import upData 309 lowess Misc 168 cnvrt coords 31 261 combine 34 combine levels 2 50 199 combine levels varclus 314 combine lt combine 34 confbar Misc 168 contents 35 229 contents list 229 cor 3 183 199 318 cor test 183 coxph 250 coxph fit 206 cph 206 250 cpower 31 37 188 250 Cs 39 50 csv get 40 163 cumcategory summary formula 266 cumsum 72 curveRep 42 curveSmooth curveRep 42 cut 29 48 cut2 21 29 47 199 263 275 323 329 Q data frame 42 50 163 255 312 data frame create modify check 49 data frame labelled label 138 data restore lll 342 datadensity 50 303 datadensity scati1d 231 dataframeReduce 211 316 317 dataframeReduce upData 309 dataRep 45 57 Date 42 85 86 89 163 255 308 312 333 Dates 229 DateTimeClasses 229 308 dec lt inc dec 128 deff 59 density 236 describe 36 50 60 128 140 175 176 229 312 322 detach 50 dimnames 214 discrete 65 dotchart 68 dotchart2 4 21 66 199 275 Dotplot xYplot 324 dotplot 329 download file 111 draw key 217 drawP lot labcurve 129 dropUnusedLevel1s 68 dvi latex 142 dvigv latex 142 dvips latex
384. r text to place at the end of each line of output begin text with which to start the first line of output Useful when adding LaTeX output to part of an existing formula nbegin number of columns of printable text in begin digits number of significant digits to write for coefficients and knots Value rcspline restate returns a vector of coefficients The coefficients are un normalized and two coefficients are added that are linearly dependent on the other coefficients and knots The vector of coefficients has four attributes knots is a vector of knots latex is a vector of text strings with the LaTeX representation of the formula columns used is the number of columns used in the output string since the last newline command function is an S function which is also return in character string format as the text attribute rcsplineFunction returns an S function with arguments x a user supplied numeric vector at which to evaluate the function and some automatically supplied other arguments Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also rcespline eval ns rcs latex Function transcan Examples set seed 1 x lt 1 100 y lt x 50 2 rnorm 100 0 50 plot x y xx lt respline eval x inclx TRU knots lt attr xx knots coef lt lsfit xx y coef options digits 4 rcspline restate must ignore intercept w lt respline restate knots c
385. racters are truncated at maxlen a vector of character strings containing labels corresponding to columns in the similar matrix if the column names of that matrix are not to be used an object created by naclus 320 which minlev abbrev slim slimds add ity col lwd vname h WwW u labelx xspace Details varclus defaults to a11 meaning to have naplot make 4 separate plots To make only one of the plots use which na per var dot chart of fraction of NAs for each variable na per obs dot chart showing frequency distri bution of number of variables having NAs in an observation mean na dot chart showing mean number of other variables missing when the indicated vari able is missing or na per var vs mean na aScatterplot showing on the x axis the fraction of NAs in the variable and on the y axis the mean number of other variables that are NA when the indicated variable is NA the minimum proportion of observations in a cell before that cell is combined with one or more cells If more than one cell has fewer than minlev n observa tions all such cells are combined into a new cell labeled OTHER Otherwise the lowest frequency cell is combined with the next lowest frequency cell and the level name is the combination of the two old level levels Gl set to TRUE to abbreviate variable names for plotting or printing Is set to TRU automatically if Llegend TRUE 2 vector specifying
386. rameter multiple of mean absolute difference used when mat ch Wweighted with a default value of 0 2 Set fweighted to a number between 0 02 and 0 2 to force the donor to have a predicted value closer to the target and set fweighted to larger values but seldom larger than 1 0 to allow donor val ues to be less tightly matched See the examples below to learn how to study the relationship between fweighted and the standard deviation of multiple imputations within individuals applies if type regression causing imputed values to be curtailed at the observed range of the target variable Set to FALSE to allow extrapolation outside the data range By default simple boostrapping is used in which the target variable is predicted using a sample with replacement from the observations with non missing target variable Specify boot method approximate bayesian to build the imputation models from a sample with replacement from a sample with re placement of the observations with non missing targets Preliminary simulations aregImpute 15 have shown this results in good confidence coverage of the final model parame ters when type regression is used Not implemented when group is used burnin aregImpute does burnin n impute iterations of the entire modeling process The first burnin imputations are discarded More burn in iteractions may be requied when multiple variables are missing on the same observations pr set
387. ranuni 1 x8 ranuni 1 output END DROP i RUN PROC MEANS RUN t i PROC COPY IN work OUT y S EL ECT test format z RUN xCreates test2 xpt lt sasxport get test2 xpt To use an existing copy of test2 xpt available on the web lt sasxport get http heswebl med virginia edu biostat s data sas test2 xpt format names for dataset test other than format had been exported as sasxport get would not create a list for that describe w test see labels Note if only one dataset just do describe w lapply w describe s descriptive stats for both datasets contents wStest another way to see variable attributes lapply w contents show contents of both datasets options digits 7 t sapply wSz c Mean mean x SD sqrt var x Min min x Max max x End Not run compare the following matrix with PROC MEANS output function x Save Faciliate Use of save and load to Remote Directories Description These functions are slightly enhanced versions of save and load that allow a target directory to be specified using options LoadPath pathname If the LoadPath option is not set the current working directory is used Usage options LoadPath mypath Save object name deparse substitute object Load object 234 scatld Arguments object the name of an object usually a data frame It must not be quoted nam
388. rary units as documented in the unit function This is especially useful for offset xrestrict When having labcurve label curves where they are most separated you can restrict the search for this separation point to a range of the x axis specified as a 2 vector xrestrict This is useful when one part of the curve is very steep Even though steep regions may have maximum separation the labels will collide when curves are steep pch vector of plotting characters for putKey and putKeyEmpty Can be any value including NA when only a line is used to indentify the group Is a single plotting character for Points with the default being the next unused value from among 1 2 3 4 16 17 5 6 15 18 19 file a file name suffix If specified plot drawPlot will send its output to a postscript file file ps using the set ps function to get nice defaults for inclu sion in reports plot set to FALSE to keep putKey or putKeyEmpty from actually drawing the key Instead the size of the key will be return by put Key or the coordinates of the key by putKeyEmpty ticks tells drawPlot which axes to draw tick marks and tick labels Default is none key for drawP lot and plot drawPlot Default is FALSE so that labcurve is used to label points or curves Set to TRUE to use putKeyEmpty Details The internal functions Points Curve Abline have unique arguments as follows label for Points and Curve is a single character
389. rawing dots Default is 0 8 Note that the original dotchart function used a default of 1 2 see par cex parameter that applies only to the line labels for the dot chart cex param eter for major grouping labels for dot chart2 Defaults to cex cex group labels sort add dot font groupfont reset par value of cex corresponding to gdata set to FALSE to keep dotchart2 from sorting the input data i e it will as sume that the data are already properly arranged This is especially useful when you are using gdata and groups and you want to control the order that groups appear on the chart from top to bottom set to TRUE to add to an existing plot font number of plotting dots Default is one Use 1 to use outline fonts For example pch 183 dotfont 1 plots an open circle for UNIX on postscript pch 1 makes an open octagon under Windows font number to use in drawing group labels for dotchart2 Default is 2 for boldface set to FALSE to cause dot chart2 to not reset the par parameters when fin ished This is useful when add TRUE is about to be used in another call The default is to reset the par parameters if add TRUE and not if add FALSE i e the program assumes that only one set of points will be added to an existing set If you fail to use reset par TRUE for the first of a series of plots the next call to plot with add TRUE will result in distorted x axis scaling dropUnusedLevel
390. rbilt edu See Also na omit na keep na detail response model frame default naresid naprint Examples options na action na delete ols y x na detail response Detailed Response Variable Information Description This function is called by certain na action functions if options na detail response TRUE is set By default this function returns a matrix of counts of non NAs and the mean of the response variable computed separately by whether or not each predictor is NA The default action uses the last column of a Surv object in effect computing the proportion of events Other summary functions may be specified by using options na fun response name of function Usage na detail response mf Arguments mf a model frame 178 na detail response Value a matrix with rows representing the different statistics that are computed for the response and columns representing the different subsets for each predictor NA and non NA value subsets Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also na omit na delete model frame default naresid naprint describe Examples sex lL mf EmeE Emm mn mmm tf Em fm age 1 NA 41 23 30 44 22 NA 32 37 34 38 36 36 50 40 43 34 22 42 30 y 1 01001201200112100110100 options na detail response TRUE na action na delete digits 3 lrm y agexsex Logistic Regression Model lrm formu
391. rdinates x and y can be scalars or vectors of length 2 If vectors of length 2 then they determine the opposite corners of the rectangle for the embedded plot and the parameters size vadj and hadj are all ignored If x and y are given as scalars then the plot position relative to the point and the size of the plot will be determined by the arguments size vadj and hadj The default is to center a 1 inch by 1 inch plot at x y Setting vadj and hadj to 0 0 will position the lower left corner of the plot at x y The rectangle defined by x y size vadj and hadj will be used as the plotting area of the new plot Any tick marks axis labels main and sub titles will be outside of this rectangle Any graphical parameter settings that you would like to be in place before fun is evaluated can be specified in the pars argument warning specifying layout parameters here p1t mf row etc may cause unexpected results After the function completes the graphical parameters will have been reset to what they were before calling the function so you can continue to augment the original plot 264 summarize Value An invisible list with the graphical parameters that were in effect when the subplot was created Passing this list to par will enable you to augment the embedded plot Author s Greg Snow greg snow imail org See Also cnvrt coords par symbols Examples make an original plot plot 11 20 sample 51 60 add so
392. re not supported for S Plus under Windows For R results may not be satisfac tory if par mfrow is in effect Author s Frank Harrell Examples plot 1 20 pstamp pwd TRUE time FALSE rcorr Matrix of Correlations and P values Description rcorr Computes a matrix of Pearson s r or Spearman s rho rank correlation coefficients for all possible pairs of columns of a matrix Missing values are deleted in pairs rather than deleting all rows of x having any missing variables Ranks are computed using efficient algorithms see reference 2 using midranks for ties Usage rcorr x y type c pearson spearman S3 method for class rcorr print x Arguments x a numeric matrix with at least 5 rows and at least 2 columns if y is absent For print x is an object produced by rcorr y a numeric vector or matrix which will be concatenated to x If y is omitted for rcorr x must be a matrix type specifies the type of correlations to compute Spearman correlations are the Pearson linear correlations computed on the ranks of non missing elements us ing midranks for ties argument for method compatiblity Details Uses midranks in case of ties as described by Hollander and Wolfe P values are approximated by using the t or F distributions 202 rcorr cens Value rcorr returns a list with elements r the matrix of correlations n the matrix of number of observa tions used in analyzing ea
393. rell vanderbilt edu References Segal M 1994 Representative curves for longitudinal data via regression trees J Comp Graph Stat 3 214 233 Jones MC Rice JA 1992 Displaying the important features of large collections of similar curves Am Statistician 46 140 145 Zheng X Simpson JA et al 2005 Data from a study of effectiveness suggested potential prog nostic factors related to the patterns of shoulder pain J Clin Epi 58 823 830 See Also clara dataRep curveRep 49 Examples Not run Simulate 200 curves with pre curve sample sizes ranging from 1 to 10 Make curves with odd numbered IDs have an x distribution that is random uniform 0 1 and those with even numbered IDs have an x dist that is half as wide but still centered at 0 5 Shift y values higher with increasing IDs set seed 1 N lt 200 nc lt sample 1 10 N TRUE id lt rep 1 N nc x lt y lt id for i in 1 N x id i lt if i y id i lt i 10 x id i 5 runif nc i 10 10 w lt curveRep x y id kxdist 2 p 10 w par ask TRUE mfrow c 4 5 plot w show everything profiles going across par mfrow c 2 5 plot w 1 show n 1 results Use a color assignment table assigning low curves to green and high to red Unique curv subject IDs are the names of the vector cols lt c rep green N 2 rep red N 2 names cols lt as character 1 N
394. rences between normal means The Statistician 46 209 226 Grouin JM Coste M Bunouf P Lecoutre B 2007 Bayesian sample size determination in non sequential clinical trials Statistical aspects and some regulatory considerations Stat in Med 26 49 14 4924 Examples Compare 2 proportions using the var stabilizing transformation arcsin sqrt x 3 8 n 3 4 Anscombe which has variance 1 4 n 5 ml lt 100 m2 lt 150 deathsl lt 10 deaths2 lt 30 f lt function events n asin sqrt events 3 8 n 3 4 gbayes 111 stat lt f deathsl ml f deaths2 m2 var stat lt function ml m2 1 4 m1 5 1 4 m2 5 cat Test statistic format stat s d format sqrt var stat ml m2 n Use unbiased prior with variance 1000 almost flat b lt gbayes 0 1000 ml m2 stat var stat 2xml 2 m2 print b plot b To get posterior Prob parameter gt w use l1 pnorm w bSmean post sqrt bSvar post If g effect nl n2 is the power function to detect an effect of effect with samples size for groups 1 and 2 of nl n2 estimate th xpected power by getting 1000 random draws from the posterior distribution computing power for each value of the population effect and averaging the 1000 powers This code assumes that g will accept vector valued effect For the 2 sample proportion problem just addressed effect could be taken approximately as the change in the arcsin of the
395. repency translate c dog cat2 snake c dog cat animal S Plus gives 1 animal animal2 snake trunc POSIXt return the floor ceiling or rounded value of date or time to specified unit Description trunc POSIXt returns the date truncated to the specified unit ceiling POSIXt returns next ceiling of the date at the unit selected in units floor POSIXt trunk POSIXt by another name round POSIXt returns the date or time value rounded to nearest specified unit selected in digits trunc POSIXt and round POSIXt have been extended from the base package functions Usage ceil x units Default S3 method ceil x units S3 method for class POSIXt trunc x units c secs mins hours days months years ers S3 method for class POSIXt ceil x units c secs mins hours days months years S3 method for class POSIXt round x digits c secs mins hours days months years Arguments x date to be floored ceilinged truncated or rounded units unit to that is is rounded up or down to digits same as units but different name to be compatible with round generic further arguments to be passed to or from other methods units 311 Value An object of class POSTX1t Author s Charles Dupont See Also Date POSIXt POSIX1t DateTimeClasses Examples date lt ISOdate 1832 7 12 ceil date units months 1
396. riable formethod response The default is to superposition all strata on one dot chart summary formula dot font reset par 275 font for plotting points set to FALSE to suppress the restoring of the old par values in plot summary formula response abbreviate dimnames prefix width min colwidth formatArgs digits prn prnmiss pctdig npct npct size Nsize excludel prUnits sep prtest round see print char matrix see print char matrix minimum column width to use for boxes printed with print char matrix The default is the maximum of the minimum column label length and the mini mum length of entries in the data cells a list containing other arguments to pass to format default suchas scientific e g formatArgs list scientific c 5 5 Forprint summary formula revers and format summary formula reverse formatArgs applies only to statistics computed on continuous variables not to percents numerators and denominators The round argument may be preferred number of significant digits to print Default is to use the current value of the digits system option set to TRUE to print the number of non missing observations on the current row variable The default is to print these only if any of the counts of non missing values differs from the total number of non missing values of the left hand side variable For met hod cross the default is to always print N
397. ription For any number of cross classification variables byst ats returns a matrix with the sample size number missing y and fun non missing y with the cross classifications designated by rows Uses Harrell s modification of the interaction function to produce cross classifications The default fun is mean and if y is binary the mean is labeled as Fraction There isa print method as well as a latex method for objects created by bystats bystats2 handles the special case in which there are 2 classifcation variables and places the first one in rows and the second in columns The print method for bystats2 uses the S Plus print char matrix function to organize statistics for cells into boxes Usage bystats y fun nmiss subset S3 method for class bystats print x S3 method for class bystats latex object title caption rowlabel bystats2 y v h fun nmiss subset S3 method for class bystats2 print x abbreviate dimnames FALSE prefix width max nchar dimnames x 1 S3 method for class bystats2 latex object title caption rowlabel Arguments y a binary logical or continuous variable or a matrix or data frame of such vari ables If y is a data frame it is converted to a matrix If y is a data frame or matrix computations are done on subsets of the rows of y and you should spec ify fun so as to be able to operate on the matrix For matrix y any col
398. rol event case lt align ids tapply u y iscase u idcase iscase sum event control lt align ids tapply uSy iscase uSidcase iscase sum n lt n case n control Remove sets having 0 cases or 0 controls in resample S lt n case gt 0 amp n control gt 0 denom lt sum event control s n case s vent case s n s or lt if denom 0 NA els sum event case s n control s event control s n s denom ors i lt or describe ors first word First Word in a String or Expression Description first word finds the first word in an expression A word is defined by unlisting the elements of the expression found by the S parser and then accepting any elements whose first character is either a letter or period The principal intended use is for the automatic generation of temporary file names where it is important to exclude special characters from the file name For Microsoft Windows periods in names are deleted and only up to the first 8 characters of the word is returned Usage first word x i l expr substitute x Arguments x any scalar character string i word number default value 1 Used when the second or ith word is wanted Currently only the i 1 case is implemented expr any S object of mode expression format df 103 Value a character string Author s Frank E Harrell Jr Department of Biostatistics Vanderbilt University f harre
399. roups of points in the order they are defined with the mouse Abline is used to call abline and not actually great a group of points For some curve types the curve generated to represent the corresponding series of points is drawn after all points are entered for that series and this curve may be different than the simple curve obtained by connecting points at the mouse clicks For example to draw a general smooth Bezier curve the user need only click on a few points and she must overshoot the final curve coordinates to define the curve The originally entered points are not erased once the curve is drawn The same goes for step functions and poly nomials If you plot the object returned by drawPlot however only final curves will be shown The last examples show how to use drawPlot The largest empty function finds the largest rectangle that is large enough to hold a rectangle of a given height and width such that the rectangle does not contain any of a given set of points This is used by labcurve and putKeyEmpty to position keys at the most empty part of an existing plot Usage labcurve curves labels names curves method NULL keys NULL keyloc c auto none type 1 step type c left right xmethod if any type s unique else grid offset NULL xlim NULL tilt FALSE window NULL npts 100 cex NULL adj auto angle adj auto 30 lty prsSlty lwd pr lwd col pr col transparent TRUE arrow factor
400. row group labels The default bfseries sets the current font to bold A vector of strings can be supplied to format each row group label differently Normal recycling applies if the vector is shorter than n rgroups See also cgroupTexCmd above regarding multiple effects rownamesTexCmd A character string specifying a LaTeX command to be used to format rownames The default NULL applies no command A vector of different commands can also be supplied See also cgroupTexCmd above regarding multiple effects colnamesTexCmd cellTexCmds A character string specifying a LaTeX command to be used to format column labels The default NULL applies no command It is possible to supply a vector of strings to format each column label differently If column groups are not used the first item in the vector will be used to format the title Please note that if column groups are used the first item of cgroupTexCmd and not colnamesTexCmd is used to format the title The user needs to allow for these issues when supplying a vector of commands See also cgroupTexCmd above regarding multiple effects A matrix of character strings which are LaTeX commands to be used to format each element or cell of the object The matrix must have the same NROW and NCOL as the object The default NULL applies no formats Empty strings also apply no formats and one way to start might be to create a matrix of empty 150 na blank latex strings withmatri
401. row names letters 1 n options digits 3 w u lt reShape w base c sbp dbp reps 3 u reShape w base c sbp dbp reps 3 timevar week times c 0 3 12 rlegend Special Version of legend for R Description rlegend is a version of legend for R that implements plot FALSE adds grid TRUE and defaults lty lwd pch to NULL and checks for length gt 0 rather than missing so it s easier to deal with non applicable parameters But when grid is in effect the preferred function to use is rlegendg which calls the Lattice draw key function Usage rlegend x y legend fill col black lty NULL lwd NULL pch NULL rlegendg x y legend col pr col 1 lty NULL lwd NULL pch NULL cex pr cex 1 other NULL Arguments x y legend fill col 220 rlegend lity lwd pch angle density bty bg pt bg cex xjust yjust x intersp y intersp adj text width merge trace ncol horiz see legend plot set to FALSE to suppress drawing the legend This is used the compute the size needed for when the legend is drawn with a later call to rlegend grid set to TRUE if the grid package is in effect see legend other a list containing other arguments to pass to draw key See the help file for xyplot Value a list with elements rect and text rect has elements w h left top with size position information Author s Frank Harrell and R Core See Also
402. rox pow ratios xout 0 9 y Check this by computing power gbayesMixPowerNP g 1 0 2 2 1 interval c 10 12 So the study will have to be 2 1 times as large as earlier thought getHdata Download and Install Datasets for Hmisc Design and Statistical Modeling getHdata Description This function downloads and makes ready to use datasets from the main web site for the Hmisc and Design libraries For R the datasets were stored in compressed save format and getHdata makes them available by running load after download For S Plus the datasets were stored in data dump format and are made available by running data restore after import The dataset is run through the cleanup import function to reduce multiple inheritance problems for SV4 S Plus 5 or later Calling getHdata with no file argument provides a character vector of names of available datasets that are currently on the web site For R R s default browser can optionally be launched to view html files that were already prepared using the Hmisc command html contents orto view txt or htm1 data description files when available Usage getHdata file what c data contents description all where http biostat mc vanderbilt edu twiki pub Main DataSets Arguments file an unquoted name of a dataset on the web site e g prostate Omit file to obtain a list of available datasets what specify what contents to browse the contents metadata for t
403. rvation after rounding for variables that were through roundN in the formula given to dat aRep is computed For numeric dataRep 61 variables the percentile of the original distribution in which the current value falls will be computed For this purpose the data are not rounded because the 101 original percentiles were retained linear interpolation is used to estimate percentiles for values between two tabulated percentiles The lowest marginal frequency of matching values across all variables is also computed For example if an age sex combination matches 10 subjects in the original dataset but the age value matches 100 ages after rounding and the sex value matches the sex code of 300 observations the lowest marginal frequency is 100 which is a best case upper limit for multivariable matching I e matching on all variables has to result on a lower frequency than this amount A print method for the output of predict dataRep prints all calculations done by predict by default Calculations can be selectively suppressed Usage dataRep formula data subset na action roundN x tol 1 clip NULL S3 method S3 method S3 method print x prdata TRUE prpct TRU Arguments formula x object data subset tol clip long newdata prdata for class dataRep print x long FALSE for class dataRep predict object newdata for class pred
404. s If omitted single predicted expected value imputation is used n impute 5 is frequently recommended default is to use the approximate Bayesian bootstrap sample with replacement from sample with replacement of the vector of residuals You can also specify boot method simple to use the usual bootstrap one stage sampling with replacement Set to TRUE to add an attribute t rantab to the returned matrix This con tains a vector of lists each with components x and y containing the unique val ues and corresponding transformed values for the columns of x This is set up to be used easily with the approx function You must specify trantab TRUE if you want to later use the predict transcan function with type original set to TRUE to cause t ranscan to return an object t rans formed containing the matrix of transformed variables transcan 299 impcat This argument tells how to impute categorical variables on the original scale The default is impcat score to impute the category whose canonical variate score is closest to the predicted score Use impcat tree to impute categorical variables using the t ree function using the values of all other transformed predictors impcat rpart willuse rpart A better but somewhat slower approach is to use impcat multinom to fit a multinomial logistic model to the categorical variable at the last iteraction of the transcan algorithm This uses the mult inom function in the
405. s This is done using scat1d set to FALSE to suppress smoothed curves from being drawn This can make sense if datadensity TRUE a logical or integer vector specifying a subset to use for processing with respect too all variables being analyzed set to TRUE if the R grid package drew the current plot optional arguments that are passed to scat 14d or optional parameters to pass to plsmo from panel plsmo See optional arguments for p1smo above plsmo 189 type set to p to have panel plsmo plot points and not call plsmo 1 to call plsmo and not plot points or use the default b to plot both pch cex font vectors of graphical parameters corresponding to the groups scalars if group is absent By default the parameters set up by trellis will be used Value plsmo returns a list of curves x and y coordinates that was passed to labcurve Side Effects plots and panel plsmo creates the Key function in the session frame See Also lowess supsmu label quantile labcurve scatild xyplot panel superpose panel xyplot Examples set seed 1 x lt 1 100 y lt x runif 100 10 10 pismo x y supsmu xlab Time of Use label y or y for ylab Entry plsmo x y add TRUE lt y 2 Add lowess smooth to existing plot with different lin typ age lt rnorm 500 50 15 survival time lt rexp 500 sample c female male 500 race lt sample c black non black plsmo
406. s or data frame variable name s with which to sort the chart s output The default is NA thereby resulting in a chart sorted by original row number sort ascending logical flag which takes effect only if the argument sort by is utilized If RUE default sorting is done in ascending order if FALSE descending order sort na last logical flag which takes effect only if the argument sort by is utilized If RUE default NA values are considered as last values in ordering sort after subset logical flag which takes effect only if the argument sort by is utilized If FALSE sorting data via sort by specified variables or columns will be per formed prior to row subsetting via subset r if TRUE default row subset ting of original data will be done before sorting y var variable name or column number of original matrix or data frame with which to scale y axis Default is NA which will result in equally spaced lines on y axis based on original data or sorted data if requested by sort by Otherwise loca tion of lines on y axis will be dictated by specified variable or column Examples of specified variables may be date of an event or a physiological covariate Any observation which has a missing value for the y var variable will not appear on the graph y var type type of variable specified in y var which will only take effect if argument y var is utilized If d specifed variable is a date eit
407. s smearingEst ytrans trans approx res fitted ignores res smearingEst ytrans exp res median median res 0 smearingEst ytrans exp rest 1 median median res 1 smearingEst ytrans trans approx res median smearingEst ytrans exp res mean mean exp ytrans 2 res should equal 2nd above smearingEst ytrans trans approx res mean smearingEst ytrans trans approx res mean Last argument can be any statistical function operating on a vector that returns a single value transcan Transformations Imputations using Canonical Variates Description transcan is a nonlinear additive transformation and imputation function and there are several functions for using and operating on its results t ranscan automatically transforms continuous and categorical variables to have maximum correlation with the best linear combination of the other variables There is also an option to use a substitute criterion maximum correlation with the first principal component of the other variables Continuous variables are expanded as restricted cubic splines and categorical variables are expanded as contrasts e g dummy variables By default the first canonical variate is used to find optimum linear combinations of component columns This function is similar to ace except that transformations for continuous variables are fitted using re stricted cubic splines monotonicity restrictions are not allowed and
408. s 71 xaxis set to FALSE to suppress drawing x axis width factor When the calculated left margin turns out to be faulty specify a factor by which to multiple the left margin as width factor to get the appropriate space for labels on horizonal charts lcolor color for horizontal reference lines Default is gray for R par col for S Plus arguments passed to plot default Side Effects GI dotchart will leave par altered if reset par FALSI Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also dotchart Examples set seed 135 maj lt factor c rep North 13 rep South 13 g lt paste Category rep letters 1 13 2 n lt sample 1 15000 26 replace TRUE yl lt runif 26 y2 lt pmax 0 yl runif 26 0 1 dotchart2 yl g groups maj auxdata n auxtitle n xlab Y dotchart2 y2 g groups maj pch 17 add TRUE Compare with dotchart function no superpositioning or auxdata allowed dotchart yl g groups maj xlab Y dropUnusedLevels Create Temporary Factor Subsetting Function Description Calling this function makes Hmisc have its pre version 3 0 behavior in which the R factor function was overridden by a customized version that caused unused factor levels to be dropped when the factor variable was subscripted subsetted drcopUnusedLevels creates a temporary version of factor in the global
409. s a limit for maximum shift in jittered values Duplicate values will be spread within fill xmin limit min u d d 1 2 The default TRUE restricts jittering to the smallest min u d d l 2 observed and results in equal amount of jittering for all d Setting to FALSE allows for locally different amount of jittering using maximum space available If the number of observations exceeds or equals nhist Spike scat1d will automatically call hist Spike to draw the data density to prevent the graphics file from being too large used by or passed to hist Spike Set to count to display frequency counts rather than relative frequencies or density to display a kernel density es timate computed using the density function set to TRUE if the R grid package is in effect for the current plot number of intervals to divide each continuous variable s axis for datadensity For hist Spike is the number of equal width intervals for which to bin x and if instead nint is a character string e g nint all1 the frequency tabu lation is done with no binning In other words frequencies for all unique values of x are derived and plotted optional arguments passed to scat 1d from datadensityortohistSpike from scat1d set to TRUE to prevent from sorting for determining the order l lt d lt u This is usefull if an existing meaningfull local order would be destroyed by sorting as in sin pi sort round runif 1000 0 10 1 an optional
410. s error pred abs error pred 2 areg areg 5 areg boot transace 283 aregImpute aregImpute 9 biVar biVar 19 bystats bystats 27 bystats2 bystats 27 char list 188 char matrix 28 29 190 275 contents data frame contents 35 contents list contents 35 curveRep curveRep 42 dataRep dataRep 57 describe describe 60 INDEX print dvi latex 142 print find matches find matches 95 print hoeffd hoeffd 123 print html htm1 125 print improveProb rcorrp cens 202 print impute impute 127 print labelled label 138 print latex latex 142 print 1ldBands ldBands 151 print lrcum mhgr 165 print mChoice mChoice 158 print mhgr mhgr 165 print popower popower 187 print posamsize popower 187 print predict dataRep dataRep a print Quantile2 spower 247 print rcorr rcorr 198 print redun redun 209 print summary areg boot transace 283 print summary formula cross summary formula 266 print summary formula response summary formula 266 print summary formula reverse summary formula 266 print summary 1ldBands ldBands 151 print summary lm 703 print summary mChoice mChoice 158 print t test cluster t test cluster 282 print transcan transcan 291 print varclus varclus 314 prn prnz 191 prnz 191 ps options 196 ps slide 192 pstamp 173 197 putKey labcurve 129 putKeyEmpty labcurve 129
411. s given if you do not want to use seqno constant a data frame with the same number of rows in id and x containing auxiliary information to be merged into the resulting data frame Logically the rows of constant within each group should have the same value of all of its variables Details In converting dimnames to vectors the resulting variables are numeric if all elements of the ma trix dimnames can be converted to numeric otherwise the corresponding row or column variable remains character When the dimnames if x have a names attribute those two names become the new variable names If x is a vector and another vector is also given in the matrices in the resulting list are named the same as the input vector calling arguments You can specify customized names for these on the fly by using e g reShape X x Y y id colvar The new names will then be X and Y instead of x and y A new variable named seqnno is also added to the resulting object seqno indicates the sequential repeated measurement number When base and times are specified this new variable is named the character value of timevar and the values are given by a table lookup into the vector times Value If x is a matrix returns a list containing the row variable the column variable and the as vector x vector named the same as the calling argument was called for x If x is a vector and no other vec tors were specified as the result is a matrix If at l
412. s in a printed table In what follows we create a data frame representing the combinations of values from an 8 x 2 x 2 x 2 event x method x sex x what table and add a non systematic variable percent to the data grid at any time Wheezing and breathless Wheezing without a cold Waking with tightness in the chest event c Wheezing 60 dataRep Waking with shortness of breath Waking with an attack of cough Attack of asthma Use of medication method c Mail Telephone sex c Male Female what c Sensitivity Specificity jcetableSpercent lt c 756 618 706 422 356 578 289 333 576 421 789 273 273 212 212 212 613 763 713 403 377 541 290 226 613 684 632 290 387 613 258 129 656 597 438 780 732 679 938 919 714 600 494 877 850 703 963 987 755 420 480 794 779 647 956 941 766 423 500 833 833 604 955 986 10 In jcetable event varies most rapidly then method then sex and what End Not run dataRep Representativeness of Observations in a Data Set Description These functions are intended to be used to describe how well a given set of new observations e g new subjects were represented in a dataset used to develop a predictive model The dataRep function forms a data frame that contains all the unique combinations of variable values that existed in a given set of variable values Cross classifications of values are
413. s list is for example upData FEV sex factor sex 0 1 c female male Note that we saved the changed data frame into a new data frame FEV2 If we were confident of the correctness of our changes we could have stored the new data frame on top of the old one under the original name FEV Step 6 Check the data frame The Hmisc describe function is perhaps the first function that should be used on the new data frame It provides documentation of all the variables and the frequency tabulation counts of NAs and 5 largest and smallest values are helpful in detecting data errors Typing describe FEV will write the results to the current output window To put the results ina new window that can persist even upon exiting S Plus we use the page function The describe output can be minimized to an icon but kept ready for guiding later steps of the analysis page describe FEV2 multi TRUE multi TRUE allows that window to persist while control is returned to other windows The new data frame is OK Store it on top of the old FEV and then use the graphical user interface to delete FEV2 click on it and hit the Delete data frame create modify check 57 key or type rm FEV2 after the next statement Hj EV lt FEV2 Next we can use a variety of other functions to check and describe all of the variables As we are analyzing all or almost all of the variables t
414. s specified constant accrual is assumed as is the minimum follow up time The maximum follow up time is then accrual tmin Treatment allocation is assumed to be 1 1 Usage ciapower tref nl n2 mlc m2c rl r2 accrual tmin alpha 0 05 pr TRUE Arguments tref time at which mortalities estimated n1 total sample size stratum 1 n2 total sample size stratum 2 mic tref year mortality stratum 1 control m2c tref year mortality stratum 2 control rl reduction in mic by intervention stratum 1 r2 reduction in m2c by intervention stratum 2 accrual duration of accrual period tmin minimum follow up time alpha type I error probability pr set to FALSE to suppress printing of details Value power 34 cnvrt coords Side Effects prints AUTHOR Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Peterson B George SL Controlled Clinical Trials 14 511 522 1993 See Also Cpower Spower Examples Find the power of a race x treatment test 25 of patients will be non white and the total sample size is 14000 Accrual is for 1 5 years and minimum follow up is 5y Reduction in 5 year mortality is 15 for whites 0 or 5 for non whites 5 year mortality for control subjects if assumed to be 0 18 for whites 0 23 for non whites n lt 14000 for nonwhite reduction in c 0 5 cat n n n Reduction in 5 year mortality for non
415. s the builtin page function but with better defaults Usage confbar at est se width gq c 0 7 Be 0 9 04955 OII col if R gray c 0 0 25 5 0 75 1 else if under unix c 1 0 8 0 5 0 2 0 065 else GC 4s Sy FoF type c v h labels TRUE ticks FALSE 0 0 cex 0 5 side 1 lwd 5 clip c le 30 1e 30 fun function x x qfun function x ifelse x 0 5 qnorm x ifelse x lt 0 5 qnorm x 2 qnorm 1 x 2 getLatestSource x NULL package Hmisc recent NULL avail FALSE type c svn cvs 7 inverseFunction x y james stein y group km quick S times q im fit qr bare x y tolerance intercept TRUE xpxi FALSE matxv a b kint 1 nomiss x Misc outerText string y adj 1 sepUnitsTrans x trap rule x 173 setAside string 1 side 4 cex par cex conversion c day 1 month 365 25 12 round FALSE digits 0 y space 1 year 365 25 week 7 trellis strip blank unPaste str xtended FALS J s p whichClosest x w whichClosePW x w f 0 2 xless x Arguments a adj at avail b cex clip col conversion digits est extended f fun group intercept kint labels lwd package q qfun recent round S title a numeric matrix or vector 0 for left justification 0 5 for centered 1 for right x coordinate for vertical
416. same size when a group has gt 125 observations bwplot g x panel panel bpplot cex 3 suppress means and reference lines for lower and upper quartiles continuous plot up until quartiles Tootsie Roll plot bwplot g x panel panel bpplot probs seq 01 25 by 01 start at quartiles then make it continuous coffin plot bwplot g x panel panel bpplot probs seq 25 49 by 01 same as previous but add a spike to give 0 95 interval bwplot g x panel panel bpplot probs c 025 seq 25 49 by 01 decile plot with reference lines at outer quintiles and median bwplot g x panel panel bpplot probs c 1 2 3 4 qref c 5 2 8 default plot with tick marks showing all observations outside the outer box 05 and 95 quantiles with very small ticks bwplot g x panel panel bpplot nout 05 scatid opts list frac 01 Eej p small dot for means show only 05 125 25 375 625 75 875 95 quantiles bwplot g x panel panel bpplot probs c 025 1 25 means FALSE qref FALS E 184 partition show 5 smallest and 5 largest observations bwplot g x panel panel bpplot nout 5 Use a scatld option preserve TRUE to ensure that the right peak extends to the same position as the extreme scatld bwplot x panel panel bpplot probs seq 00 5 by 001 datadensity TRUE scatld opt list preserve TRUE Draw a prototype showing how to interpret
417. sformations from ace are seldom smooth enough and are often overfitted With areg the complexity can be controlled with the nk parameter and predicted values are easy to obtain because parametric functions are fitted If one side of the equation has a categorical variable with more than two categories and the other side has a continuous variable not assumed to act linearly larger sample sizes are needed to reliably estimate transformations as it is difficult to optimally score categorical variables to maximize R against a simultaneously optimally transformed continuous variable Value a list of class areg containing many objects 10 areg Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu References Breiman and Friedman Journal of the American Statistical Association September 1985 See Also cancor ace transcan Examples set seed 1 ns lt c 30 300 3000 for n inns q y lt sample 1 5 n TRUE x lt abs y 3 runif n par mfrow c 3 4 for k in c 0 3 5 z lt areg x y ytype c nk k plot x zStx title paste R2 format zS rsquared tapply zSty y range a lt tapply x y mean b lt tapply zSty y mean plot a b abline lsfit a b Should get same result to within linear transformation if reverse x and y w lt areg y x xtype c nk k plot zSty wStx title paste R2 format wSrsquared a
418. sing non missing observations for that predictor biVar is used to do the looping and bookkeeping By default the plot shows the adjusted rho 2 using the same formula used for the ordinary adjusted R 2 The F test uses the unadjusted R2 spearman computes Spearman s rho on non missing values of two variables spoearman test is a simple version of spearman2 default chiSquare is set up like spearman2 except it is intended for a categorical response variable Separate Pearson chi square tests are done for each predictor with optional collapsing of infrequent categories Numeric predictors having more than g levels are categorized into g quantile groups chiSquare uses biVar Usage biVar formula statinfo data NULL subset NUL na action na retain exclude imputed TRU td E x lt S3 method for class biVar print x S3 method for class biVar plot x what info defaultwhat sort TRUE main xlab vnames c names labels spearman2 x Default S3 method spearman2 x y p l minlev 0 na rm TRUE exclude imputed na rm S3 method for class formula biVar 23 spearman2 formula data NULL subset na action na retain exclude imputed TRUI EJ lt spearman x y spearman test x y p 1 chiSquare formula data NULL subset NULL na action na retain exclude imputed TRUE Arguments formula a formula with a single left side variable stat
419. sion to pcx format Also implies close TRUI mi set to TRUE to initiate conversion to t i f f format Also implies close TRUI close bty type height width tck las eps sublines toplines leftlines trellis setTrellis strip blank lty dot lin lwd dot lin seqno color region ps slide set to TRUE to finish construction of the postscript file box type surrounding graph Default is 1 for L shape Use c for com plete box For ps slide type is an integer In this case set t ype 1 to use black on white background smaller pointsize and other settings that are good for making overhead transparencies and graphs to include in reports Set type 3 for 5 x 7 landscape plots and t ype 4 for overheads For setps and setpdf type char specifies that the filename argument is a character string or vector and any other value indicates that it is an unquoted name defaults to 5 if t ype 3 otherwise no default except for type 4 defaults to 7 if t ype 3 otherwise no default except for type 4 length of tick marks See par set to 0 to have axis labels always parallel to the axis 1 for always horizontal 2 for perpendicular to axis set to TRUE if you are going to be importing the postscript file to a system that really cares that it is marked to officially be encapsulated postscript If you set eps TRUE you may put only one figure in the file see the onefile argumen
420. sity f harrell vanderbilt edu Richard M Heiberger Department of Statistics Temple University Philadelphia PA rmh astro ocis temple edu See Also latex Examples Not run x lt data frame a 1 2 b 3 4 xSm lt matrix 5 8 nrow 2 names x dim x x format df x dim format df x End Not run 106 format pval format pval Format P Values Description format pval is intended for formatting p values Usage format pval x pv x digits max 1 OptionsSdigits 2 eps MachineSdouble er Arguments pv a numeric vector x argument for method compliance digits how many significant digits are to be used eps a numerical tolerance see Details na form character representation of NAs arguments passed to format in the format pval function body Details format pval is mainly an auxiliary function for print summary 1m etc and does separate formatting for fixed floating point and very small values those less than eps are formatted as lt eps where eps stands for format eps digits Value A character vector Note This is the base format pval function with the ablitiy to pass an nsma11 argument to format Examples format pval c runif 5 pi 100 NA format pval c 0 1 0 0001 le 27 format pval c 0 1 le 27 nsmall 3 gbayes 107 gbayes Gaussian Bayesian Posterior and Predictive Distributions Description gbay
421. smo 184 panel superpose 184 186 panel xYplot xYplot 324 panel xyplot 186 329 par 33 67 92 93 163 164 173 196 261 partition 181 paste 157 177 pcl 182 pdf 196 pipe 112 plelust 318 plot 93 206 plot areg areg 5 pP pP lot areg boot transace 283 lot aregImpute aregImpute 9 plot biVar biVar 19 plot curveRep curveRep 42 plot data frame 50 plot drawPlot labcurve 129 plot gbayes gbayes 104 O 00 0 GE UGE GE set CE EE 3 o o Lo lo o o INDEX ldBands 1dBands 151 Quantile2 spower 247 rm boot rm boot 218 summary formula response summary formula 266 summary formula reverse summary formula 266 transcan transcan 291 varclus varclus 314 window 76 tCorrPrecision 183 tmathTranslate label 138 tMultSim varclus 314 plsmo 184 236 322 points 242 polygon 93 223 popower 187 posamsize popower 187 POSIXct 42 312 POSIXL1t 308 POSIXt 308 333 postscript 196 prcomp 182 303 predab resample 289 predict areg areg 5 predict areg boot transace 283 predict dataRep dataRep 57 predict transcan transcan 291 prepanel Dotplot xYplot 324 prepanel Ecdf Ecdf 69 prepanel xYplot xYplot 324 prin prin prin prin prin prin prin prin prin prin prin prin prin prin prin er CE OPE OPE Pr EE EF p ET PrO OFE ET 192 ab
422. specified function evaluated at evaluation points spanning the whole x axis For the density the user must click in the left tail at the highest value at the mean and in the right tail with the two tail values being approx imately equidistant from the mean The density is scaled to fit in the highest value regardless of its area step type type of step functions used default is left xmethod method for generating the unique set of x coordinates to examine see above Default is grid for type 1 or unique for type s offset distance in y units between the center of the label and the line being labeled Default is 0 75 times the height of an m that would be drawn in a label For R grid lattice you must specify offset using the grid unit function e g offset unit 2 native oroffset unit 25 cm native means data units xlim limits for searching for label positions and is also used to set up plots when pl TRUE and add FALSE Default is total x axis range for current plot par usr 1 2 For largest empty xlim limits the search for largest rectanges but it has the same default as above For p1 TRUE add FALSE you may want to ex tend x1im somewhat to allow large keys to fit when using keyloc auto For drawP lot default is c 0 1 ta Lt set to TRUE to tilt labels to follow the curves for method offset when keys is not given window width of a window in x units to use in determining
423. sponding to these the number of extra characters to leave to the left of the string s adj 0 or to the right adj 1 a character string vector a character string vector set to TRUE to draw lines between rectangles a numeric vector of times a character string to title a window or plot tolerance for judging singularity in matrix y for vertical h for horizontal For get Latest Source this specifies the type of source code repository svn the default or cvs which is now outdated as Subversion has replaced CVS in the Vanderbilt Biostatistics server a numeric vector width of confidence rectanges in user units a numeric vector matrix for 1m fit qr bare or data frame For xless may be any object that is sensible to print For sepUnitsTrans is a char acter or factor variable For get Latest Source is a character string or vec tor of character strings containing base file names to retrieve from CVS Set x al1 to retrieve all source files For clowess x may also be a list with x and y components For inverseFunction x and y contain evaluations of the function whose inverse is needed x is typically an equally spaced grid of 1000 points set to TRUE to add an element to the result containing the inverse of X X a numeric vector For inverseFunction y is the evaluated function values at x arguments passed through to another function trap rule 1 100 1 100 unPaste c a b or c ab d qr s
424. sponds to the different groups regardless of how many bands are drawn The default colors for filled bands are pastel colors matching the default colors superpose linecol plot linecol a vector the same length as x giving a variable whose values are a linear function of the size of the symbol drawn This is used for example for bubble plots a vector of two values specifying the range in character sizes to use for the size variable lowest first highest second size values are linearly translated to this range based on the observed range of size when x and y coordinates are not missing Specify a single numeric cex value for rangeCex to use the first character of each observations s size as the plotting symbol set to FALSE to not make the panel strip backgrounds blank lty dot line line type for dot plot reference lines default 1 for dotted use 2 for dotted lwd dot line line thickness for reference lines for dot plots default 1 label a scalar character string to be used as a variable label after numericScale converts the variable to numeric form skip weekends Details see axis time Unlike xyplot xYplot senses the presence of a groups variable and automatically invokes panel superpose instead of panel xyplot The same is true for Dotplot vs dotplot Value Cbhind returns a matrix with attributes Other functions return standard trellis results Side Effects plots and panel xYplot may create te
425. square root of the probability of the event g lt function effect nl n2 alpha 05 sd lt sqrt var stat n1 n2 z lt qnorm 1 alpha 2 ffect lt abs effect 1 pnorm z effect sd pnorm z effect sd effects lt rnorm 1000 b mean post sqrt b var post powers lt g effects 500 500 hist powers nclass 35 xlab Power describe powers gbayes2 examples First consider a study with a binary response where th sample size is n1 500 in the new treatment arm and n2 300 in the control arm The parameter of interest is the treated control log odds ratio which has variance 1 nl pl 1 pl1 1 n2 p2 1 p2 This is not really constant so we average the variance over plausible values of the probabilities of response pl and p2 We think that these are between 4 and 6 and we take a further short cut v lt function nl n2 pl p2 1 nl pl 1 pl 1 n2 p2x 1 p2 ni lt 500 n2 lt 300 ps lt seq 4 6 length 100 vguess lt quantile v nl n2 ps ps 75 vguess 75 0 02183459 112 gbayes The minimally interesting treatment effect is an odds ratio of 1 1 The prior distribution on the log odds ratio is a 50 50 mixture of a vague Gaussian mean 0 sd 100 and an informative prior from a previous study mean 1 sd 1 prior lt function delta 0 5 dnorm delta 0 100 0 5 dnorm delta 1 1 deltas lt seq 5 5 length 150
426. ssure clinic describe Concise Statistical Description of a Vector Matrix Data Frame or Formula Description describe is a generic method that invokes describe data frame describe matrix describe vector or describe formula describe vector is the basic function for handling a single variable This function determines whether the variable is character factor cat egory binary discrete numeric and continuous numeric and prints a concise statistical summary according to each A numeric variable is deemed discrete if it has lt 10 unique values In this case quantiles are not printed A frequency table is printed for any non binary variable if it has no more than 20 unique values For any variable with at least 20 unique values the 5 lowest and highest values are printed This behavior can be overriden for long character variables with many levels using the 1istunique parameter to get a complete tabulation 64 describe describe is especially useful for describing data frames created by get as labels formats value labels and in the case of sas get frequencies of special missing values are printed For a binary variable the sum number of 1 s and mean proportion of 1 s are printed If the first argument is a formula a model frame is created and passed to describe data frame If a variable is of class impute a count of the number of imputed values is printed If a date variable has an attribute par
427. st values and the frequency table There are used if a formula is specified na action defaults to na retain which does not delete any NAs from the data frame Use na action na omit or na delete to drop any observation with any NA before processing arguments passed to describe default which are passed to calls to format for numeric variables For example if using R POSIXct or Date date time for mats specifying describe d format d b y will print date time variables as 01Jan2000 This is useful for omitting the time component See the help file for format POSIXct or format Date for more informa tion For latex methods is ignored 66 describe file name of output file should have a suffix of tex Default name is formed from the first word of the descript element of the describe object prefixed by describe Set file to send LaTeX code to standard output instead of a file append set to TRUE to have latex append text to an existing file named file size LaTeX text size smal1 the default or normalsize tiny scriptsize etc for the describe output in LaTeX tabular set to FALSE to use verbatim rather than tabular environment for the summary Statistics output By default tabular is used if the output is not too wide greek By default the latex methods will change LaTeX names of greek letters that appear in variable labels to appropriate LaTeX symbols in math mode unless greek FALSE greek TRUE
428. start a table Set table env FALSE to suppress enclosing the table in a LaTeX table environment table env only applies when Longtable FALSE You may not specify a caption if table env FALSE Set to TRUE if you are using table env TRUE with longtable FALSE and you have installed David Carlisle s here sty LaTeX style This will cause the LaTeX table environment to be set up with option H to guaran tee that the table will appear exactly where you think it will in the text The style element is set to here The latex usepackage must ref erence here The file here sty will need to be in a directory in your TEXINPUTS path here is largely obsolete with LaTeX2e Applies if longtable TRUE No more than lines page lines in the body of a table will be placed on a single page Page breaks will only occur at rgroup boundaries a text string to use as a caption to print at the top of the first page of the table Default is no caption a text string representing a short caption to be used in the List of Tables By default LaTeX will use caption If you get inexplicable latex errors you may need to supply caption lot to make the errors go away set to bottom to position a caption below the table instead of the default of t op X set to TRUE to output as in LaTeX commands Useful when you are reading the output file back into an S vector for later output logical When vbar TRUE columns in
429. subset sex female digits 2 f lt function y c Hazard sum y 2 sum y 1 gets the hazard estimate for right censored data from exponential dist bystats cbhind d time death race sex fun f bystats cbind pressure cholesterol age decile fun function y c Median pressure median y 1 Median cholesterol median y 2 y lt cbind pressure cholesterol bystats y age decile fun function y apply y 2 median same result as last one bystats y age decile fun function y apply y 2 quantile c 25 75 The last one computes separately the 0 25 and 0 75 quantiles of 2 vars latex bystats2 death race sex fun table End Not run capitalize capitalize the first letter of a string Description Capitalizes the first letter of each element of the string vector Usage capitalize string Arguments string String to be capitalized ciapower 33 Value Returns a vector of charaters with the first letter capitalized Author s Charles Dupont Examples capitalize c Hello bob daN ciapower Power of Interaction Test for Exponential Survival Description Uses the method of Peterson and George to compute the power of an interaction test in a 2 x 2 setup in which all 4 distributions are exponential This will be the same as the power of the Cox model test if assumptions hold The test is 2 tailed The duration of accrual i
430. summary etc would have automatically used imputation corrected variances and covariances Example demonstrating how using the multinomial logistic model to impute a categorical variable results in a frequency distribution of imputed values that matches the distribution of non missing values of the categorical variable Not run set seed 11 xl lt factor sample letters 1 4 1000 TRU x1 1 200 lt NA table x1 sum table xl1 x2 lt runif 1000 z lt transcan x1 I x2 n impute 20 impcat multinom table zSimputed x1 sum table zSimputed x1 End Not run iE 308 transcan Example where multiple imputations are for basic variables and modeling is done on variables derived from these set seed 137 n lt 400 x1 lt runif n x2 lt runif n y lt xl x2 x1 1 x2 rnorm n 3 x1 1 5 lt NA d lt data frame x1 x2 y w lt transcan xl x2 y n impute 5 data d Add show imputed actual for graphical diagnostics Not run g lt fit mult impute y product ratio ols w data data frame x1 x2 y derived expression product lt x1 x2 ratio lt x1 1 x2 print cbind x1 x2 x1 x2 product 1 6 End Not run Here s a method for creating a permanent data frame containing one set of imputed values for each variable specified to transcan that had at least one NA and also containing all the variables in an original data fr
431. t and no space is taken up for such variables in the main listing number of significant digits to print For a character variable that is not an mChoice variable that has its longest string length greater than 1istnchar and that has no more than 1istunique unique values all values are listed in alphabetic order Any value having more than one occurrence has the frequency of occurrence after it in parentheses Specify 1istunique equal to some value at least as large as the number of observations to ensure that all character variables will have all their values listed For purposes of tabulating character strings multiple white spaces of any kind are translated to a single space leading and trailing white space are ignored and case is ignored see Listunique a numeric vector of frequencies or sample weights Each observation will be treated as if it were sampled weights times value passed to summary mChoice The default normwt FALSE results in the use of weights as weights in computing various statistics In this case the sample size is assumed to be equal to the sum of weights Specify normwt TRUE to divide weights by a constant so that weights sum to the number of observations length of vectors specified to describe In this case the number of observations is taken to be the actual number of records given to describe aresult of describe unused default isTRUE to condense the output with regard to the 5 lowest and highe
432. t are used in the model formula This is useful when multiple imputations are done for component variables but the actual model uses combinations of these e g ratios or other derivations For a single derived variable you can specified for example derived expression ratio lt weight height For multiple derived variables use the form derived expression ratio lt weight height product lt weight height or put the ex pression on separate input lines To monitor the multiply imputed derived vari ables you can add to the expression acommand suchas print describe ratio See the example below Note that derived is not yet implemented By default the matrix of transformed variables is returned with imputed val ues on the transformed scale If you had specified trantab TRUE to transcan specifying type original does the table look ups with linear interpolation to return the input matrix x but with imputed values on the original scale inserted for NA values For categorical variables the method used here is to select the category code having a corresponding scaled value closest to the predicted transformed value This corresponds to the default impcat a problem in getting predicted values for t ree objects prevented using tree for this Note imputed values thus returned when type original are single expected value imputations even inn impute is given an object created by t ranscan or an object to be
433. t in postscript This applies to UNIX systems only other arguments to ps options or postscript for Windows or pdf graph for setpdf width of plot Default is chosen to scale nicely to h for a landscape plot height of plot default is 3in number of lines to reserve for subtitles number of lines to reserve for main title number of lines to reserve for left margin set to TRUE to set up for postscript output for Trellis graphics This makes trellis device postscript becalledinsteadofpostscript directly and leaves par parameters at defaults set to FALSE to prevent set Trellis from being called to set the strip panel background and to set characteristics for dot plot reference lines Gl set to FALSE to keep shading in conditioning variable panel titles if set Trellis TRUI if set Trellis TRUE the line type for dot plot reference lines default solid line if set Trellis TRUE the line width for dot plot reference lines default 1 if non null pastes the value of seqno at the end of the base of the file name for setps and setpdf set color TRUE to use a color Trellis device instead of default of black and white for setps For set pdf set to TRUE to get color pdf graphics see pdf graph Default is to use an image region that is just large enough to contain the graphic ps slide 199 ratio ratio of width to height of the plot when only one of those is
434. t lies in a variable size allowed for certain points as a function of the level of some variable s in the dataset default is NA extra points no mult vector of variables in the dataset to ignore for purposes of using point cex mult for example for some variables there may be interest in allowing a variable size allowed for the plotting of the points whereas other variables e g dropout time there may be no interest in such manipulation the vector should be the same size as the number of variables specified in subset c with NA entries where variable point size is of interest and the variable name or location in event chart legend legend legend legend legend legend legend legend legend legend legend legend 87 subset c specified when the variable point size is not of interest in this latter case the associated argument in point cex is instead used as the point cex used only when non NA arguments are provided to point cex mult var default is NA plot logical flag if TRUE a legend will be plotted Location of legend will be based on specification of legend location along with values of other arguments listed below Default is FALSE i e no legend plotting location will be used only if legend plot TRUE If o default a one page legend will precede the output of the chart The user will need to hit enter in order for the event chart to be displayed This feature is possibl
435. t ranks are still preserved scatld lwd col y Curve bottom align preserve aii limit nhistSpike type grid nint presorted group 237 line width for tick marks passed to segments color for tick marks passed to segments specify a vector the same length as x to draw tick marks along a curve instead of by one of the axes The y values are often predicted values from a model The side argument is ignored when y is given If the curve is already represented as a table look up you may specify it using the curve argument instead y may be a scalar to use a constant vertical placement a list containing elements x and y for which linear interpolation is used to derive y values corresponding to values of x This results in tick marks being drawn along the curve For histSpike interpolated y values are derived for bin midpoints set to TRUE to have the bottoms of tick marks for side 1 or side 3 aligned at the y coordinate The default behavior is to center the tick marks For datadensity data frame bottom align defaults to TRUEif nint gt 1 In other words if you are only labeling the first and last axis tick mark the scat 1d tick marks are centered on the variable s axis set to TRUE to invoke jitter2 maximum fraction of the axis filled by jittered values If d are duplicated values between a lower value 1 and upper value u then d will be spread within fill min u d d 1l 2 specifie
436. tSim plots multiple similarity matrices with the similarity measure being on the x axis of each subplot na pattern prints a frequency table of all combinations of missingness for multiple variables If there are 3 variables a frequency table entry labeled 110 corresponds to the number of observations for which the first and second variables were missing but the third variable was not missing Usage varclus x similarity c spearman pearson hoeffding bothpos ccbothpos type c data matrix similarity matrix method if R complete else compact data NULL subset NULL na action na retain S3 method for class varclus print x abbrev FALSE S3 method for class varclus plot x ylab abbrev FALSE legend FALSE loc maxlen labels naclus df method naplot obj which c all na per var na per obs mean na na per var vs mean na combine levels x minlev 05 plotMultSim s x 1 dim s 3 slim range pretty c 0 max s na rm TRUE slimds FALSE add FALSE lty par lty col par col lwd par lwd vname NULL h 5 w 75 u 05 labelx TRUE xspace 35 na pattern x Arguments x a formula a numeric matrix of predictors or a similarity matrix If x is a for mula model matrix is used to convert it to a design matrix If the formula excludes an intercept e g a b 1 the first categorical factor vari
437. ta frame Do analyses involving the whole data frame without attaching it Data frame still in Data Do analyses of individual variables after attaching the data frame in search position two or later Details The examples below use the FEV dataset from Rosner B 1995 Fundamentals of Biostatistics 4th Edition New York Duxbury Press Almost any dataset would do The jcetable data are taken from Galobardes et al 1998 J Clin Epi 51 875 881 Presently giving a variable the units attribute using the Hmisc units function only benefits the Hmisc describe function and the Design library s version of the Surv function Variables labels defined with the Hmisc label function are used by describe summary formula and many of the plotting functions in Hmisc and Design References Alzola CF Harrell FE 2001 An Introduction to S Plus and the Hmisc and Design Libraries Chap ters 3 and4 http biostat mc vanderbilt edu twiki pub Main RS sintro paf data frame create modify check 53 See Also scan read table cleanup import sas get data frame attach detach describe datadensity plot data frame hist data frame naclus factor label units names expand grid summary formula summary data frame casefold edit page plot data frame Cs combine levels upData Examples Not run First we do steps that create or manipulate the data frame in its entirety These are done with Data in search positi
438. th the original col lapsed data Side Effects print dataRep prints Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu See Also round table Examples set seed 13 num symptoms lt sample 1 4 1000 TRUE sex lt factor sample c female male 1000 TRUE Xx lt runif 1000 x 1 lt NA table num symptoms sex 25xround x 25 d lt dataRep num symptoms sex roundN x 25 print d long TRUE predict d data frame num symptoms 1 3 sex c male male female Xe 03y eor SNN deff Design Effect and Intra cluster Correlation Description Computes the Kish design effect and corresponding intra cluster correlation for a single cluster sampled variable describe 63 Usage deff y cluster Arguments y variable to analyze cluster a variable whose unique values indicate cluster membership Any type of vari able is allowed Value a vector with named elements n total number of non missing observations cLusters number of clusters after deleting missing data rho intra cluster correlation and deff design effect Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell vanderbilt edu See Also bootcov robcov Examples set seed 1 blood pressure lt rnorm 1000 120 15 clinic lt sample letters 1000 replace TRUE deff blood pre
439. the data frame through the Hmisc cleanup import function to change numeric variables that are always whole numbers to be stored as integers the remaining numerics to single precision strange values from Excel to NAs and character variables that always contain legal numeric values to numeric variables cleanup import typically halves the size of th data frame If you do not specify any parameters to cleanup import the function assumes that no numeric variable needs more than 7 significant digits of precision so all non integer valued variables will be converted to single precision FEV lt cleanup import FEV Step 3 Make global changes to the data frame A data frame has attributes that are external to its variables There are the vector of its variable names names attribute the observation identifiers row names and the class whose value is data frame The names attribute is the one most commonly in need of modification If we had wanted to change all the variable names to lower case we could have specified lowernames TRUE to the cleanup import invocation above or type names FEV lt casefold names FEV The upData function can also be used to change variable names in two ways see below To change names in a non systematic way we use other options Under Windows NT the most straigtforward approach is to change the names interactively Click on th
440. the error bars yminus vector of y axis values the bottoms of the error bars cap the width of the little lines at the tops and bottoms of the error bars in units of the width of the plot Defaults to 0 015 main a main title for the plot see also title sub a sub title for the plot xlab optional x axis labels if add FALSE ylab optional y axis labels if add FALSE Defaults to blank for horizontal charts add set to TRUE to add bars to an existing plot available only for vertical error bars lty type of line for error bars xlim x axis limits Default is range of x values ylim y axis limits Default is to use range of y yminus and yplus For horizonal charts ylim is really the x axis range excluding differences lwd line width for line segments not main line pch character to use as the point Type used for horizontal bars only Is an integer vector with values 1 if corresponding values represent simple estimates 2 if they represent differences ann a logical value indicating whether the default annotation title and x and y axis labels should appear on the plot axes a logical value indicating whether both axes should be drawn on the plot errbar 719 panel first an expression to be evaluated after the plot axes are set up but before any plotting takes place panel last an expression to be evaluated after plotting has taken place asp the y a aspect ratio see plot window other parameters passed to all graphi
441. the extracolheads argument to latex default auxCol is filled with blanks when a variable being sum marized takes up more than one row in the output This happens with categorical variables for method reverse specifies whether proportions or percentages are to be plotted for method cross with two right hand side variables twoway controls whether the resulting table will be printed in enumeration format or as a two way table the default For method response specifies the sequential number or a vector of sub scripts of response variables to plot If you had any stratify variables these are counted as if multiple response variables were analyzed For method reverse specifies whether to plot results for categorical variables continuous variables or both the default For plotting method reverse plots for continuous variables dot plots showing quartiles are drawn by default Specify conType bp to draw box percentile plots using all the quantiles in quant except the outermost ones Means are drawn with a solid dot and vertical reference lines are placed at the three quartiles Specify conType raw to make a strip chart showing the raw data This can only be used if the sample size for each left hand side group is less than or equal to nmin character size for means in box percentile plots default is 5 vector of length two specifying x axis limits For method reverse this is only used for pl
442. the limit of a bootstrap average as the number of bootstrap resamples becomes infinitely large Usage hdquantile x probs seq 0 1 0 25 se FALSE na rm FALSE names TRUE weights FALS Gl lt Arguments x a numeric vector probs vector of quantiles to compute se set to TRUE to also compute standard errors na rm set to TRUE to remove NAs from x before computing quantiles names set to FALSE to prevent names attributions from being added to quantiles and standard errors weights set to TRUE to return a weights attribution with the matrix of weights used in the H D estimator corresponding to order statistics with columns correspond ing to quantiles Details A Fortran routine is used to compute the jackknife leave out one quantile estimates Standard errors are not computed for quantiles 0 or 1 NAs are returned Value A vector of quantiles If se TRUE this vector will have an attribute se added to it containing the standard errors If weight s TRUE also has a weights attribute which is a matrix hist data frame 117 Author s Frank Harrell References Harrell FE Davis CE 1982 A new distribution free quantile estimator Biometrika 69 635 640 Hutson AD Ernst MD 2000 The exact bootstrap mean and variance of an L estimator J Roy Statist Soc B 62 89 94 See Also quantile Examples set seed 1 x lt runif 100 hdquantile x 1 3 4 se TRUE Not run
443. the local slope for tilting labels Default is 0 5 times number of characters in the label times the x width of an m in the current character size and font npts number of points to use if xmethod grid cex character size to pass to text and key Default is current par cex For putKey putKeyEmpty and Points is the size of the plotting symbol adj Default is auto which has labcurve figure justification automatically when method offset This will cause centering to be used when the local angle of the curve is less than angle adj auto in absolute value left justification if the angle is larger and either the label is under a curve of positive slope or over a curve of negative slope and right justification otherwise For step functions left justification is used when the label is above the curve and right justifcation otherwise Set adj 5 to center labels at computed coordinates Set to 0 for left justification for right Set adj to a vector to vary adjustments over the curves angle adj auto see adj Does not apply to step functions lty vector of line types which were used to draw the curves This is only used when keys are drawn If all of the line types line widths and line colors are the same lines are not drawn in the key labcurve lwd col col transparent arrow factor point inc opts key opts empty method numbins pl add ylim xlab ylab whichLabel 137 vector of line
444. the number of observation times vary greatly from subject to subject the resulting analysis will be a robust efficient repeated measures analysis for the one sample problem Predicted values of the fitted models are evaluated by default at a grid of 100 equally spaced time points ranging from the minimum to maximum observed time points Predictions are for the av erage subject effect Pointwise confidence intervals are optionally computed separately for each of the points on the time grid However simultaneous confidence regions that control the level of confidence for the entire regression curve lying within a band are often more appropriate as they allow the analyst to draw conclusions about nuances in the mean time response profile that were not stated apriori The method of Tibshirani 1997 is used to easily obtain simultaneous confidence sets for the set of coefficients of the spline or linear regression function as well as the average in tercept parameter over subjects Here one computes the objective criterion here both the 2 log likelihood evaluated at the bootstrap estimate of beta but with respect to the original design matrix and response vector and the sum of squared errors in predicting the original response vector for the original fit as well as for all of the bootstrap fits The confidence set of the regression coefficients is the set of all coefficients that are associated with objective function values that are less than or equ
445. the resulting labels would appear outside the plot region Both ordinary lines and step functions are handled and there is an option to draw the labels at the same angle as the curve within a local window Unless the mouse is used to position labels or plotting symbols are placed along the curves to distinguish them curves are examined at 100 by default equally spaced points over the range of x coordinates in the current plot area Linear interpolation is used to get y coordinates to line up step function or constant interpolation is used for step functions There is an option to instead examine all curves at the set of unique x coordinates found by unioning the x coordinates of all the curves This option is especially useful when plotting step functions By setting adj auto you can have labcurve try to optimally left or right justify labels depending on the slope of the curves at the points at which labels would be centered plus a vertical offset This is especially useful when labels must be placed on steep curve sections You can use the on top method to write short curve names directly on the curves centered on the y coordinate This is especially useful when there are many curves whose full labels would run into each other You can plot letters or numbers on the curves for example using the keys option and have labcurve use the key function to provide long labels for these short ones see the end of the example There is another
446. third arg lt c rep 1 18 0 1 third arg is for intensity postscript c temp ehgfig7 ps horiz TRUE colors cbind seq 0 1 len 20 second arg third arg par omi c 0 1 25 0 5 1 25 col 19 event history cirrhos2 eh subset rows NULL survtime col cirrhos2 ehStime surv col cirrhos2 ehSevent covtime cols as matrix cirrhos2 eh 2 18 2 cov cols as matrix cirrhos2 eh 2 18 2 1 cut cov as numeric quantile as matrix cirrhos2 eh 2 18 2 1 c 0 2 4 6 8 1 na rm TRUE c 1 0 0 0 0 1 colors c 20 4 8 11 14 x lab time under observation in days title Figure 7 Event history graph for liver cirrhosis data color cens mark right TRUE cens mark cens mark ahead 100 0 cens mark cex 0 85 dev off End Not run find matches Find Close Matches Description Compares each row in x against all the rows in y finding rows in y with all columns within a tolerance of the values a given row of x The default tolerance tol is zero i e an exact match is required on all columns For qualifying matches a distance measure is computed This is the sum of squares of differences between x and y after scaling the columns The default scaling values are tol and for columns with to1l 1 the scale values are set to 1 0 since they are ignored anyway Matches up to maxmatch of them are stored and listed in order of increasing distance The sum
447. thod for class data frame Ecdf x group rep 1 nrows weights normwt label curves TRUE n unique 10 na big FALSE subtitles TRU vnames Gl c labels names S3 method for class formula Ecdf x data sys frame sys parent groups NULL prepan ylab fun function x x subset TRUE Arguments x what weights normwt xlab ylab pl add lty lwd col group l prepanel Ecdf panel panel Ecdf xlab a numeric vector data frame or Trellis Lattice formula The default is F which results in plotting the fraction of values lt x Set to 1 F to plot the fraction gt x or to plot the cumulative frequency of values lt X numeric vector of weights Omit or specify a zero length vector or NULL to get unweighted estimates see above x axis label Default is label x or name of calling argument For Ecdf formula xlab defaults to the 1abe1 attribute of the x axis variable y axis label Default is Proportion lt x Proportion gt x or Frequency lt x depending on value of what a vector for quantiles for which to draw reference lines on the plot Default is not to draw any set to F to omit the plot to just return estimates set toTRUE to add the cdf to an existing plot integer line type for plot If group is specified this can be a vector line width for plot Can be a vector corresponding to groups color for ste
448. tial date this is set up by sas get counts of how many partial dates are actually present missing month missing day missing both are also presented If a variable was created by the special purpose function substi which substitutes values of a second variable if the first variable is NA the frequency table of substitutions is also printed A latex method exists for converting the describe object to a LaTeX file For numeric variables having at least 20 unique values describe saves in its returned object the frequencies of 100 evenly spaced bins running from minimum observed value to the maximum latex inserts a spike histogram displaying these frequency counts in the tabular material using the LaTeX picture envi ronment For example output see http biostat mc vanderbilt edu twiki pub Main Hmisc counties pdf Note that the latex method assumes you have the following styles installed in your latex installation setspace and relsize Sample weights may be specified to any of the functions resulting in weighted means quantiles and frequency tables Usage S3 method for class vector describe x descript exclude missing TRUE digits 4 listunique 0 listnchar 12 weights NULL normwt FALSE minlength NULL S3 method for class matrix describe x descript exclude missing TRUE digits 4 S3 method for class data frame describe x descript exclude missing TRUE digits 4
449. tion times 5 pmax abs times 5 3 y lt g times Generate multivariate normal errors for 20 subjects at 11 times Assume equal correlations of rho 7 independent subjects nt lt length times rho lt 7 set seed 19 errors lt mvrnorm n p nt S diag rep 1l rho nt rho Note first random number seed used gave rise to mean errors 0 24 Add E Y error components and subject effects y lt matrix rep ey n ncol nt byrow TRUE errors matrix rep sub nt ncol nt String out data into long vectors for times responses and subject ID y lt as vector t y times lt rep times n id lt sort rep 1 n nt Show lowess estimates of time profiles for individual subjects f lt rm boot times y id plot individual TRUE B 25 cor pattern estimate smoother lowess bootstrap type x fixed nk 6 In practice use B 400 or 500 This will compute a dependent structure log likelihood in addition to one assuming independence By default the dep structure objective will be used by the plot method could have specified rho 7 NOTE Estimating the correlation pattern from the residual does not work in cases such as this one where there are large subject effects Plot fits for a random sample of 10 of the 25 bootstrap fits plot f individual boot TRUE ncurves 10 ylim c 6 8 5 aneous confidence regions col pointwise 1 ylim c 6 8 5
450. tions digits 3 print s7 twoway FALSE s7 same as print s7 w lt latex s7 here TRU ake s7 tex H library trellis TRUE invisible ps options reset TRUE trellis device postscript file demo2 ps dotplot S size quartile bone data s7 s7 is name of summary stats xlab Fraction ap gt 1 ylab Quartile of Tumor Size Can do this more quickly with summarize s7 lt summarize ap gt 1 llist size cut2 sz g 4 bone mean stat name Proportion dotplot Proportion siz bone data s7 summary age stage method cross summary age stage fun quantile method cross summary age stage fun smean sd method cross summary age stage fun smedian hilow method cross symbol freq 283 summary age stage fun function x c Mean mean x Median median x method cross The next statements print real two way tables summary cbind age ap stage bone fun function y apply y 2 quantile c 25 75 method cross options digits 2 summary log ap sz bone fun function y c Mean mean y quantile y method cross Summarize an ordered categorical response by all of the needed cumulative proportions summary cumcategory disease severity age sex End Not run symbol freq Graphic Representation of a Frequency Table Description This function can be used to represent contingency tables graphically Freque
451. tions n impute 5 is frequently recommended but 10 or more doesn t hurt a character or factor variable the same length as the number of observations in data and containing no NAs When group is present causes a bootstrap sample of the observations corresponding to non NAs of a target variable to have the same frequency distribution of group as the that in the non NAs of the original sample This can handle k sample problems as well as lower the chance that a bootstrap sample will have a missing cell when the original cell frequency was low B 75 14 nk tlinear type match fweighted curtail boot method aregImpute number of knots to use for continuous variables When both the target variable and the predictors are having optimum transformations estimated there is more instability than with normal regression so the complexity of the model should decrease more sharply as the sample size decreases Hence set nk to 0 to force linearity for non categorical variables or 3 minimum number of knots possible with a linear tail restricted cubic spline for small sample sizes Simulated prob lems as in the examples section can assist in choosing nk See nk to a vector to get bootstrap validated and 10 fold cross validated R and mean and median absolute prediction errors for imputing each sometimes missing variable with nk ranging over the given vector The errors are on the original untransformed scale The mean absolute erro
452. tor specifying the names of tables to import Default is to import all tables Specify tables TRUE to return the list of available tables set this to TRUE to change variable names to lower case a vector of characters allowed by R that should not be converted to periods in variable names By default underscores in variable names are converted to periods as with R before version 1 9 see cleanup import Default is the usual Access format used in the U S arguments to pass to csv get Uses the mdb tools package executables mdb tables mdb schema and mdb export cleanup import is invoked by csv get to transform variables and store them as efficiently as possible 166 Value a new data frame or a list of data frames Author s Frank Harrell Vanderbilt University See Also mgp axis data frame cleanup import csv get Date chron Examples Not run Read all tables in the Microsoft Access database Nwind mdb d lt mdb get Nwind mdb contents d for z in d print contents z Just print the names of tables in the database mdb get Nwind mdb Import one table Orders lt mdb get Nwind mdb End Not run tables TRUE tables Orders mgp axis Draw Axes With Side Specific mgp Parameters Description mgp axis is a version of axis that uses the appropriate side specific mgp parameter see par to account for different space requirements for axis labels v
453. transEst a vector of transformed values In log normal regression these could be pre dicted log Y for example inverseTrans a function specifying the inverse transformation needed to change transEst to the original untransformed scale inverseTrans may also be a 2 element list defining a mapping from the transformed values to untransformed values Linear interpolation is used in this case to obtain untransform values monotonic transace categorical binary pl data weights subset na action B method nk evaluation valrsgq probs tolerance res statistic ylim boot 289 These are vectors of variable names specifying what to assume about each col umn of x for t ransace Binary variables are not transformed of course set p1 FALSE to prevent t ransace from plotting each fitted transformation data frame to use if x is a formula and variables are not already in the search list a numeric vector of observation weights By default all observations are weighted equally an expression to subset data if x is a formula a function specifying how to handle NAs Default is na delete in Hmisc number of bootstrap samples default 100 areg the default or avas number of knots for continuous variables not restricted to be linear Default is 4 One or two is not allowed nk 0 forces linearity for all continuous variables number of equally spaced points at which to evaluate and save the nonparamet
454. ts TRUE middle bold FALSE outer size Scriptsize caption rowlabel insert bottom TRUE dcolumn FALSE formatArgs NULL round NULL prtest c P stat df name prmsd FALSE msdsize NULL long dotchart pdig 3 eps 0 001 auxCol NULL dotchart FALSE S3 method for class summary formula cross print x twoway nvar 2 prnmiss any statsS Missing gt 0 prn TRU abbreviate dimnames FALSE prefix width max nchar v min colwidth formatArgs NULL GI S3 method for class summary formula cross latex object title first word deparse substitute object twoway nvar 2 prnmiss TRUE prn TRUE caption attr object heading vnames c labels names rowlabel stratify na group FALSE shortlabel TRU Gl 272 summary formula S3 method for class summary formula cross formula x cumcategory y Arguments formula x y drop data subset na action fun method An S formula with additive effects For method response or cross the dependent variable has the usual connotation For method reverse the dependent variable is what is usually thought of as an independent variable and it is one that is used to stratify all of the right hand side variables For method response only the formula may contain one or more in vocations of the stratify
455. ts and numbers of trials num lt c 10 NA 20 0 15 data are 10 12 NA 999 20 20 0 25 15 35 denom lt c 12 999 20 25 35 wW lt num denom setup num denom w attach my data frame wSsubs 326 Xy group xy group Mean x vs function of y in groups of x Description Compute mean x vs a function of y e g median by quantile groups of x or by x grouped to have a given average number of observations Deletes NAs in x and y before doing computations Usage xy group x y m 150 g fun mean result list Arguments x a vector may contain NAs y a vector of same length as x may contain NAs m number of observations per group g number of quantile groups fun function of y such as median or mean the default result list the default or matrix Value if result list a list with components x and y suitable for plotting if result matrix matrix with rows corresponding to x groups and columns named n x and y See Also cuta tapply Examples plot xy group x y g 10 Plot mean y by deciles of x xy group x y m 100 result matrix Print table 100 obs group xYplot 327 xYplot xyplot and dotplot with Matrix Variables to Plot Error Bars and Bands Description A utility function Cbind returns the first argument as a vector and combines all other argu ments into a matrix stored as an attribute called other The arguments can be named e g Chind pressure y ylow yhigh
456. turn a list containing all the original values of x in cluding duplicates and without sorting and the smoothed y values correspond ing to them Set type ordered all tosortby x andtype evaluate to evaluate the smooth only at evaluation equally spaced points between the observed limits of x y a numeric vector the same length as x robust span degree cell valuation see loess smooth The default is linear degree 1 and 100 points to evaluation if type evaluate Details The functions correctly combine weights of observations having duplicate values of x before com puting estimates When normwt FALSE the weighted variance will not equal the unweighted variance even if the weights are identical That is because of the subtraction of from the sum of the weights in the denominator of the variance formula If you want the weighted variance to equal the unweighted variance when weights do not vary use normwt TRUE The articles by Gatz and Smith discuss alternative approaches to arrive at estimators of the standard error of a weighted mean wtd rank does not handle NAs as elegantly as rank if weights is specified Value wtd mean and wtd var return scalars wtd quantile returns a vector the same length as probs wtd Ecdf returns a list whose elements x and Ecdf correspond to unique sorted values of x If the first CDF estimate is greater than zero a point min x 0 is placed at the beginning of the estimates See abo
457. uantile2 function allows the user to specify the intervention control hazard ratio as a function of time the probability of a control subject actually receiving the intervention dropin as a function of time and the probability that an intervention subject receives only the control agent as a function of time non compliance dropout Quantile2 returns a function that generates either control or intervention uncensored survival times subject to non constant treatment effect dropin and dropout There is a plot method for plotting the results of Quantile2 which will aid in understanding the effects of the two types of non compliance and non constant treatment effects Quant ile2 assumes that the hazard function for either treatment group is a mixture of the control and intervention hazard functions with mixing proportions defined by the dropin and dropout probabilities It computes hazards and survival distributions by numerical differentiation and integration using a grid of by default 7500 equally spaced time points The logrank function is intended to be used with spower but it can be used by itself It returns the 1 degree of freedom chi square statistic with the hazard ratio estimate as an attribute spower 251 The Weibu112 function accepts as input two vectors one containing two times and one containing two survival probabilities and it solves for the scale and shape parameters of the Weibull distribu tion S t exp alpha t ga
458. ult 1 point cex mult var NA extra points no mult rep NA length subset c legend plot FALSE legend location o legend titl titl legend titl cex 3 legend titl line 1 82 event chart legend point at list x c 5 95 y c 95 30 legend point pch point pch legend point text ifelse rep is data frame data length subset c names data subset c subset c legend cex 2 5 legend bty n legend line at list x c 5 95 y c 20 5 legend line text names table as character data line by exclude c NA legend line lwd line lwd legend loc num 1 Arguments data a matrix or data frame with rows corresponding to subjects and columns cor responding to variables Note that for a data frame or matrix containing multi ple time to event data e g time to recurrence time to death and time to last follow up one column is required for each specific event subset r subset of rows of original matrix or data frame to place in event chart Logi cal arguments may be used here e g treatment arm a if the data frame data has been attached to the search directory otherwise dataStreatment arm a subset c subset of columns of original matrix or data frame to place in event chart if working with a data frame a vector of data frame variable names may be used for subsetting purposes e g c randdate eventl sort by column
459. umber of items equal to number of columns of input x 104 rdec cdec cdot na blank dcolumn numeric dollar format df a vector specifying the number of decimal places to the right for each row cdec is more commonly used than rdec A vector rdec must have number of items equal to number of rows of input x rdec is expanded to matrix dec a vector specifying the number of decimal places for each column The vector must have number of items equal to number of columns or components of input Xx Set to TRUE to use centered dots rather than ordinary periods in numbers The output uses a syntax appropriate for latex Set to TRUE to use blanks rather than NA for missing values This usually looks better in latex Set to TRUE to use David Carlisle s dcolumn style for decimal alignment in latex Default is FALSE You will probably want to use dcolumn if you use rdec as a column may then contain varying number of places to the right of the decimal dcolumn can line up all such numbers on the decimal point with integer values right justified at the decimal point location of num bers that actually contain decimal places When you use dcolumn TRUE numeric dollar is set by default to FALSE When you use dcolumn TRUE the object attribute style set to dcolumn as the latex usepackage must reference dcolumn The three files dcolumn sty newarray sty and array sty will
460. umeric matrix hi if specified the first PC is scaled so that its maximum value is hi and its mini mum value is zero Value The vector of observations with the first PC An attribute coef is attached to this vector coef contains the raw variable coefficients Author s Frank Harrell See Also prcomp Examples set seed 1 xl lt rnorm 100 x2 lt x1 rnorm 100 w lt pel cbhind x1 x2 attr w coef 186 plotCorrPrecision plotCorrPrecision Plot Precision of Estimate of Pearson Correlation Coefficient Description This function plots the precision margin of error of the product moment linear correlation coef ficient r vs sample size for a given vector of correlation coefficients rho Precision is defined as the larger of the upper confidence limit minus rho and rho minus the lower confidence limit labcurve is used to automatically label the curves Usage plotCorrPrecision rho c 0 0 5 n seq 10 400 length 100 conf int 0 95 offset 0 025 Arguments rho single or vector of true correlations A worst case precision graph results from rho 0 n vector of sample sizes to use on the x axis conf int confidence coefficient default uses 0 95 confidence limits offset see labcurve other arguments to Labcurve Author s Xing Wang and Frank Harrell See Also YOOrr COE COr Lest Examples plotCorrPrecision plotCorrPrecision rho 0 plsmo 187 pismo Plot smoothed estimates
461. umn with bystats fun nmiss subset 31 a missing value causes the entire row to be considered missing and the row is not passed to fun For bystats one or more classifcation variables separated by commas For print bystats options passed to print default suchas digits For latex bystats and latex bystats2 options passed to latex default such as digits If you pass cdec to latex default keep in mind that the first one or two positions depending on nmiss should have zeros since these correspond with frequency counts vertical variable for bystats2 Will be converted to factor horizontal variable for bystats2 Will be converted to factor a function to compute on the non missing y for a given subset You must specify fun in front of the function name or definition fun may return a single num ber or a vector or matrix of any length Matrix results are rolled out into a vector with names preserved When y is a matrix a common fun is function y apply y 2 ff where ff is the name of a function which operates on one column of y A column containing a count of missing values is included if nmiss TRUE or if there is at least one missing value a vector of subscripts or logical values indicating the subset of data to analyze abbreviate dimnames set to TRUE to abbreviate dimnames in output prefix width see print char matrix if using S Plus title caption rowlabel x object Value title to
462. uous or binary Set categorical x to treat all variables as categorical a character vector of names of variables that are not to be transformed For these variables the guts of lm fit qr is used to impute missing values You may want to treat binary variables asis this is automatic if using a formula If imputed TRUE you may want to use categorical for binary variables if you want to force imputed values to be one of the original data values Setasis x to treat all variables asis number of knots to use in expanding each continuous variable not listed in asis ina restricted cubic spline function Default is 3 yielding 2 parameters for a variable if n lt 30 4 if 30 lt n lt 100 and 5 if n gt 100 4 parameters Set to TRUE to return a list containing imputed values on the original scale If the transformation for a variable is non monotonic imputed values are not unique transcan uses the approx function which returns the highest value of the variable with the transformed score equalling the imputed score imputed TRUE also causes original scale imputed values to be shown as tick marks on the top margin of each graph when show na TRUE for the final iteration only For categorical predictors these imputed values are passed through the jitter function so that their frequencies can be visualized When n impute is used each NA will have n impute tick marks number of multiple imputation
463. us on a particular area of the event history graph zooming can be performed This is best done by specifying appropriate xl im and ylim arguments at the end of the event history function call taking advantage of the argument link to the plot function An example of zoom ing can be seen in Plate 4 of the paper referenced below Please read the reference below to understand how the individual covariate and survival information is provided in the plot how ties are handled how right censoring is handled etc WARNING This function has been tested thoroughly but only within a restricted version and environment i e only within S Plus 2000 Version 3 and within S Plus 6 0 version 2 both on a Windows 2000 machine Hence we cannot currently vouch for the function s effectiveness in other versions of S Plus e g S Plus 3 4 nor in other operating environments e g Windows 95 Linux or Unix The function has also been verified to work on R under Linux Note The authors have found better control of the use of color by producing the graphs via the postscript plotting device in S Plus In fact the provided examples utilize the postscript function How ever your past experiences may be different and you may prefer to control color directly to the graphsheet in Windows environment for example The event history function will work with either approach 96 Author s Joel Dubin jdubin uwaterloo ca References event
464. use the system dvips command to print the dvi file to the default system printer or create a postscript file if file is specified dvigv uses the system dvips command to convert the input object to a dvi file and uses the system dvips command to convert it to postscript Then the postscript file is displayed using Ghostview assumed to be the system command gv There are show methods for displaying typeset LaTeX on the screen using the system xdvi command If you show a LaTeX file created by latex without running it through dvi using show dvi object the show method will run it through dvi automatically These show methods are not S Version 4 methods so you have to use full names such as show dvi and show latex Use the print methods for more automatic display of typesetting e g typing latex x will invoke xdvi to view the typeset document Usage latex object title first word deparse substitute object Default S3 method latex object title first word deparse substitute object file paste title tex sep append FALSE label title rowlabel title rowlabel just 1 cgroup NULL n cgroup NULL rgroup NULL n rgroup NULL cgroupTexCmd bfseries rgroupTexCmd bfseries rownamesTexCmd NULL colnamesTexCmd NULL cellTexCmds NULL rowname cgroup just rep c length n cgroup colheads NULL extracolheads NULL extracolsize scriptsize dcolumn FALSE numeric dollar dcolumn cdot FALSE longtable
465. used and with the mean drawn with a solid dot This schematic plot is rendered nicely in postscript with an image height of 3 5 inches Usage panel bpplot x y box ratio 1l means TRUE qref c 5 25 75 probs c 05 125 25 375 nout 0 datadensity FALSE scatld opts NULL font box dot font pch box dotS pch cex box dotScex col box dotScol E g bwplot formula panel panel bpplot panel bpplot parameters 182 bpplt stats qref c 5 25 75 pch 16 grid FALSE Arguments x Yy box ratio means qref probs nout datadensity scatld opts font pch cex col stats xlim xlab gomit cex labels cex points grid panel bpplot Ei xlab box ratio 1 means TRU gomit c 025 975 cex labels par cex xlim continuous variable whose distribution is to be examined grouping variable see panel bwplot set to FALSE to suppress drawing a character at the mean value vector of quantiles for which to draw reference lines These do not need to be included in probs vector of quantiles to display in the box plot These should all be less than 0 5 the mirror image quantiles are added automatically By default probs is set to c 05 125 25 375 so that intervals contain 0 9 0 75 0 5 and 0 25 of the data To draw all 99 percentiles i e to draw a box percentile plot set probs seq 01 49 by 01 To make a more traditional box plot use probs 25
466. variable is unknown t linear defaults to TRUE to limit the variance in predicted values when nk is positive Value a list of class aregImpute containing the following elements call the function call expression formula the formula specified to aregImpute match the match argument fweighted the fweighted argument n total number of observations in input dataset p number of variables na list of subscripts of observations for which values were originally missing nna named vector containing the numbers of missing values in the data type vector of types of transformations used for each variable s 1 c for smooth spline linear or categorical with dummy variables tlinear value of tlinear parameter nk number of knots used for smooth transformations cat levels list containing character vectors specifying the levels of categorical variables df degrees of freedom number of parameters estimated for each variable n impute number of multiple imputations per missing value imputed a list containing matrices of imputed values in the same format as those cre ated by transcan Categorical variables are coded using their integer codes Variables having no missing values will have NULL matrices in the list X if x is TRUE the original data matrix with integer codes for categorical variables rsq for the last round of imputations a vector containing the R squares with which each sometimes missing variable could be predicted
467. variance covariance matrix fit mult impute can also use the object created by the mice function in the MICE library to draw the multiple imputations as well as objects created by aregImpute The summary method for t ranscan prints the function call R squares achieved in transforming each variable and for each variable the coefficients of all other transformed variables that are used to estimate the transformation of the initial variable If imputed TRUE was used in the call to transcan also uses the describe function to print a summary of imputed values If long TRUE also prints all imputed values with observation identifiers There is also a simple function print transcan which merely prints the transformation matrix and the function call It has an optional argument long which if set to TRUE causes detailed parameters to be printed Instead of plotting while t ranscan is running you can plot the final transformations after the fact using plot transcan if the optiontrantab TRUE was specified to transcan If in addition the option imputed TRUE was specified to transcan plot transcan will show the location of imputed values including multiples along the axes impute does imputations for a selected original data variable on the original scale if imputed TRUE was given to transcan If you do not specify a variable to impute it will do impu tations for all variables given to t ranscan which had at least one missing va
468. vation unused This function assumes that you have stored the 1d98 executable in a subdirectory that is in your system path Obtain 1d98 program from the URL given in the reference below The plot method makes two plots if power is specified the second containing exit and cumulative exit probabilities as a function of time If par mfrow c is not already set to something besides c 1 1 par mfrow c 2 1 is set temporarily IdBands Value 157 A list of class 1dBands with the following components When summary is used and n and either pbar or sd are given summary returns the same object returned by 1dBands but with possible components diff lower diff lower needed difference means or proportions and or lower or upper needed odds ratios data a data frame containing the main calculations power power specified if any drift drift calculated if power specified type boundaries if power not specified power otherwise n number of interim analyses for 1dBands For summary is the number of observations per arm and it must be a vector with length equal to the number of looks if pbar or sd are given alpha input value of alpha sided 1 3 alphaLower lower alphaUpper upper a spending name of a spending function used phi parameter for spending function spending2 name of spending function for upper boundary Defaults to spending phi2 parameter for second spending function Defaults to phi but is ignored if spen
469. ve for wtd table wtd rank returns a vector the same length as x after removal of NAs depending on na rm See above for wtd loess noiter wtd stats 325 Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu References Research Triangle Institute 1995 SUDAAN User s Manual Release 6 40 pp 8 16 to 8 17 Gatz DF Smith L 1995 The standard error of a weighted mean concentration I Bootstrapping vs other methods Atmospheric Env 11 1185 1193 Gatz DF Smith L 1995 The standard error of a weighted mean concentration II Estimating confidence intervals Atmospheric Env 29 1195 1200 See Also mean var quantile table rank loess smooth lowess plsmo Ecdf somers2 describe Examples set seed 1 x lt runif 500 wts lt sample 1 6 500 TRUE std dev lt sqrt wtd var x wts wtd quantile x wts death lt sample 0 1 500 TRUE plot wtd loess noiter x death wts type evaluate describe x weights wts describe uses wtd mean wtd quantile wtd table xg lt cut2 x g 4 table xg wtd table xg wts type table Here is a method for getting stratified weighted means y lt runif 500 g lt function y wtd mean y 1 y 2 summarize cbind y wts llist xg g stat name y Restructure data to generate a dichotomous response variable from records containing numbers of even
470. wait for a mouse click if the matrix requires more than one page Categorical variables character variables and variables having fewer than a set number of unique values are ignored If par mfrow is not set up before Ecdf data frame is called the function will try to fig ure the best layout depending on the number of variables in the data frame Upon return the original mf row is left intact When the first argument to Ecd f is a formula a Trellis Lattice function Ecdf formula is called This allows for multi panel conditioning superposition using a groups variable and other Trellis features along with the ability example if fun qnorm the to easily plot transformed ECDFs using the fun argument For inverse normal transformation will be used for the y axis If the transformed curves are linear this indicates normality Like the xYplot function Ecdf will create a function Key if the groups variable is used This function can be invoked by the user to define the keys for the groups Ecdf Usage ECAC Xo isare col 1 73 Default S3 method Ecdf x what c F 1 F xlab ylab q pl TRUE add FALSE Ilty 1 weights normwt FALSE group rep 1 length x label curves TRUE xlim subtitles TRUE datadensity c none rug hist density side 1 frac switch datadensity none NA rug 03 hist 1 density 1 dens opts NULL lwd S3 me
471. wess 42 44 186 223 322 cum mhgr 165 lrm 206 lrm fit 206 fit 303 keNstr 156 mApply 157 mapply 158 ma tch 333 tch mChoice mChoice 158 tchCases find matches 95 th mChoice mChoice 158 trix 214 trix2dataFrame 158 trix2dataFrame summarize 261 txv Misc 168 max 238 mC Bt 333538 at hoice 158 275 mdb get 162 ean transace 283 mean 322 ean areg boot transace 283 gp axis 163 gp axis labels 196 hgr 165 ce 14 303 inor tick 167 sc 168 model frame default 63 174 176 model matrix 3 8 monotone transace 283 monthDays yearDays 332 mtext 173 mtitle 172 na na na na na na delete 63 173 175 176 detail response 63 174 174 176 include 128 keep 63 174 176 omit 174 176 pattern 318 344 na pattern varclus 314 na retain summary formula 266 naclus 14 50 303 naclus varclus 314 names 34 50 314 naplot 14 303 naplot varclus 314 naprint 63 174 176 naresid 174 176 nchar 74 258 259 nomiss Misc 168 nomogram 289 ns 205 208 nstr 177 num denom setup wtd stats 319 numeric string sedit 239 numericScale xYplot 324 ols 3 289 Ops mChoice mChoice 158 outer 214 outerText Misc 168 page 50 panel abline 329 panel bpplot 27 178 panel bwplot 178 180 panel Dotplot xYplot 324 panel Ecdf Ecdf 69 panel plsmo pl
472. whites nonwhite reduction n n pow lt ciapower 5 75 n 25 n i187 aAa 15 nonwhite reduction 1 5 5 cat n nPower format pow n cnvrt coords Convert between the 5 different coordinate sytems on a graphical de vice Description Takes a set of coordinates in any of the 5 coordinate systems usr plt fig dev or tdev and returns the same points in all 5 coordinate systems Usage cnvrt coords x y NULL input c usr plt fig dev tdev cnvrt coords 35 Arguments x Vector Matrix or list of x coordinates or x and y coordinates NA s allowed y y coordinates if x is a vector NA s allowed input Character scalar indicating the coordinate system of the input points Details Every plot has 5 coordinate systems usr User the coordinate system of the data this is shown by the tick marks and axis labels plt Plot Plot area coordinates range from 0 to 1 with O corresponding to the x and y axes and 1 corresponding to the top and right of the plot area Margins of the plot correspond to plot coordinates less than 0 or greater than 1 fig Figure Figure area coordinates range from 0 to 1 with 0 corresponding to the bottom and left edges of the figure including margins label areas and corresponds to the top and right edges fig and dev coordinates will be identical if there is only 1 figure area on the device layout mfrow or mfcol has not been used dev Device
473. widths which were used to draw the curves This is only used when keys are drawn See lt y also vector of integer color numbers for use in curve labels symbols lines and leg ends Default is par col for all curves See 1ty also Default is TRUE to make key draw transparent legends i e to suppress drawing a solid rectangle background for the legend Set to FALSE otherwise factor by which to multiply default arrow lengths When keys is a vector of integers point inc specifies the x increment be tween the point symbols that are overlaid periodically on the curves By default point inc is equal to the range for the x axis divided by 5 an optional list which can be used to specify any of the options to labcurve with the usual element name abbreviations allowed This is useful when Labcurve is being called from another function Example opt s list method arrow cex 8 np 200 For drawPlot a list of labcurve options to pass as labcurve opts a list of extra arguments you wish to pass to key e g key opt s list background 1 between 3 The argument names must be spelled out in full These two arguments are passed to the largest empty function s method and numbins arguments see below For largest empty specifies the number of bins in which to discretize both the x and y directions for searching for rectangles Default is 25 set to TRUE or specify add to cause the curves in curves to be dra
474. will be described Logical flag determining whether a description of attributes will be printed Front material of a line for internal use Fill character used for each level of indentation Name of object for internal use deparsed version of struct by default Logical flag should the size of the object in bytes be printed A description of the structure of struct will be printed in outline form with in dentation for each level of recursion showing the internal storage mode length class es if any attributes and first few elements of each data vector By default each level of list recursion is indicated by a and attributes by A Alan Zaslavsky zaslavsk hcp med harvard edu See Also SCE Examples Xx lt list a ordered c 1 30 30 1 b c Rick John Allan c diag 300 e cbind p 1008 1019 q 4 list tree X In R you can say str X makeNstr creates a string that is a repeat of a substring Description Takes a character and creates a string that is the character repeated len times Usage makeNstr char len 160 mApply Arguments char character to be repeated len number of times to repeat char Value A string that is char repeated len times Author s Charles Dupont See Also paste rep Examples makeNstr 5 mApply Apply a Function to Rows of a Matrix or Vector Description mApp ly is like tapply except that the first argument can be a matrix
475. witch type proportion Proportion count Frequency density Density y NULL curve NULL add FALSE bottom align type density col par col lwd par lwd grid FALSE Fl Arguments x a vector of numeric data or a data frame for jitter2 object a data frame or list even with unequal number of observations per variable as long as group is not specified side axis side to use 1 bottom default for hist Spike 2 left 3 top default for scat1d 4 right frac fraction of smaller of vertical and horizontal axes for tick mark lengths Can be negative to move tick marks outside of plot For hist Spike this is the relative length to be used for the largest frequency When scat1d calls histSpike it multiplies its frac argument by 2 5 jitfrac fraction of axis for jittering If lt 0 no jittering is done If preserve TRUE the amount of jittering is independent of jitfrac tfrac fraction of tick mark to actually draw If t frac lt 1 will draw a random fraction t frac of the line segment at each point This is useful for very large samples or ones with some very dense points The default value is 1 if the number of non missing observations n is less than 125 and max 1 125 n otherwise eps fraction of axis for determining overlapping points in x For preserve TRUE the default is 0 and original unique values are retained bigger values of eps tends to bias observations from dense to sparse regions bu
476. wn under the control of type 1lty lwd col parameters defined either in the curves lists or in the separate arguments given to Labcurve or through opts For largest empty set pl1 TRUE to show the rectangle the function found by drawing it with a solid color By default when curves are actually drawn by labcurve anew plot is started To add to an existing plot set add TRUE When a plot has already been started yl im defaults to par usr 3 4 When p1 TRUE ylim and xlim are determined from the ranges of the data Specify ylim yourself to take control of the plot construction In some cases it is advisable to make y 1im larger than usual to allow for automatically positioned keys For largest empty ylim specifies the limits on the y axis to limit the search for rectangle Here y1im defaults to the same as above i e the range of the y axis of an open plot from par For drawPlot the default is c 0 1 x axis and y axis labels when p1 TRUE and add FALSE or for drawP lot Defaults to unless the first curve has names for its first two elements in which case the names of these elements are taken as x Lab and ylab integer vector corresponding to curves specifying which curves are to be la belled or have a legend 138 labcurve grid set to TRUE if the R grid package was used to draw the current plot This prevents labcurve from using par usr etc If using R grid you can pass coordinates and lengths having arbit
477. x and max x For groups containing no fewer than three unique x values clustering is done on the trio of values min x max x and the longest gap between any successive x Then within sample size and x distribution strata clustering of time response profiles is based on p values of y all evaluated at the same p equally spaced x s within the stratum An option allows per curve data to be smoothed with lowess before proceeding Outer x values are taken as extremes of x across all curves within the stratum Linear interpolation within curves is used to estimate y at the grid of x s For curves within the stratum that do not extend to the most extreme x values in that stratum extrapolation uses flat lines from the observed extremes in the curve unless ext rap TRUE The p y values are clustered using clara print and plot methods show results By specifying an auxiliary idcol variable to plot other variables such as treatment may be depicted to allow the analyst to determine for example 46 curveRep whether subjects on different treatments are assigned to different time response profiles To write the frequencies of a variable such as treatment in the upper left corner of each panel instead of the grand total number of clusters in that panel specify freq curveSmooth takes a set of curves and smooths them using lowess If the number of unique x points in a curve is less than p the smooth is evaluated at the unique x values Otherwis
478. x is the same as specifying old S value a character vector 244 sedit Value sedit returns a vector of character strings the same length as text substring location returns a list with components named first and last each specifying a vector of character positions corresponding to matches replace substring wild returns a single character string numeric string and all digits return a single logical value Side Effects substring2 lt modifies its first argument Author s Frank Harrell Department of Biostatistics Vanderbilt University School of Medicine f harrell vanderbilt edu See Also grep substring Examples x lt this string substring2 x 3 4 lt IS x substring2 x 7 lt x substring location abcdefgabc ab substring location abcdefgabc ab restrict c 3 999 replace substring wild this is a cat thisx cat thatxdog replace substring wild there is a cat is ax is not ax replace substring wild this is a cat is ax Z qualify lt function x x 1 5 x 2 5 1 replace substring wild He won 1 5 million won million lost million test qualify replace substring wild He won 1 million won million lost million test qualify replace substring wild He won 1 2 million won million lost million test numeric string x lt c a b c lt d hello sedit x c hexo c hex sedit x
479. x rep NROW x NCOL x nrow NROW x and then selectively change appropriate elements of the matrix Note that you might need to set numeric dollar FALSE to disable math mode for some effects to work See also cgroupTexCmd above regarding multiple effects Set to TRUE to use blanks rather than NA for missing values This usually looks better in Latex insert bottom an optional character string to typeset at the bottom of the table For ctable style tables this is placed in an unmarked footnote first hline double rowname cgroup just colheads set to FALSE to use single horizontal rules for styles other than bookmark or ctable rownames for t abular environment Default is rownames of matrix or data frame Specify rowname NULL to suppress the use of row names justification for labels for column groups Defaults to c a character vector of column headings if you don t want to use dimnames object 2 Specify colheads NULL to suppress column headings extracolheads extracolsize dcolumn an optional vector of extra column headings that will appear under the main headings e g sample sizes This character vector does not need to include an empty space for any rowname in effect as this will be added automatically You can also form subheadings by splitting character strings defining the column headings using the usual backslash n newline character size for ext racolheads or for any
480. y measure the proportion of observations for which two variables are both positive similarity ccbothpos uses a chance corrected mea sure which is the proportion of observations for which both variables are positive minus the product of the two marginal proportions This difference is expected to be zero under independence For diagonals ccbothpos still uses the proportion of positives for the single variable So ccbothpos is not re ally a similarity measure and clustering is not done This measure is useful for plotting with plotMult Sim see the last example if x is not a formula it may be a data matrix or a similarity matrix By default it is assumed to be a data matrix see hclust The default for both varclus and naclus is compact for R itis complete These may be specified if x is a formula The default na actionisna retain defined by varclus This causes all observations to be kept in the model frame with later pairwise deletion of NAs for varclus these are optional arguments to pass to the dataframeReduce function Otherwise passed to plclust or to dotchart or dotchart2 for naplot y axis label Default is constructed on the basis of similarity set to TRUE to plot a legend defining the abbreviations a list with elements x and y defining coordinates of the upper left corner of the legend Default is Locator 1 if a legend is plotted describing abbreviations original labels longer than maxlen cha
481. y the original order of levels in component factor variables if there were any input variables that were factors Set add none to TRUE to make a new category none if it doesn t already exist and if there is an observations with no choices selected set drop FALSE to keep unused factor levels as columns of the matrix pro duced by mChoice an object of class mchoice such as that created by mChoice Foris mChoice is any object an object of class mchoice such as that created by mChoice maximum number of combos With of a line of text to be formated quote the output max levels to be displayed By default no abbreviation of levels is done in format and summary Specify a positive integer to use abbreviation in those functions See abbreviate character to use to separate levels when formatting set to FALSE to keep print summary mChoice from printing the variable label and number of unique values a scalar or vector If values is integer it is the choice codes and if it is a character vector it is assumed to be value labels mChoice returns a character vector of class mChoice plus attributes levels and label summary mChoice returns an object of class Summary mChoice inmChoice returns a logical vector format mChoice returns a character vector and as double mChoice re turns a binary numeric matrix Author s Frank Harrell Department of Biostatistics Vanderbilt University f harrell van
482. y variables and nonlinear spline components as separate variables This will perform a redundancy analysis on pieces of the variables if iterms TRUE you can set pc to TRUE to replace the submatrix of terms corresponding to each variable with the orthogonal principal components before doing the redundancy analysis The components are based on the correlation matrix set to TRUE to monitor progress of the stepwise algorithm arguments to pass to dataframeReduce to remove difficult variables from data if formula is to use all variables in data data must be specified when these arguments are used Ignored for print an object created by redun number of digits to which to round R values when printing set to FALSE to prevent the print method from printing the R history and the original R with which each variable can be predicted from ALL other variables A categorical variable is deemed redundant if a linear combination of dummy variables representing it can be predicted from a linear combination of other variables For example if there were 4 cities in the data and each city s rainfall was also present as a variable with virtually the same rainfall reported for all observations for a city city would be redundant given rainfall or vice versa the one declared redundant would be the first one in the formula If two cities had the same rainfall city might be declared redundant even though tied cities might be deemed non
483. y was specified with legend location o orlegend location i this argument will dictate the location of the upper left and lower right comers of legend area to be utilized for describing the different line by val ues e g treatment arm The default is setup for legend location oO line text text to be used for describing 1ine by values the default are the names of the unique non missing 1ine by values as produced from the table function 88 event chart legend line lwd vector of line widths corresponding to line by values legend loc num number used for locator argument when legend locator 1 If 1 de fault user is to locate only the top left corner of the legend box If 2 user is to locate both the top left corner and the lower right corner This will be done twice when line by is specified once for points and once for lines additional par arguments for use in main plot Details if you want to put say two eventcharts side by side in a plot region you should not set up par mfrow c 1 2 before running the first plot Instead you should add the argument mfg c 1 1 1 2 to the first plot call followed by the argument mfg c 1 2 1 2 to the second plot call if dates in original data frame are in a specialized form eg mm dd yy of mode CHARACTER the user must convert those columns to become class dates or julian numeric mode see Date for more information For example in a data frame call
484. y y age groups ascites fun g par mfrow c 1 2 oma c 3 0 3 0 allow outer margins for overall for ivar in 1 2 title sub lt 1 3 ivar 1 gt 3 3 number of quantiles var plot s3 which isub main xlab c Cholesterol Bilirubin ivar pch c 91 16 93 Closed circle mtext paste Quartiles of label y adj 5 outer TRUE cex 1 75 Overall outer title priatex latex s3 trios TRUE trios gt collapse 3 quartiles Summarize only bilirubin but do it with two statistics the mean and the median ake separate tables for the two randomized groups and make plots for the active arm g lt function y c Mean mean y Median median y for sub in c D penicillamine placebo ss lt summary bili age groups ascites chol fun g subset drug sub cat n sub n n print ss if sub D penicillamine par mfrow c 1 1 plot s4 which 1 2 dotfont c 1 1 subtitles FALSE main 1 mean 2 median 1 font open circle title sub Closed circle mean Open circle median adj 0 title sub sub adj 1 w lt latex ss append TRUE fi my tex label if sub placebo s4b else s4a caption paste label bili em sub sep Note symbolic labels for tables for two subsets s4a s4b priatex w Now consider examples in reverse format where the lone dependent variable tells the summary function how to stratify
485. y zero The sample mean of the residuals is constrained to be exactly zero although this does not simplify anything Value transace returns a matrix like x but containing transformed values This matrix has attributes rsq vector of R with which each variable can be predicted from the others and omitted row numbers of x that were deleted due to NAs areg boot returns alist of class areg boot containing many elements including if valrsq is TRUE rsquare app and rsquare val summary areg boot returns a list of class summary areg boot containing a matrix of results for each predictor and a vector of adjust to settings It also contains the call and a label for the statistic that was computed A print method for these objects handles the printing predict areg boot returns a vector unless statistic terms in which case it returns a matrix Function areg boot returns by default a list of functions whose argument is one of the variables on the original scale and whose returned values are the corresponding transformed values The names of the list of functions corre spond to the names of the original variables When t ype individual Function areg boot invisibly returns the vector of names of the created function objects Mean areg boot and Quantile areg boot also return functions smearingEst returns a vector of estimates of distribution parameters of class labelled so that print labelled wil print a label documenting the esti
486. ylab rm boot a second object created by rm boot that can also be passed to plot rm boot This is used for two sample problems for which the time profiles are allowed to differ between the two groups The bootstrapped predicted y values for the sec ond fit are subtracted from the fitted values for the first fit so that the predicted mean response for group 1 minus the predicted mean response for group 2 is what is plotted The confidence bands that are plotted are also for this differ ence For the simultaneous confidence band the objective criterion is taken to be the sum of the objective criteria 2 log L or sum of squared errors for the separate fits for the two groups The times vectors must have been identical for both calls to rm boot although NAs can be inserted by the user of one or both of the time vectors in the rm boot objects so as to suppress certain sections of the difference curve from being plotted the confidence level to use in constructing simultaneous and optionally point wise bands Default is 0 95 label for y axis Default is the label attribute of the original y variable or y if no label was assigned to y using the 1abe1 function for example individual boot pointwise band set to TRUE to plot the first 100 bootstrap regression fits set to TRUE to draw a pointwise confidence band in addition to the simultaneous band curves in simultaneous band set to TRUE to draw all bootstrap regressi
487. you may also specify that a variable is to remain untransformed by enclosing its name with the iden tify function e g I x3 The user may add other variable names to the asis and categorical vectors For invert Tabulated x is a vector or a list with three components the x vector the corresponding vector of transformed values and the corresponding vector of frequencies of the pair of original and transformed variables For print plot impute and predict x is an object created by transcan any S model formula any S or Design modeling function not in quotes that computes a vector of coefficients and for which Varcov will return a variance covariance ma xtrans method categorical asis nk imputed n impute boot method trantab transformed transcan trix E g fitter lm glm ols At present models involving non regression parameters e g scale parameters in parametric survival models are not handled fully an object created by transcan aregImpute or Mice use method canonical or any abbreviation thereof to use canoni cal variates the default method pc transforms a variable instead so as to maximize the correlation with the first principal component of the other variables a character vector of names of variables in x which are categorical for which the ordering of re scored values is not necessarily preserved If categorical is omitted it is assumed that all variables are contin
488. ze for 2 sample Binomial Description Computes sample size s for 2 sample binomial problem given vector or scalar probabilities in the two groups Usage samplesize bin alpha beta pit pic rho 0 5 Arguments alpha scalar ONE SIDED test size or two sided size 2 beta scalar or vector of powers pit hypothesized treatment probability of success pic hypothesized control probability of success rho proportion of the sample devoted to treated group 0 lt rho lt 1 230 sasxport get Value TOTAL sample size s AUTHOR Rick Chappell Dept of Statistics and Human Oncology University of Wisconsin at Madison chappell stat wisc edu Examples alpha lt 05 beta lt c 70 80 90 95 N1 is a matrix of total sample sizes whose rows vary by hypothesized treatment success probability and columns vary by power See Meinert s book for formulae SE OSE SE te N1 lt samplesize bin alpha beta pit 55 pic 5 N1 lt rbind Nl1 samplesize bin alpha beta pit 60 pic 5 N1 lt rbind Nl1 samplesize bin alpha beta pit 65 pic 5 N1 lt rbind N1l samplesize bin alpha beta pit 70 pic 5 attr N1l dimnames lt NULL Accounting for 5 noncompliance in the treated group inflation lt 1 95 2 print round Nl inflation 5 0 sasxport get Enhanced Importing of SAS Transport Files using read xport Description Uses the read xport and lookup xport functions in the f

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