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Package `astrochron`

1. run simulations with rho 7 and scaling noise to have 50 of the etp model variance arletp etpdat ex1 rho 0 7 wtAR sqrt 0 5 arcsinT Arcsine Transformation of Stratigraphic Series Description Arcsine transformation of stratigraphic series Usage arcsinT dat genplot T verbose T Arguments dat Stratigraphic series for arcsine transformation First column should be location e g depth second column should be data value for transformation genplot Generate summary plots T or F verbose Verbose output T or F See Also demean detrend divTrend logT prewhiteAR and prewhiteAR1 armaGen 9 armaGen Generate Autoregressive Moving average Model Description Generate an Autoregressive Moving average time series model Usage armaGen npts 1024 dt 1 m 0 std 1 rhos c 0 9 thetas c 0 genplot T verbose T Arguments npts Number of time series data points dt Sampling interval m Mean value of final time series std Standard deviation of final time series rhos Vector of AR coefficients for each order thetas Vector of MA coefficients for each order genplot Generate summary plots T or F verbose Verbose output T or F asm Average Spectral Misfit Description Calculate Average Spectral Misfit with Monte Carlo spectra simulations as updated in Meyers et al 2012 Usage asm freq target fper NULL rayleigh nyquist sedmin 1 sedmax 5 numsed 50 linLog 1 iter 100000 output F genpl
2. Arguments dat srstart srend genplot verbose Value Time series First column should be time in ka second column should be data value Initial sedimentation rate in m ka Final sedimentation rate in m ka Generate summary plots T or F Verbose output T or F modeled stratigraphic series 60 sortNave Examples generate example series with 3 precession terms using function cycles then convert from time to space using sedimentation rate that increases from 1 to 7 cm ka ex sedRamp cycles srstart 0 01 srend 0 07 sedrate2time Integrate sedimentation rate curve to obtain time space map Description Integrate sedimentation rate curve to obtain time space map Usage sedrate2time sedrates timedir 1 genplot T verbose T Arguments sedrates Data frame containing depth height in first column meters and sedimentation rates in second column cm ka timedir Floating time scale direction 1 time increases with depth height 2 time de creases with depth height genplot Generate summary plots T or F verbose Verbose output T or F sortNave Remove Missing Entries Sort Data Average Duplicates Description Sort and average duplicates in stratigraphic series as performed in read function Usage sortNave dat sortDecr F ave T genplot T verbose T Arguments dat Stratigraphic series for processing First column should be location e g depth second column should be
3. xlab Frequency cycles ka main Comparison of rectangle black 1pi DPSS green and 3pi DPSS red taper cex main 1 lines freqL 1 log pwr_dpss1L 1 col green lines freqL 1 log pwr_dpss3L 1 col red lwd 2 points c 1 10 1 4 1 29 1 21 1 19 1 14 1 10 1 5 1 4 1 3 rep ymax 10 cex 5 col purple D xxxxx PART 2 Now add a very small amount of red noise to the series D with lag 1 correlation 0 5 ex2 ex ex2 2 ex2 2 ar1 rho 5 dt 1 npts 500 sd 005 genplot FALSE 2 compare the original series with the series noise p1 2 plot ex type 1 1lwd 2 1ty 3 col black xlab time ka ylab signal main signal black dotted and signal noise red lines ex2 col red plot ex 11 ex2 21 ex 21 xlab time ka ylab difference main Difference between the two time series very small calculate the periodogram with no tapering applied a rectangular window res 2 periodogram ex2 output 1 padfac pad save the frequency grid and the power for plotting freq 2 res 2 1 pwr_rect 2 res 2 3 now compare with results obtained after applying four different tapers Hann cosine taper DPSS with a time bandwidth product of 1 and DPSS with a time bandwidth product of 3 pl 51 pwr_hann 2 periodogram hannTaper ex2 demean FALSE output 1 padfac pad 3 pwr_cos 2 periodogram cosTaper ex2 p 3 demean FALSE output 1 padfac pad 3 pwr_dpss1 2 periodo
4. bandpass ex flow 0 038 fhigh 0 057 win 2 p 4 hilbert transform hil_ex lt hilbert res_ex idPts Interactively Identify Points in Plot Description Interactively identify points in x y plot 32 Usage integratePower idPts dat1 dat2 NULL ptsize 1 xmin NULL xmax NULL ymin NULL ymax NULL logx F logy F plotype 1 annotate 1 output 1 verbose T Arguments dat1 dat2 ptsize xmin xmax ymin ymax logx logy plotype annotate output verbose See Also Data frame with one or two columns If one column dat2 must also be specified Data frame with one column Size of plotted points Minimum x value column 1 to plot Maximum x value column 1 to plot Minimum y value column 2 to plot Maximum y value column 2 to plot Plot x axis using logarithmic scaling T or F Plot y axis using logarithmic scaling T or F Type of plot to generate 1 points and lines 2 points 3 lines Annotate plot with text indicating coordinates O none 1 annotate above point 2 annotate below point Return identified points as a data frame 0 no 1 return x and y 2 return index x and y Verbose output T or F delPts iso trimand trimAT integratePower Determine the total power within a given bandwidth Description Determine the total power within a given bandwidth and also the ratio of this power to the total power in the spectrum or up to a specified frequency If bandwi
5. delPts 17 32 34 71 72 demean 8 18 19 37 54 55 detrend 8 18 18 19 37 54 55 divTrend 8 18 19 19 37 54 55 dpssTaper 16 19 28 30 eAsm 10 20 eAsmTrack 2 22 eha 21 23 26 33 69 70 etp 8 24 extract 24 26 flip 27 freq2sedrate 27 gausTaper 16 20 28 30 getColor 28 getLaskar 8 25 29 78 hannTaper 6 20 28 30 headn 30 hilbert 31 idPts 8 31 34 71 72 integratePower 32 iso 18 32 34 71 72 linage 35 linterp 36 logT 8 18 19 36 54 55 lowpass 13 37 46 47 54 55 65 lowspec 38 42 43 45 49 modelA 40 mtm 40 41 43 49 mtmAR 40 42 45 mtmML96 40 43 44 noKernel 13 38 46 47 54 55 65 noLow 13 38 46 47 65 pad 47 peak 48 periodogram 40 42 43 45 48 pl 51 plotEha 52 pls 53 prewhiteAR 8 13 18 19 37 38 46 47 53 55 05 prewhiteAR1 8 13 18 19 37 38 46 47 54 54 65 rankSeries 55 read 56 readMatrix 56 replQ 57 replEps 58 resample 58 rfbaseline 40 runmed 45 INDEX s 59 sedRamp 59 sedrate2time 60 sortNave 60 spec mtm 40 42 43 45 stepHeat 61 76 strats 63 surrogates 63 taner 13 65 testPrecession 66 tones 68 traceFreg 24 68 70 trackFreg 24 69 70 trim 18 32 34 71 72 trimAT 18 32 34 71 72 trough 72 tune 73 writecsv 73 writeT 74 wtMean 62 74 xplot 76 zoomlIn 77 79
6. prewhiteAR1 dat setrho NULL bias F genplot T verbose T Arguments dat Stratigraphic series for prewhitening First column should be location e g depth second column should be data value for prewhitening Series must have uniform sampling interval setrho Specified lag 1 correlation coefficient rho By default rho is calculated bias Calculate unbiased estimate of rho as in Mudelsee 2010 eq 2 45 T or F genplot Generate summary plots T or F verbose Verbose output T or F rankSeries 55 References M Mudelsee 2010 Climate Time Series Analysis Classical Statistical and Bootstrap Methods 474 pp Springer Dordrecht Netherlands See Also arcsinT bandpass demean detrend divTrend logT lowpass noKernel and prewhiteAR rankSeries Create lithofacies rank series from bed thickness data Description Create lithofacies rank series from bed thickness data Usage rankSeries dat dt genplot T verbose T Arguments dat First column should be bed thickness and second column should bed lithofacies rank dt Sampling interval for piecewise linear interpolation genplot Generate summary plots T or F verbose Verbose output T or F Examples generate example series with random bed thicknesses exThick rnorm n 20 mean 10 sd 2 assign alternating rank of 1 and 2 rank double 20 rank seg from 1 to 19 by 2 lt 1 rank seg from 2 to 20 by 2 lt 2 combine into a dataframe ex c
7. txt 1 comma csv 2 semicolon CSV is the default option which interfaces well with EXCEL Does the data file have column titles headers yes no auto auto will auto detect column titles headers which must be single strings and start with a character Return data as 1 matrix 2 data frame generate summary plots T or F Missing values in the file that you are reading from should be indicated by NA If you have included characters in the column titles that are not permitted by R they will be modified repla Replace Values lt 0 with 0 Description Replace all variable values lt O with 0 If first column is location ID depth height time it will not be processed Any number of variables columns permitted Usage repl0 dat ID T genplot T verbose T Arguments dat ID genplot verbose Data series to process If location is included e g depth it should be in the first column Is a location ID included in the first column T or F Generate summary plots T or E Verbose output T or F 58 resample replEps Replace Values lt 0 with Smallest Positive Value Description Replace all variable values lt 0 with the smallest positive floating point number eps that can be represented on machine If first column is location ID depth height time 1t will not be processed Any number of variables columns permitted Usage replEps dat ID T genplot T verbose
8. xmax Maximum spatial frequency to plot ymin Minimum depth height to plot ymax Maximum depth height to plot ncolors Number of colors to use in plot pl How do you want to represent the spatial frequency path 1 lines and points 2 lines 3 points In Plot natural log of spectral results T or F See Also eha and trackFreq Examples Check to see if this is an interactive R session for compliance with CRAN standards YOU CAN SKIP THE FOLLOWING LINE IF YOU ARE USING AN INTERACTIVE SESSION if interactive Generate example series with 3 terms using function cycles Then convert from time to space with sedimentation rate that increases from 1 to 5 cm ka using function sedramp Finally interpolate to median sampling interval using function linterp dat linterp sedRamp cycles freqs c 1 100 1 40 1 20 start 1 end 2500 dt 5 EHA anlaysis output amplitude results out eha dat output 3 Interactively track frequency drift freq traceFreq out 70 trackFreq trackFreq Frequency domain minimal tuning Use interactive graphical inter face and sorting to track frequency drift Description Frequency domain minimal tuning Use interactive graphical interface and sorting algorithm to track frequency drift Usage trackFreq spec threshold NULL pick T fmin NULL fmax NULL dmin NULL dmax NULL xmin NULL xmax NULL ymin NULL ymax NULL h 6 w 4 ydir 1 ncolors 100 ge
9. data value sortDecr Sorting direction F increasing T decreasing ave Average duplicate values T or F genplot Generate summary plots T or F verbose Verbose output T or F stepHeat 61 stepHeat Ar Ar Geochronology Generate an Ar Ar age spectrum and calculate step heating plateau age Description The stepHeat function will evaluate data from stepwise heating experiments producing an Ar Ar age spectrum a weighted mean age with uncertainty and other helpful statistics plots with inter active graphics for data culling The function includes the option to generate results using the approach of IsoPlot 3 70 Ludwig 2008 or ArArCALC Koppers 2002 Usage stepHeat dat unc 1 lambda 5 463e 10 J NULL Jsd NULL CI 2 cul1 T del NULL output F idPts T size NULL unit 1 setAr 95 color black genplot T verbose T Arguments dat dat must be a data frame with six columns as follows 1 Ar39 released 2 date 3 date uncertainty one or two sigma 4 K Ca 5 Ar40 6 F and 7 F uncertainty one or two sigma NOTE F is the ratio Ar40 Ar39K see Koppers 2002 unc What is the uncertainty on your input dates 1 one sigma or 2 2 sigma DEFAULT is one sigma This also applies to the F uncertainty and the J value uncertainty if specified lambda Total decay constant of K40 in units of 1 year The default value is 5 463e 10 year Min et al 2000 J Neutron fluence parameter Jsd Uncertain
10. lowpass noKernel prewhiteAR and prewhiteAR1 pad Pad Stratigraphic Series with Zeros Description Pad Stratigraphic Series with Zeros zero padding Usage pad dat zeros genplot T verbose T Arguments dat Stratigraphic series for mean removal First column should be location e g depth second column should be data value zeros Number of zeros to add on the end of the series By default the number of points will be doubled genplot Generate summary plots T or F verbose Verbose output T or F 48 periodogram peak Identify maxima of peaks in series filter at desired threshold value Description Identify maxima of peaks in any 1D or 2D series filter at desired threshold value Usage peak dat level genplot T verbose T Arguments dat 1 or 2 dimensional series If 2 dimesions first column should be location e g depth second column should be data value level Threshold level for filtering peaks By default all peak maxima reported genplot Generate summary plots T or F verbose Verbose output T or F Examples ex cycles genplot FALSE peak ex level 0 02 periodogram Simple Periodogram Description Calculate periodogram for stratigraphic series Usage periodogram dat padfac 2 demean T detrend F nrm 1 xmin xmax Nyq pl 1 output fQ F genplot T verbose T periodogram 49 Arguments dat Stratigraphic series to analyze First column should be location
11. padfac 5 demean T detrend F medsmooth 0 2 opt 1 linLog 2 siglevel 0 9 output 0 CLpwr T xmin 0 xmax Nyq sigID F p1 1 genplot T verbose T Arguments dat Stratigraphic series for MTM spectral analysis First column should be location e g depth second column should be data value tbw MTM time bandwidth product ntap Number of DPSS tapers to use By default this is set to 2 tbw 1 padfac Pad with zeros to padfac npts points where npts is the original number of data points demean Remove mean from data series T or F detrend Remove linear trend from data series T or F medsmooth MI Op median smoothing parameter 1 use 100 percent of spectrum 0 20 use 20 percent opt Optimization method for robust AR1 model estimation 1 Brent s method fast 2 Gauss Newton fast 3 grid search slow linLog Optimize ART model fit using 1 linear power or 2 log power siglevel Significance level for peak identification output What should be returned as a data frame O nothing 1 spectrum CLs ART fit median smoothed spectrum 2 sig peak freqs 3 sig peak freqs prob 4 all CLpwr Plot ML96 AR 1 noise confidence levels on power spectrum T or F xmin Smallest frequency for plotting xmax Largest frequency for plotting sigID Identify signficant frequencies on power and probabilty plots T or F pl Plot logarithm of spectral power 1 or linear spectral power 2 genplot Generate summary plots T or F ver
12. tbw MTM time bandwidth product ntap Number of DPSS tapers to use By default this is set to 2 tbw 1 order Order of the AR spectrum method AR method yule walker burg ols mle yw CItype Illustrate 1 one sided or 2 two sided confidence intervals on plots padfac Pad with zeros to padfac npts points where npts is the original number of data points demean Remove mean from data series T or F detrend Remove linear trend from data series T or F output Output 1 intermediate spectrum and confidence levels 2 intermediate spec trum 3 confidence levels xmin Smallest frequency for plotting xmax Largest frequency for plotting pl Plot logarithm of spectral power 1 or linear spectral power 2 genplot Generate summary plots T or F verbose Verbose output T or F References Thomson D J L J Lanzerotti and C G Maclennan 2001 The interplanetary magnetic field Statistical properties and discrete modes J Geophys Res 106 15 941 15 962 doi 10 1029 2000JA0001 13 See Also spec mtm mtm mtmML96 lowspec and periodogram A4 mtmML96 mtmML96 Mann and Lees 1996 robust red noise MTM analysis Description Mann and Lees 1996 robust red noise MTM analysis This function implements several improve ments to the algorithm used in SSA MTM toolkit including faster AR1 model optimization and more appropriate edge effect treatment Usage mtmML96 dat tbw 3 ntap NULL
13. to padfac npts points where npts is the original number of data points fcut Cutoff frequency for lowpass filtering win Window type for bandpass filter 0 rectangular 1 Gaussian 2 Cosine tapered window demean Remove mean from data series T or F detrend Remove linear trend from data series T or F addmean Add mean value to bandpass result T or F alpha Gaussian window parameter alpha is l stdev a measure of the width of the Dirichlet kernal Larger values decrease the width of data window reduce dis continuities and increase width of the transform Choose alpha gt 2 5 p Cosine tapered window parameter p is the percent of the data series tapered choose 0 1 xmin Smallest frequency for plotting 38 lowspec xmax Largest frequency for plotting genplot Generate summary plots T or F verbose Verbose output T or F See Also bandpass noKernel noLow prewhiteAR and prewhiteAR1 Examples generate example series with periods of 405 ka 100 ka and 20 ka plus noise ex cycles freqs c 1 405 1 100 1 20 noisevar 1 dt 5 lowpass filter using cosine tapered window res_ex lowpass ex fcut 2 win 2 p 4 lowspec Robust Locally Weighted Regression Spectral Background Estimation Description LOWSPEC Robust Locally Weighted Regression Spectral Background Estimation Usage lowspec dat decimate NULL tbw 3 padfac 5 detrend F siglevel 0 9 setrho lowspan b_tun output 0 CLpwr T x
14. 1 title eha eha ex tbw 3 win 1000 pad 1000 noKernel Remove Gaussian Kernel Smoother from Stratigraphic Series Description Estimate trend and remove from stratigraphic series using a Gaussian kernel smoother Usage noKernel dat smooth 0 1 sort F output 1 genplot T verbose T Arguments dat Stratigraphic series for smoothing First column should be location e g depth second column should be data value smooth Degree of smoothing with a Gaussian kernal 0 no smoothing for a value of 0 5 the kernel is scaled so that its quartiles viewed as prob densities are at 25 percent of the data series length Must be gt 0 sort Sort data into increasing depth required for ksmooth T or F output 1 output residual values 2 output Gaussian kernel smoother genplot Generate summary plots T or F verbose Verbose output T or F See Also bandpass lowpass noLow prewhiteAR and prewhiteAR1 noLow 47 noLow Fit and Remove Lowess Smoother from Stratigraphic Series Description Fit and remove lowess smoother from stratigraphic series Usage noLow dat smooth 20 output 1 genplot T verbose T Arguments dat Stratigraphic series for lowess smoother removal First column should be loca tion e g depth second column should be data value smooth Lowess smoothing parameter output 1 output residual values 2 output lowess fit genplot Generate summary plots T or F verbose Verbose output T or F See Also bandpass
15. 15 Arguments dat Stratigraphic series First column should be location e g depth second col umn should be data value thresh Clip below what theshold value By default will clip at mean value clipval What number should be assigned to the clipped values By default the value of thresh is used clipdiv Clip using what divisor A typical value is 2 By default clipdiv is unity genplot Generate summary plots T or F verbose Verbose output T or F constantSedrate Apply a constant sedimentation rate model to transform a spatial se ries to temporal series Description Apply a constant sedimentation rate model to transform a spatial series to temporal series Usage constantSedrate dat sedrate begin 0 timeDir 1 genplot T verbose T Arguments dat sedrate begin timeDir genplot verbose Stratigraphic series First column should be location e g depth second col umn should be data value Sedimentation rate in same spatial units as dat Time value to assign to first datum Direction of floating time in tuned record 1 elapsed time increases with depth height 1 elapsed time decreases with depth height Generate summary plots T or F Verbose output T or F 16 cycles cosTaper Apply Cosine Taper to Stratigraphic Series Description Apply a percent tapered cosine taper a k a Tukey window to a stratigraphic series Usage cosTaper dat p 25 rms T demean T detrend F ge
16. 2 143627 2 135163 2 090196 2 051682 Fsd lt c 0 00439 0 00270 0 00192 0 00149 0 00331 0 01557 0 03664 0 07846 ex lt data frame cbind perAr39 age sd KCa perAr40 Fval Fsd strats 63 stepHeat ex plot without points identified stepHeat ex size 0 idPts FALSE cull FALSE strats Summary Statistics for Stratigraphic Series Description Summary statistics for stratigraphic series sampling interval and proxy values Usage strats dat output 0 genplot 1 Arguments dat Stratigraphic series to evaluate First column should be location e g depth second column should be data value output Output 0 nothing 1 cumulative dt as percent of data points 2 cumulative dt as percent of total interval duration 3 dt by location genplot Generate summary plots 0 none 1 include plot of cumulative dt 2 include dt histogram density plot Details This function will generate a range of summary statistics for time series including sampling interval information and the statistical distribution of proxy values surrogates Generate phase randomized surrogate series as in Ebisuzaki 1997 Description Generate phase randomized surrogate series as in Ebisuzaki 1997 Usage surrogates dat nsim 1 preserveMean T std T genplot T verbose T 64 Arguments dat nsim preserveMean std genplot verbose Details surrogates Data series with one or two columns If two columns first should b
17. 2554 OS 4 SEA EEE RAE HER eS Ee eS 22 CHE Sieg abe a 2 a Ge a ee EE ENEE 23 CID ahh ore cdot e ete Dee e er oe Br ee eh Sb eS ee eae A 24 GI GENEE He GP cay A gee es 27 Tre q2scdrale 5 4 4 Ga Sow HHA e a be aa EE Se OS BE Se Ep a 27 gaus Tap ar wal Rea EE a a a e a 28 A AI AA 28 petLaskar 2220 6 obo eh EIN md Serie e a ee e A 29 hannlaper s ee SH a De wae ee Re ba ae A ee a ae 30 IT gais ecs aw ead A eee ee be am as 30 Hubert bob a beh oe GE NED ee be ee AN e be ee AN 3 TS ts DA e a EE 31 intepratePOWer seas EE E RRR AAA dE E 32 O ria A eRe de be bE eee he ek bebe 34 Image 0 bree a te BORLA Ee We Ee Gee A eae nee 35 A A ANA 36 Joel ee ee EEN be bBo a BESS a 36 LO WPass e ee eb ei Ge wee et eddie A Pes e a che Belin 37 LOWSPES AE EE A a EE we Oe EE ENEE a oA EE 38 MOdelA eric e a be SEI Ree See A Aa 40 M e as a ba A EOD re a dd E 41 MMAR ibas ir a a A a E a 42 MMMLIG coc a rr a A a ELE 44 e s oreg ai o E O e Be See A 46 NOLOW E EE A whe RE A ee ee eae dw ee EE 47 pado aeea po ae Se Re ates Bees ALA ences ed Bee Gan Ryans ain es 47 pease ae bie es Pag BOs ah os AAA eh eee eos 48 pemodosram e e emra a ee tae bees bee ee ee 48 Pll izan De e v E eee oR Bde wees oS OS See es Re ah eo 51 plotEha 2 ege Ze cits rs Beda S IA A SE Piece e Ee ep e ir Shea be E E E EE E E 53 PrlewhileAR eog sr RE EA A ED e E E EO 33 prewhiteAR ect rs a AS Heeb bets 54 TADOS OTIS ba mr aa A ad o EE 35 A NN 56 id AA Ba ae Be ao SG Be SAS Bla a
18. Choudhury Shah and Thornhill 2008 fregs c 0 12 0 18 0 30 0 42 phase c pi 3 pi 12 pi 4 3xpi 8 amp c 1 1 1 1 cycles freqs phase amp start 0 end 4095 dt 1 noisevar 0 2 delPts Interactively Delete Points in Plot Description Interactively delete points in x y plot Usage delPts dat del NULL ptsize 1 xmin NULL xmax NULL ymin NULL ymax NULL plotype 1 Arguments dat del ptsize xmin Data frame with two columns A vector of indices indicating points to delete If specified the interactive plot is disabled Size of plotted points Minimum x value column 1 to plot 18 detrend xmax Maximum x value column 1 to plot ymin Minimum y value column 2 to plot ymax Maximum y value column 2 to plot plotype Type of plot to generate 1 points and lines 2 points 3 lines See Also idPts iso trim and trimAT demean Remove Mean Value from Stratigraphic Series Description Remove mean value from stratigraphic series Usage demean dat genplot T verbose T Arguments dat Stratigraphic series for mean removal First column should be location e g depth second column should be data value genplot Generate summary plots T or F verbose Verbose output T or F See Also arcsinT detrend divTrend logT prewhiteAR and prewhiteAR1 detrend Subtract Linear Trend from Stratigraphic Series Description Remove linear trend from stratigraphic series U
19. F test confidence level results to spec spec eha modelAInterp win 8 step 2 pad 1000 output 4 perform Evolutive Average Spectral Misfit analysis save results to res res eAsm spec target c 1 405 47 1 126 98 1 96 91 1 37 66 1 22 42 1 18 33 rayleigh 0 1245274 22 eAsmTrack nyquist 6 66597 sedmin 0 5 sedmax 3 numsed 100 siglevel 0 8 iter 10000 output 4 identify minimum Ho SL in each record and plot pl 1 eAsmTrack res 1 threshold 0 05 extract Ho SL result at 18 23 m HoSL18 23 extract res 1 get 18 23 p1 1 tt extract ASM result at 18 23 m asm18 23 extract res 2 get 18 23 p1 0 eAsmTrack EXPERIMENTAL Track ASM Null Hypothesis significance level min ima in eASM results Description EXPERIMENTAL Track ASM Null Hypothesis significance level minima in eASM results Usage eAsmTrack res threshold 5 ydir 1 genplot T verbose T Arguments res threshold ydir genplot verbose Details eAsm results Must have the following format column 1 sedimentation rate remaining columns 2 to n Ho SL titles for columns 2 to n must be the location depth or height Note that this format is ouput by function eAsm Threshold Ho SL value for analysis and plotting Direction for y axis in plots depth or height 1 values increase downwards slower plotting 1 values increase upwards Generate summary plots T or F Verbose output T or F Please see function eAsm for d
20. Low prewhiteAR and prewhiteAR1 66 Examples testPrecession generate example series with 3 precession terms and noise ex lt cycles noisevar 0004 dt 5 bandpass precession terms using Taner window res_ex lt taner ex flow 0 038 fhigh 0 057 generate example series with periods of 405 ka 100 ka and 20 ka plus noise ex2 cycles freqs c 1 405 1 100 1 20 noisevar 1 dt 5 lowpass filter using Taner window res_ex2 taner ex2 fhigh 02 rol1 10 4 testPrecession Astrochronologic testing via the amplitude modulation approach of Zeeden et al 2015 Description Astrochronologic testing via the amplitude modulation approach of Zeeden et al 2015 Usage testPrecession dat nsim 1000 gen 1 rho NULL esinw NULL output T genplot T verbose T Arguments dat nsim gen rho esinw output genplot verbose Stratigraphic series to analyze First column should be location time in kyr a positive value second column should be data value Number of Monte Carlo simulations phase randomized surrogates or ART sur rogates Monte Carlo simulation generator 1 use phase randomized surrogates 2 use ART surrogates Specified lag 1 correlation coefficient rho This value is only used if gen 2 If rho is not specified it will be calculated within the function Theoretical target eccentricity sin omega used for astrochronologic testing By default this is automatically det
21. Package astrochron July 1 2015 Type Package Title A Computational Tool for Astrochronology Version 0 4 3 Date 2015 07 01 Author Stephen Meyers Maintainer Stephen Meyers lt smeyers geology wisc edu gt Description Routines for astrochronologic testing astronomical time scale construction and time se ries analysis Also included are a range of statistical analysis and modeling routines that are rele vant to time scale development and paleoclimate analysis Imports multitaper IDPmisc fields License GPL 3 NeedsCompilation yes Repository CRAN Date Publication 2015 07 01 22 50 04 R topics documented astrochron package 3 anchor Time s pi we leg aw weve ow ad da E de A E io Ae ep it ab A 3 Ml A A A a Ge eG 6 E A histo ae mae O E cages 7 arcsin orar A a ee A 8 GEHEIT y AS ad Ga e i El NEE Yo a i at OG ea a 9 Te EEN 11 bandpass 2 EE E EN E E e be a AE E berperPenods 2 A a ur e A e E EE e Aer 13 ED ida EE eae A e NO a aa 14 CPIE esa ead ow eRe A AR A A A 14 constanitSedrale sas ed a e dd I5 COS Taper eiii eee SSS EECHER 16 Cycles ra a e SG Rot EE 16 R topics documented del vc a aie aa E Ee oe e Se EN ea oe a 17 dEMEAN qe pe See Sa aR Fo oy bbe EE AA de 18 dettend 22 2584 25s ie beets be ete eben 18 divirend oe ke RAE a Rew Dee ee a ee ek eae 19 dpsslaper 4 4 e 8 i346 035 baa e Ve be babe be Bee bt ew bs 19 CASM 4 2 44 REDS HSS EEA SEES eH oe ede ee HS A 20 Asmlrack wy 454042
22. Perform Evolutive Harmonic Analysis using 2pi Slepian tapers a window of 8 meters pad to 1000 points and output Harmonic F test confidence level results fCL eha modelAInterp win 8 pad 1000 output 4 HH Extract Harmonic F test spectrum at approximately 22 meters height spec extract fCL 22 In this extracted spectrum identify F test peak maxima exceeding 90 confidence level freqs peak spec level 0 9 2 Conduct ASM testing on these peaks set Rayleigh frequency in cycles m rayleigh 0 1245274 set Nyquist frequency in cycles m nyquist 6 66597 set orbital target in 1 ky target c 1 405 47 1 126 98 1 96 91 1 37 66 1 22 42 1 18 33 execute ASM asm freq freqs target target rayleigh rayleigh nyquist nyquist sedmin 0 5 sedmax 3 numsed 100 linLog 1 iter 100000 output FALSE Check to see if this is an interactive R session for compliance with CRAN standards YOU CAN SKIP THE FOLLOWING LINE IF YOU ARE USING AN INTERACTIVE SESSION anchorTime 5 if interactive Interactively track obliquity term in EHA harmonic F test confidence level results freqs trackFreq fCL fmin 1 2 fmax 2 4 threshold 8 Convert the spatial frequencies to sedimentation rates sedrate freq2sedrate freqs period 37 66 Convert the sedimentation rate curve to a time space map time sedrate2time sedrate Tune the stratigraphic series using the time space map modelATuned tune modelAInterp time HH I
23. T Arguments dat Data series to process If location is included e g depth it should be in the first column ID Is a location ID included in the first column T or F genplot Generate summary plots T or F verbose Verbose output T or F resample Resample Stratigraphic Series Description Resample a stratigraphic series using a new variably sampled time or space axis Values are piecewise linearly interpolated from original data Usage resample dat xout genplot T verbose T Arguments dat Stratigraphic series for resampling First column should be location e g depth second column should be data value xout Vector of new sampling locations genplot Generate summary plots T or F verbose Verbose output T or F 59 Standardize variable in Stratigraphic Series Description Standardize variable in Stratigraphic Series subtract mean value and divide by standard deviation Usage s dat genplot F verbose T Arguments dat Stratigraphic series for standardization First column should be location e g depth second column should be data value genplot Generate summary plots T or F verbose Verbose output T or F sedRamp Apply ramping sedimentation rate model to convert time to stratig raphy Description Apply a linearly increasing or decreasing sedimentation rate model to convert time to stratigraphy Usage sedRamp dat srstart 0 01 srend 0 05 genplot T verbose T
24. a frame containing Sedimentation rate cm ka ASM cycles ka Null hypothesis signifi cance level 0 100 percent Number of astronomical terms fit References S R Meyers and B B Sageman 2007 Quantification of Deep Time Orbital Forcing by Average Spectral Misfit American Journal of Science v 307 p 773 792 S R Meyers B B Sageman and M A Arthur 2012 Obliquity forcing of organic matter accumula tion during Oceanic Anoxic Event 2 Paleoceanography 27 PA3212 doi 10 1029 2012PA002286 See Also eAsm autoPlot Examples 11 these frequencies are from modelA type astrochron for more information Units are cycles m freq lt c 0 1599833 0 5332776 1 5998329 2 6797201 3 2796575 3 8795948 5 5194235 6 5459830 freq lt data frame freq Rayleigh frequency in cycles m rayleigh lt 0 1245274 Nyquist frequency in cycles m nyquist lt 6 66597 orbital target in 1 ky Predicted periods for 94 Ma see Meyers et al 2012 target lt c 1 405 47 1 126 98 1 96 91 1 37 66 1 22 42 1 18 33 percent uncertainty in orbital target fper c 0 023 0 046 0 042 0 008 0 035 0 004 asm freq freq target target fper fper rayleigh rayleigh nyquist nyquist sedmin 0 5 sedmax 3 numsed 1 00 linLog 1 iter 100000 output FALSE autoPlot Automatically plot multiple stratigraphic series with smoothing if de sired Description Automatically plot and smooth specified stratigraphic data versus locat
25. at are relevant to time scale development and paleocli mate analysis Details Note Package astrochron Type Package Version 0 4 3 Date 2015 07 01 License GPL 3 Please note that this version of astrochron is undergoing BETA TESTING Development of astrochron is partially supported by the U S National Science Foundation 4 astrochron package CAREER Deciphering the Beat of a Timeless Rhythm The Future of Astrochronology EAR 1151438 to S Meyers Collaborative Research Evolution of the Climate Continuum Late Paleogene to Present OCE 1003603 to S Meyers and L Hinnov TO CITE PACKAGE astrochron IN PUBLICATIONS PLEASE USE Meyers S R 2014 astrochron An R Package for Astrochronology http cran r project org package astrochron Also cite the original research papers that document the relevant algorithms as referenced on the help pages for specific functions Author s Stephen Meyers Maintainer Stephen Meyers lt smeyers geology wisc edu gt Examples EXAMPLES OF SOME FUNCTIONS AVAILABLE IN astrochron HH This demo will use a model series are usually read using the function read data modelA HH Interpolate the model stratigraphic series to its median sampling interval modelAInterp linterp modelA Calculate MTM spectrum using 2pi Slepian tapers include ART condfidence level estimates plot power with linear scale mtm modelAInterp tbw 2 ar TRUE pl 2
26. b exThick rank generate lithofacies rank series rankSeries ex 56 readMatrix read Read Data from File Description Read stratigraphic data series from a file either tab delimited CSV or semicolon delimited First column must contain location data depth height time The function will remove missing entries sort by location average duplicate values and generate summary plots Usage read d 1 h auto srt T ave T genplot T Arguments d What column delimiter is used 0 tab txt 1 comma csv 2 semicolon CSV is the default option which interfaces well with EXCEL h Does the data file have column titles headers yes no auto auto will auto detect column titles headers which must be single strings and start with a character srt Sort data values by first column T or F ave Average duplicate values T or F Only applies if input file has 2 columns genplot generate summary plots T or F Details Missing values in the file that you are reading from should be indicated by NA If you have included characters in the column titles that are not permitted by R they will be modified readMatrix Read Data Matrix from File Description Read data matrix from a file either tab delimited CSV or semicolon delimited Usage readMatrix d 1 h auto output 1 genplot F repl0 Arguments d output genplot Details 57 What column delimiter is used 0 tab
27. bel for the x axis in quotes ylab Label for the y axis in quotes main Label for the plot in quotes fill Use gray fill for density plots T or F Examples random numbers from a normal distribution ex1 lt rnorm 1000 random numbers from an exponential distribution ex2 lt rexp 1000 xplot ex1 ex2 zoomIn Dynamically explore cross plot zoom in into specified region Description Dynamically explore cross plot zoom in into specfied region Accepts one dataframe matrix with two columns or two dataframes vectors with one column Usage zoomIn dat1 dat2 NULL ptsize 1 xmin NULL xmax NULL ymin NULL ymax NULL plotype 1 verbose T Arguments dat Data frame with one or two columns If one column dat must also be specified dat2 Data frame with one column ptsize Size of plotted points xmin Minimum x value column 1 to plot xmax Maximum x value column 1 to plot ymin Minimum y value column 2 to plot ymax Maximum y value column 2 to plot plotype Type of plot to generate 1 points and lines 2 points 3 lines verbose Verbose output T or F Index Topic package astrochron package 3 anchorTime 5 ar 54 ar1 6 arletp 7 arcsinT 8 18 19 37 54 55 armaGen 9 asm 9 2 astrochron astrochron package 3 astrochron package 3 autoPlot 11 bandpass 12 38 46 47 54 55 65 bergerPeriods 13 cb 14 clipIt 14 constantSedrate 15 cosTaper 16 20 28 30 cycles 16
28. bose Verbose output T or F mm MI Oe 45 Details This function conducts the Mann and Lees 1996 ML96 robust red noise analysis with an improved median smoothing approach The original Mann and Lees 1996 approach applies a truncation of the median smoothing window to include fewer frequencies near the edges of the spectrum while truncation is required its implementation in the original method often results in an edge effect that can produce excess false positive rates at low frequencies commonly within the eccentricity band Meyers 2012 To help address this issue an alternative median smoothing approach is applied that implements Tukey s robust end point rule and symmetrical medians see the function runmed for details Nu merical experiments indicate that this approach produces an approximately uniform false positive rate across the spectrum It should be noted that the false positive rates are still inflated with this method but they are substantially reduced compared to the original ML96 approach For example simulations using rho 0 9 using identical parameters to those in Meyers 2012 yield median false positive rates of 1 7 7 3 and 13 4 for the 99 95 and 90 confidence levels respectively This compares with 4 7 11 4 and 17 8 using the original approach see Table 2 of Meyers 2012 NOTE If the fast Brent or Gauss Newton methods fail use the slow grid search approach This version of the ML96 algor
29. ches Width of plot in inches Direction for y axis in plots depth or height 1 values increase downwards slower plotting 1 values increase upwards An option for the color plots O do nothing 1 plot log of value useful for plotting power 2 normalize to maximum value useful for plotting amplitude 3 use normalization provided in norm Optional amplitude normalization divisor consisting of a single column dataframe This option is provided in case you d like to normalize a set of EHA results using the same scheme e g before and after removal of spectral lines Label for x axis Label for y axis pls 53 ncolors Number of colors to use in plot colorscale Include a color scale in the plot T or F filetype Generate pdf jpeg or png file O no 1 pdf 2 jpeg 3 png output If amplitude is normalized pl 2 output normalization used T or F verbose Verbose output T or F pls Set default plotting parameters for vertical stratigraphic plots Description Set default plotting parameters for vertical stratigraphic plots This is ususally invoked after function pl Usage plS f T s 1 Arguments f Are you plotting the first leftmost stratigraphic plot T or F s Size of the symbols and text on plot Default 1 prewhiteAR Prewhiten Stratigraphic Series with Autoregressive Filter Order Se lected by Akaike Information Criterion Description Prewhiten stratigraphic series using autoregre
30. column should be data value tbw MTM time bandwidth product ntap Number of DPSS tapers to use By default this is set to 2 tbw 1 padfac Pad with zeros to padfac npts points where npts is the original number of data points demean Remove mean from data series T or F detrend Remove linear trend from data series T or F siglevel Significance level for peak identification ar Estimate conventional AR 1 noise spectrum and confidence levels T or F CLpwr Plot AR 1 noise confidence levels on power spectrum T or F output What should be returned as a data frame O nothing l spectrum CLs AR1 fit 2 sig peak freqs 3 sig peak freqs prob 4 all xmin Smallest frequency for plotting xmax Largest frequency for plotting pl Plot logarithm of spectral power 1 or linear spectral power 2 sigID Identify signficant frequencies on power and probabilty plots T or F genplot Generate summary plots T or F verbose Verbose output T or F 42 mtmAR References Rahim K J and Burr W S and Thomson D J 2014 Appendix A Multitaper R package in Appli cations of Multitaper Spectral Analysis to Nonstationary Data PhD diss Queen s Univieristy pp 149 183 http hndl handle net 1974 12584 Thomson D J 1982 Spectrum estimation and harmonic analysis Proc IEEE 70 1055 1096 doi 10 1109 PROC 1982 12433 See Also spec mtm lowspec and periodogram Examples generate example series with periods o
31. d on Berger et al 1992 Values are determined by piecewise linear interpolation Usage bergerPeriods age genplot T Arguments age Age millions of years before present genplot Generate summary plots T or F References A Berger M F Loutre and J Laskar 1992 Stability of the Astronomical Frequencies Over the Earth s History for Paleoclimate Studies Science v 255 p 560 566 14 clipIt cb Combine Multiple Vectors Description Bind two vectors together and return result as a data frame Alternatively extract specified columns from a data frame bind them together and return result as a data frame Usage cb a b Arguments a first input vector OR a data frame with gt 1 column b second input vector OR if a is a data frame with gt 1 column a list of columns to bind Examples example dataset x lt rnorm 100 dim x lt c 10 10 x lt data frame x bind two columns cb x 1 x 5 bind five columns cb x c 1 2 4 7 9 clipIt Create non linear response by clipping stratigraphic series Description Create non linear response by clipping stratigraphic series below a threshold value Alternatively mute response below a threshold value using a contant divisor Both approaches will enhance power in modulator e g eccentricity and diminish power the carrier e g precession Usage clipIt dat thresh NULL clipval NULL clipdiv NULL genplot T verbose T constantSedrate
32. d precession In order to construct such models it is necessary to choose standardize T and to set the individual weights eWt oWt pWt to the square root of the desired variance contribution Value Eccentricity tilt precession References Laskar J Robutel P Joutel F Gastineau M Correia A CM Levrard B 2004 A long term numerical solution for the insolation quantities of the Earth Astron Astrophys Volume 428 261 285 Laskar J Fienga A Gastineau M Manche H 2011 La2010 A new orbital solution for the long term motion of the Earth Astron Astrophys Volume 532 A89 Laskar J Gastineau M Delisle J B Farres A Fienga A 2011 Strong chaos induced by close encounters with Ceres and Vesta Astron Astrophys Volume 532 L4 See Also getLaskar Examples create an ETP model from 10000 ka to 20000 ka with a 5 ka sampling interval this will automatically download the astronomical solution ex etp tmin 10000 tmax 20000 dt 5 tt alternatively download the astronomical solution first ex2 getLaskar ex etp tmin 10000 tmax 20000 dt 5 solution ex2 26 extract extract Extract record from EHA time frequency output or eAsm output Description Extract record from EHA time frequency output or eAsm output Use interactive graphical interface to identify record Usage extract spec get NULL xmin NULL xmax NULL ymin NULL ymax NULL h 6 w 4 ydir 1 p1 0 ncolor
33. dth is not specified generate interactive plots for bandwidth selection For use with the function eha integratePower can process spectrograms time frequency or single spectra Usage integratePower spec flow NULL fhigh NULL fmax NULL unity F f T xmin NULL xmax NULL ymin NULL ymax NULL nots NULL pad NULL ydir 1 ncolors 100 h 6 w 9 ln F genplot T verbose T integratePower 33 Arguments spec Spectral results to evaluate If the data frame contains time frequency results it must have the following format column 1 frequency remaining columns 2 to n power titles for columns 2 to n must be the location depth or height Note that this format is ouput by function eha If the data frame contains one spec trum it must have the following format column 1 frequency column 2 power flow Low frequency cutoff for integration If flow or fhigh are not specified interac tive plotting is activated fhigh High frequency cutoff for integration If flow or fhigh are not specified interac tive plotting is activated fmax Integrate total power up to this frequency unity Normalize spectra such that total variance up to fmax is unity T of F fo Is 0 included in the spectra T or F xmin Minimum frequency for PLOTTING xmax Maximum frequency for PLOTTING ymin Minimum depth height time for PLOTTING Only used if processing time frequency results ymax Maximum depth height time for PLOTTING Only used
34. e g depth second column should be data value padfac Pad with zeros to padfac npts points where npts is the original number of data points padfac will automatically promote the total padded series length to an even number to ensure the Nyquist frequency is calculated However if padfac is set to 0 no padding will be implemented demean Remove mean from data series T or F detrend Remove linear trend from data series T or F nrm Power normalization 0 no normalization 1 divide Fourier transform by npts xmin Smallest frequency for plotting xmax Largest frequency for plotting pl Power spectrum plotting 1 log power 2 linear power output Return output as new data frame 0 no 1 frequency amplitude power phase 2 frequency real coeff imag coeff To Return results for the zero frequency T or E genplot Generate summary plots T or F verbose Verbose output T or F See Also mtm and lowspec Examples xxxxx PART 1 Demonstrate the impact of tapering tt generate example series with 10 periods 100 40 29 21 19 14 10 5 4 and 3 ka ex cycles c 1 100 1 40 1 29 1 21 1 19 1 14 1 10 1 5 1 4 1 3 amp c 1 75 0 01 5 25 0 01 0 1 0 05 0 001 0 01 set zero padding amount for spectral analyses pad 1 results in no padding pad 2 will pad the series to two times its original length start with pad 1 then afterwards evaluate pad 2 pad 1 calculate the periodogram with no tapering a
35. e location e g depth second column should be data value Number of phase randomized surrogate series to generate Should surrogate series have the same mean value as data series T or F Standardize results to guarantee equivalent variance as data series T or F Generate summary plots Only applies if nsim 1 T or F Verbose output T or F This function will generate phase randomized surrogate series as in Ebisuzaki 1997 It is an R translation of the Matlab code by V Moron see link below with modifications and additional features References W Ebisuzaki 1997 A Method to Estimate the Statistical Significance of a Correlation When the Data Are Serially Correlated Journal of Climate v 10 p 2147 2153 Matlab code by V Moron http www mathworks com matlabcentral fileexchange 10881 weaclim content ebisuzaki m Original C code by W Ebisuzaki http www ftp cpc ncep noaa gov wd5 we random_phase Examples generate example series with 3 precession terms and noise ex lt cycles start 0 end 500 noisevar 0004 dt 5 generate phase randomized surrogates ran_ex lt surrogates ex nsim 1 compare periodograms of data and surrogates resl lt periodogram ex padfac 0 output 1 genplot FALSE res2 lt periodogram ran_ex padfac 0 output 1 genplot FALSE p1 2 plot ex type 1 main black original red surrogate lines ran_ex col red 1ty 4 plot res1 1 res1 2 type 1 1lwd 2 main black
36. eld and J A Dodd 2001 Baseline subtraction using robust local regression estimation Journal of Quantitative Spectroscopy amp Radiative Transfer v 68 p 179 193 D J Thomson 1982 Spectrum estimation and harmonic analysis TEEE Proceedings v 70 p 1055 1096 See Also spec mtm rfbaseline mtm mtmAR mtmML96 and periodogram Examples generate example series with periods of 400 ka 100 ka 40 ka and 20 ka ex cycles fregs c 1 400 1 100 1 40 1 20 start 1 end 1000 dt 5 add AR1 noise noise ar1 npts 200 dt 5 sd 5 ex 2 ex 2 noise 2 LOWSPEC analysis p1 1 title lowspec lowspec ex compare to MIM spectral analysis with conventional AR1 noise test pl 1 title mtm mtm ex ar1 TRUE compare to ML96 analysis p1 1 title mtmML96 mtmML96 ex compare to amplitudes from eha pl 1 title eha eha ex tbw 3 win 1000 pad 1000 modelA Example stratigraphic model series mtm 41 Description Example stratigraphic model series Usage modelA Format Height meters weight percent CaCO3 mtm Multitaper Method Spectral Analysis Description Multitaper Method MTM Spectral Analysis Usage mtm dat tbw 3 ntap NULL padfac 5 demean T detrend F siglevel 0 9 ar1 T output 0 CLpwr T xmin xmax pl 1 sigID F genplot T verbose T Arguments dat Stratigraphic series for MTM spectral analysis First column should be location e g depth second
37. erkeley Geochronology Center Special Publication No 4 Berkeley 77 p I McDougall and T M Harrison 1991 Geochronology and Thermochronology by the 40Ar 39Ar Method Oxford University Press New York 269 pp K Min R Mund P R Renne and K Ludwig 2000 A test for systematic errors in 40Ar 39Ar geochronology through comparison with U Pb analysis of a 1 1 Ga rhyolite Geochimica et Cos mochimica Acta v 64 p 73 98 I Wendt and C Carl 1991 The statistical distribution of the mean squared weighted deviation Chemical Geology v 86 p 275 285 See Also stepHeat Examples Check to see if this is an interactive R session for compliance with CRAN standards YOU CAN SKIP THE FOLLOWING LINE IF YOU ARE USING AN INTERACTIVE SESSION if interactive Sample NE 08 01 Ar Ar data from Meyers et al 2012 supplement age lt c 93 66 94 75 94 6 94 22 86 87 94 64 94 34 94 03 93 56 93 85 88 55 93 45 93 84 94 39 94 11 94 48 93 82 93 81 94 18 93 78 94 41 93 49 95 07 94 19 sd2 lt c 5 83 4 10 8 78 2 5 8 86 3 37 4 63 3 18 8 35 5 73 4 23 2 56 2 3 1 7 3 1 2 78 1 62 92 98 1 41 1 21 1 38 1 48 0 93 sd lt sd2 2 wtMean age sd H xplot Generate Cross plot with Density Estimates on Axes Description Generate a Cross plot with Density Estimates on Axes Custom axes titles optional Usage xplot x y xlab NULL ylab NULL main NULL fi11 T zoomIn 71 Arguments D Variable 1 y Variable 2 xlab La
38. ermined within the function using the solu tion of Laskar et al 2004 Return results as a new data frame T or F Generate summary plots T or F Verbose output T or F testPrecession 67 Details This astrochronologic testing method compares observed precession scale amplitude modulations to those expected from the theoretical eccentricity solutions It is applicable for testing astrochronolo gies spanning 0 50 Ma The technique implements a series of filters to guard against artificial intro duction of eccentricity modulations during tuning and data processing and evaluates the statistical significance of the results using Monte Carlo simulation Zeeden et al 2015 The astronomically tuned data series under evaluation should consist of two columns time in kilo years amp data value Note that time must be positive The default astronomical solutions used for the astrochronologic testing come from Laskar et al 2004 When reporting a p value for your result it is important to consider the number of simulations used A factor of 10 is appropriate such that for 1000 simulations one would report a minimum p value of p lt 0 01 and for 10000 simulations one would report a minimum p value of p lt 0 001 Please be aware that the kernel density estimate plots which summarize the simulations represent smoothed models Due to the smoothing bandwidth they can sometimes give the impression of simulation values that are
39. erms using function cycles Then convert from time to space with sedimentation rate that increases from 1 to 5 cm ka using function sedramp Finally interpolate to median sampling interval using function linterp dat linterp sedRamp cycles freqs c 1 100 1 40 1 20 start 1 end 2500 dt 5 EHA anlaysis output probability results out eha dat output 4 Isolate peaks with probability gt 0 9 freq trackFreq out 9 trim Remove Outliers from Stratigraphic Series Description Automatically remove outliers from stratigraphic series using boxplot algorithm Usage trim dat c 1 5 genplot T verbose T Arguments dat Stratigraphic series for outlier removal First column should be location e g depth second column should be data value Cc c defines the coef variable for boxplot stats For more information box plot stats genplot Generate summary plots T or F verbose Verbose output T or F See Also delPts idPts iso and trimAT 72 trough trimAT Remove Outliers from Stratigraphic Series Description Remove outliers from stratigraphic series using specified threshold value Usage trimAT dat thresh 0 dir 2 genplot T verbose T Arguments dat Stratigraphic series for outlier removal First column should be location e g depth second column should be data value thresh Threshold value for outlier detection dir Remove values 1 smaller than or 2 larger
40. erve power for white process T or E demean Remove mean from data series T or F detrend Remove linear trend from data series T or E genplot Generate summary plots T or F verbose Verbose output T or F See Also cosTaper gausTaper and hannTaper eAsm EXPERIMENTAL Evolutive Average Spectral Misfit Description EXPERIMENTAL Calculate Evolutive Average Spectral Misfit with Monte Carlo spectra simula tions as updated in Meyers et al 2012 Usage eAsm spec siglevel 0 9 target fper NULL rayleigh nyquist sedmin 1 sedmax 5 numsed 50 linLog 1 iter 100000 ydir 1 output 4 genplot F Arguments spec Time frequency spectral results to evaluate Must have the following format column frequency remaining columns 2 to n probability titles for columns 2 to n must be the location depth or height Note that this format is ouput by function eha siglevel Threshold level for filtering peaks target A vector of astronomical frequencies to evaluate 1 ka These must be in order of increasing frequency e g el e2 e3 01 02 p1 p2 Maximum allowed is 50 frequencies fper A vector of uncertainties on each target frequency 1 ka Values should be from 0 1 representing uncertainty as a percent of each target frequency The order of the uncertainties must follow that of the target vector By default no uncertainty is assigned eAsm 21 rayleigh Rayleigh frequency cycles m nyquist Nyquist frequency cycles m sedmin Minimu
41. etails eha 23 eha Evolutive Harmonic Analysis Description Evolutive Harmonic Analysis using the Thomson Multitaper Method Usage eha dat tbw 2 pad fmin fmax step win demean T detrend T siglevel 0 90 sigID F ydir 1 output 0 p1 1 xlab ylab genplot 2 verbose T Arguments dat tbw pad fmin fmax step win demean detrend siglevel sigID ydir output pl xlab ylab genplot verbose Stratigraphic series to analyze First column should be location e g depth second column should be data value MTM time bandwidth product lt 10 Pad with zeros to how many points Must not factor into a prime number gt 23 Maximum number of points is 200 000 Smallest frequency for analysis and plotting Largest frequency for analysis and plotting Step size for EHA window in units of space or time Window size for EHA in units of space or time Remove mean from data series T or F Remove linear trend from data series T or F Significance level for peak identification filtering 0 1 Identify signficant frequencies on power amplitude and probabilty plots Only applies when one spectrum is calculated T or F Direction for y axis in EHA plots depth height time 1 values increase downwards slower plotting 1 values increase upwards Return output as new data frame O no 1 all results 2 power 3 amplitude 4 probability 5 significant frequencies only for one spectrum 6 significant freq
42. evaluate the spectral properties of the window alpha Gaussian window parameter alpha is 1 stdev a measure of the width of the Dirichlet kernel Larger values decrease the width of data window reduce dis continuities and increase width of the transform Choose alpha gt 2 5 rms Normalize taper to RMS 1 to preserve power for white process T or F demean Remove mean from data series T or F detrend Remove linear trend from data series T or F genplot Generate summary plots T or F verbose Verbose output T or F References Harris 1978 On the use of windows for harmonic analysis with the discrete Fourier transform Proceedings of the IEEE v 66 p 51 83 See Also cosTaper dpssTaper and hannTaper getColor Query R for color information Description Query R for color information Usage getColor color getLaskar 29 Arguments color The name of the color you are interested in in quotes getLaskar Download Laskar et al 2004 201 la 201 1b astronomical solutions Description Download Laskar et al 2004 201 1a 2011b astronomical solutions Usage getLaskar sol 1a04 Arguments sol A character string that specifies the astronomical solution to download la04 lal0a la10b la10c la Details 1a04 three columns containing precession angle obliquity and eccentricity of Laskar et al 2004 lal0a one column containing the la10a eccentricity solution of Laskar et al 201 1a
43. f 400 ka 100 ka 40 ka and 20 ka ex cycles fregs c 1 400 1 100 1 40 1 20 start 1 end 1000 dt 5 add AR1 noise noise ar1 npts 200 dt 5 sd 5 ex 2 ex 2 noise 2 MTM spectral analysis with conventional AR1 noise test pl 1 title mtm mtm ex ar1 TRUE compare to ML96 analysis p1 1 title mtmML96 mtmML96 ex compare to analysis with LOWSPEC p1 1 title lowspec lowspec ex compare to amplitudes from eha p1 1 title eha eha ex tbw 3 win 1000 pad 1000 mtmAR Intermediate spectrum test of Thomson et al 2001 Description Perform the intermediate spectrum test of Thomson et al 2001 Paraphrased from Thomson et al 2001 Form an intermediate spectrum by dividing MTM by AR estimate Choose an order P for a predictor A variety of formal methods are available in the mtmAR 43 literature but practically one keeps increasing P the order until the range of the intermediate spectrum Si f equation C4 of Thomson et al 2001 stops decreasing rapidly as a function of P If the intermediate spectrum is not roughly white as judged by the minima the value of P should be increased Usage mtmAR dat tbw 3 ntap NULL order 1 method mle CItype 1 padfac 5 demean T detrend F output 1 xmin 0 xmax Nyq pl 1 genplot T verbose T Arguments dat Stratigraphic series for analysis First column should be location e g depth second column should be data value
44. fonts plotype Type of plot to generate 1 points and lines 2 points 3 lines output Return which of the following 1 tuned stratigraphic series 2 age control points 3 tuned stratigraphic series and age control points genplot Generate additional summary plots tuned record time space map sedimenta tion rates T or F References Paillard D L Labeyrie and P Yiou 1996 Macintosh program performs time series analysis Eos Trans AGU v 77 p 379 Examples Check to see if this is an interactive R session for compliance with CRAN standards YOU CAN SKIP THE FOLLOWING LINE IF YOU ARE USING AN INTERACTIVE SESSION if interactive generate example series with 3 precession terms and noise using function cycles then convert from time to space using sedimentation rate that increases from 1 to 7 cm ka ex sedRamp cycles start 1 end 400 dt 2 noisevar 00005 srstart 0 01 srend 0 07 36 logT tt create astronomical target series targ cycles start 1 end 400 dt 2 manually tune tuned linage ex targ should you need to flip the direction of the astronomical target series use function cb tuned linage ex cb targ 1 1 targ 2 linterp Piecewise Linear Interpolation of Stratigraphic Series Description Interpolate stratigraphic series onto a evenly sampled grid using piecewise linear interpolation Usage linterp dat dt start genplot T verbose T Arguments dat S
45. g equation 18 of Koppers 2002 References A A P Koppers 2002 ArArCALC software for 40Ar 39Ar age calculations Computers amp Geo sciences v 28 p 605 619 K R Ludwig 2008 User s Manual for Isoplot 3 70 A Geochronological Toolkit for Microsoft Excel Berkeley Geochronology Center Special Publication No 4 Berkeley 77 p I McDougall and T M Harrison 1991 Geochronology and Thermochronology by the 40Ar 39Ar Method Oxford University Press New York 269 pp K Min R Mund P R Renne and K Ludwig 2000 A test for systematic errors in 40Ar 39Ar geochronology through comparison with U Pb analysis of a 1 1 Ga rhyolite Geochimica et Cos mochimica Acta v 64 p 73 98 I Wendt and C Carl 1991 The statistical distribution of the mean squared weighted deviation Chemical Geology v 86 p 275 285 See Also wtMean Examples Check to see if this is an interactive R session for compliance with CRAN standards YOU CAN SKIP THE FOLLOWING LINE IF YOU ARE USING AN INTERACTIVE SESSION if interactive Sample MT 09 09 incremental heating Ar Ar data from Sageman et al 2014 perAr39 lt c 4 96 27 58 19 68 39 9 6 25 1 02 0 42 0 19 age lt c 90 08 89 77 89 92 89 95 89 89 89 55 87 71 86 13 sd lt c 0 18 0 11 0 08 0 06 0 14 0 64 1 5 3 22 KCa lt c 113 138 101 195 307 27 17 24 perAr49 lt c 93 42 99 42 99 64 99 79 99 61 97 99 94 64 90 35 Fval lt c 2 148234 2 140643 2 144197 2 145006
46. ge a 56 PEPlO cn ea RE A he eo ee A ee dd ee ee ae 57 TEPLEDS Soca gs Seek ae ee A A RR ER ee Ee E 58 TESAMPle ss sas SAAS whoa ee Ke wep Re ak A hee Sew dea argos A 58 Sheed e OMA EGA Roe ee he ea Oe RR ee we he 59 astrochron package 3 Sedrate2ume 5 5 46S Sew a Ei EE EN SE eee Ree E E e 60 GOEN Oe uge God beh e RE aee ee AA A Eee Re Ae eg pa gh aa 60 StEpHEal egies ee be A oe A ee eat Pah a Pe Fa ee As 61 BER oe BA se Bo ep ee ae ee a oe eo eee aes bd eh 63 SUITOBALES ee TR Oe RS ESE RS RA 63 TANCE yi ose a hares Mie ety es o EE 65 TEStPrECESSION sirio mp exe ho eS eee GAO ee e a amp eee A wk E 66 TONES a 4 dose ee oe we hae eee oe ee BS e ee E 68 tracePreG s bs kk RB eee ee SOE REAR EEE Eee ee eS a 68 trackPreq ha ss he ee we ale eda Gea bee oe eee eee E 70 A E 71 TE bk EE E EN 72 MOUSE a e ago EE ELE EN els Beno Rh Ae a a la OE A e EN da 72 TUNG foe ce ke ek OR ee eee E eS ER oe ee a Re ee ee 73 WIHleESV Zoe AR aee a eh A pa reg A A dere eA E be 73 WEN ENEE A 74 WiMeat 4 22404 20554 a a de E Ee Ee EN E a 74 Pl acc oi EES EEN EE 76 ZOOMIN ioa gs ar he eta de A eed Bare e NS a Index 78 astrochron package astrochron A Computational Tool for Astrochronology Description astrochron is a computational tool for astrochronology It includes routines for astrochronologic testing astronomical time scale construction and time series analysis Also included are a range of statistical analysis and modeling routines th
47. gram dpssTaper ex2 tbw 1 demean FALSE output 1 padfac pad 3 pwr_dpss3 2 periodogram dpssTaper ex2 tbw 3 demean FALSE output 1 padfac pad 3 now plot the results ymin min rbind log pwr_rect 2 1 log pwr_hann 2 1 log pwr_cos 2 1 log pwr_dpss1 2 1 log pwr_dpss3 2 1 ymax max rbind log pwr_rect 2 1 log pwr_hann 2 1 log pwr_cos 2 1 log pwr_dpss1 2 1 log pwr_dpss3 2 1 p1 2 plot freg 2 1 log pwr_rect 2 11 type 1 ylim c ymin ymax 1lwd 2 ylab log Power xlab Frequency cycles ka main Comparison of rectangle black 30 cosine blue and Hann orange taper cex main 1 lines freq 2 1 log pwr_hann 2 1 col orange lwd 2 lines freq 2L 1 log pwr_cos 2 1 col blue points c 1 100 1 40 1 29 1 21 1 19 1 14 1 10 1 5 1 4 1 3 rep ymax 10 cex 5 col purple gt plot fregq 2 1 log pwr_rect 2 11 type 1 ylim c ymin ymax 1wd 2 ylab log Power xlab Frequency cycles ka main Comparison of rectangle black 1pi DPSS green and 3pi DPSS red taper cex main 1 lines freq 2 1 log pwr_dpss1 2 1 col green lines freq 2 1 log pwr_dpss3 2 1 col red lwd 2 points c 1 100 1 4 1 29 1 21 1 19 1 14 1 10 1 5 1 4 1 3 rep ymax 10 cex 5 col purple pl Set Up Plots Description Open new device and set up for multiple plots output to screen or PDF if desired Usage pl n r c h w mar file title A
48. icity tilt precession time series using the theoretical astronomical solutions By default the Laskar et al 2004 solutions will be downloaded Alternatively one can specify the astronomical solution Usage etp tmin 0 tmax 1000 dt 1 eWt 1 oWt 1 pWt 1 esinw T solution NULL standardize T genplot T verbose T Arguments tmin Start time ka before present J2000 for ETP tmax End time ka before present J2000 for ETP dt Sample interval for ETP ka Minimum ka eWt Relative weight applied to eccentricity solution oWt Relative weight applied to obliquity solution pwt Relative weight applied to precession solution etp 25 esinw Use e sinw in ETP calculation T or F If set to false sinw is used solution A data frame containing the astronomical solution to use The data frame must have four columns Time ka positive and increasing Precession Angle Obliq uity Eccentricity standardize Standardize subtract mean divide by standard deviation precession obliquity and eccentricity series before applying weight and combining T or F genplot Generate summary plots T or F verbose Verbose output T or F Details Note If you plan to repeatedly execute the etp function it is advisable to download the astronomical solution once using the function getLaskar Note It is common practice to construct ETP models that have specified variance ratios e g 1 1 1 or 1 0 5 0 5 for eccentricity obliquity an
49. if processing time frequency results npts The number of points in the processed time series window This is needed for proper spectrum normalization pad The total padded length of the processed time series window This is needed for proper spectrum normalization ydir Direction for y axis in plots depth or height 1 values increase downwards slower plotting 1 values increase upwards Only used if processing time frequency results ncolors Number of colors to use in plot Only used if processing time frequency results h Height of plot in inches w Width of plot in inches In Plot natural log of spectral results T or F genplot Generate summary plots T or F verbose Verbose output T or F Details Depending on the normalization used you may want to preprocess the power spectra prior to inte gration See Also eha 34 iso Examples generate etp signal over past 10 Ma ex etp tmax 10000 evolutive power pwr eha ex win 500 fmax 1 pad 2000 output 2 p1 2 integrate power from main obliquity term integratePower pwr flow 0 02 fhigh 0 029 npts 501 pad 2000 iso Isolate Data from a Specified Stratigraphic Interval Description Isolate a section of a uni or multi variate stratigraphic data set for further analysis Usage iso dat xmin xmax col 2 logx F logy F genplot T verbose T Arguments dat Data frame containing stratigraphic variable s of interest First col
50. in 0 xmax Nyq genplot T verbose T Stratigraphic series for bandpass filtering First column should be location e g depth second column should be data value Pad with zeros to padfac npts points where npts is the original number of data points Lowest frequency to bandpass Highest frequency to bandpass Window type for bandpass filter O rectangular 1 Gaussian 2 Cosine tapered window Gaussian window parameter alpha is l stdev a measure of the width of the Dirichlet kernel Choose alpha gt 2 5 Cosine tapered window parameter p is the percent of the data series tapered choose 0 1 Remove mean from data series T or F Remove linear trend from data series T or F Add mean value to bandpass result T or F Output 1 filtered series 2 bandpass filter window Smallest frequency for plotting Largest frequency for plotting Generate summary plots T or F Verbose output T or F bergerPeriods 13 Value bandpassed stratigraphic series See Also lowpass noKernel noLow prewhiteAR prewhiteAR1 and taner Examples generate example series with 3 precession terms and noise ex lt cycles noisevar 0004 dt 5 tt bandpass precession terms using cosine tapered window res_ex lt bandpass ex flow 0 038 fhigh 0 057 win 2 p 4 bergerPeriods Obliquity and Precession Periods of Berger et al 1992 Description Determine the predicted precession and obliquity periods base
51. inations tones Description Determine all possible difference and combinations tones from a set of frequencies and find the closest one to a specified frequency Usage tones a NULL freqs NULL f T Arguments a The frequency you seeking to match in cycles ka freqs The vector of frequencies from which to calculate difference and combination tones in cycles ka f Output results as frequencies cycles ka If false will output results as periods ka T or F traceFreq Frequency domain minimal tuning Use interactive graphical inter face to trace frequency drift Description Frequency domain minimal tuning Use interactive graphical interface to trace frequency drift Usage traceFreq spec color 2 h 6 w 4 ydir 1 xmin NULL xmax NULL ymin NULL ymax NULL ncolors 100 pl 1 1n F traceFreq 69 Arguments spec Time frequency spectral results to evaluate Must have the following format column 1 frequency remaining columns 2 to n power amplitude or proba bility titles for columns 2 to n must be the location depth or height Note that this format is ouput by function eha color Line color for tracing 1 transparent black 2 transparent white 3 trans parent yellow h Height of plot in inches w Width of plot in inches ydir Direction for y axis in plots depth or height 1 values increase downwards slower plotting 1 values increase upwards xmin Minimum spatial frequency to plot
52. ion Data are smoothed with a Gaussian kernel Usage autoPlot dat cols NULL nrows NULL ydir 1 smooth 0 xgrid 1 output F genplot T verbose T Arguments dat cols nrows ydir smooth Your data frame first column should be location identifier e g depth A vector that identifies the columns to extract first column automatically ex tracted Number of rows in figure Direction for y axis in plots depth height time 1 values increase down wards values increase upwards Width temporal or spatial dimension for smoothing with a Gaussian kernel 0 no smoothing the Gaussian kernel is scaled so that its quartiles viewed as probability densities that is containing 50 percent of the area are at 25 percent of this value 12 bandpass xgrid For kernal smoothing 1 evaluate on ORIGINAL sample grid or 2 evaluate on EVENLY SPACED grid covering range output Output data frame of smoothed values T or F genplot generate summary plots T or F verbose verbose output T or F bandpass Bandpass Filter Stratigraphic Series Description Bandpass filter stratigraphic series using rectangular Gaussian or tapered cosine window Usage bandpass dat padfac 2 flow NULL fhigh NULL wins alpha 3 p 25 demean T Arguments dat padfac flow fhigh win alpha demean detrend addmean output xmin xmax genplot verbose detrend F addmean T output 1 xm
53. ithm was first implemented in Patterson et al 2014 References Mann M E and Lees J M 1996 Robust estimation of background noise and signal detection in climatic time series Clim Change 33 409 445 Meyers S R 2012 Seeing red in cyclic stratigraphy Spectral noise estimation for astrochronol ogy Paleoceanography 27 PA3228 Patterson M O McKay R Naish T Escutia C Jimenez Espejo F J Raymo M E Meyers S R Tauxe L Brinkhuis H and IODP Expedition 318 Scientists 2014 Response of the East Antarctic Ice Sheet to orbital forcing during the Pliocene and Early Pleistocene Nature Geoscience v 7 p 841 847 Thomson D J 1982 Spectrum estimation and harmonic analysis Proc IEEE 70 1055 1096 doi 10 1109 PROC 1982 12433 http www meteo psu edu holocene public_html Mann tools MTM RED Tukey J W 1977 Exploratory Data Analysis Addison See Also runmed spec mtm mtmAR lowspec and periodogram Examples generate example series with periods of 400 ka 100 ka 40 ka and 20 ka ex cycles fregs c 1 400 1 100 1 40 1 20 start 1 end 1000 dt 5 add AR1 noise noise ar1 npts 200 dt 5 sd 0 5 ex 2 ex 2 noise 2 46 noKernel run ML96 analysis p1 1 title mtmML96 mtmML96 ex compare to analysis with conventional AR1 noise test p1 1 title mtm mtm ex compare to analysis with LOWSPEC p1 1 title lowspec lowspec ex compare to amplitudes from eha p1
54. la10b one column containing the la10b eccentricity solution of Laskar et al 201 1a la10c one column containing the la10c eccentricity solution of Laskar et al 201 1a la10d one column containing the la10d eccentricity solution of Laskar et al 201 1a la11 one column containing the la11 eccentricity solution of Laskar et al 201 1b please also cite 2011a References J Laskar P Robutel F Joutel M Gastineau A CM Correia and B Levrard B 2004 A long term numerical solution for the insolation quantities of the Earth Astron Astrophys Volume 428 261 285 Laskar J Fienga A Gastineau M Manche H 2011a La2010 A new orbital solution for the long term motion of the Earth Astron Astrophys Volume 532 A89 Laskar J Gastineau M Delisle J B Farres A Fienga A 2011b Strong chaos induced by close encounters with Ceres and Vesta Astron Astrophys Volume 532 L4 30 headn hannTaper Apply Hann Taper to Stratigraphic Series Description Apply a Hann Hanning taper to a stratigraphic series Usage hannTaper dat rms T demean T detrend F genplot T verbose T Arguments dat Stratigraphic series for tapering First column should be location e g depth second column should be data value If no data is identified will output a 256 point taper to evaluate the spectral properties of the window rms Normalize taper to RMS 1 to preserve power for white process T or E de
55. larger or smaller than actually present However the reported p value does not suffer from these issues Value When nsim is set to zero the function will output a data frame with five columns 1 time 2 precession bandpass filter output 3 amplitude envelope of 2 4 lowpass filter out put of 3 5 theoretical eccentricity as extracted from precession modulations using the filtering algorithm When nsim is gt 0 the function will output the correlation coefficients for each simulation References C Zeeden S R Meyers L J Lourens and F J Hilgen 2015 accepted Testing astronomically tuned age models Paleoceanography J Laskar P Robutel F Joutel M Gastineau A CM Correia and B Levrard B 2004 A long term numerical solution for the insolation quantities of the Earth Astron Astrophys Volume 428 261 285 Examples HH as a test series use the three dominant precession terms from Berger et al 1992 ex lt cycles start 0 end 1000 dt 2 now conduct astrochronologic testing resl testPrecession ex if you plan to run testPrecession repeatedly it is advisable to download the astronomical solution and construct esinw first ex2 lt getLaskar ex3 lt etp tmin 0 tmax 1000 dt 2 eWt 0 oWt 0 pWt 1 esinw TRUE solution ex2 standardize FALSE 68 traceFreq now conduct astrochronologic testing res2 lt testPrecession ex esinw ex3 tones Calculate all possible difference and comb
56. m sedimentation rate for investigation cm ka sedmax Maximum sedimentation rate for investigation cm ka numsed Number of sedimentation rates to investigate in ASM optimization grid Maxi mum allowed is 500 linLog Use linear or logarithmic scaling for sedimentation rate grid spacing O linear 1 log iter Number of Monte Carlo simulations for significance testing Maximum allowed is 100 000 ydir Direction for y axis in plots depth or height 1 values increase downwards slower plotting 1 values increase upwards output Return output as a new data frame 0 nothing 1 Ho SL 2 ASM 3 astronomical terms 4 everything genplot Generate summary plots T or F Details Please see function asm for details References S R Meyers and B B Sageman 2007 Quantification of Deep Time Orbital Forcing by Average Spectral Misfit American Journal of Science v 307 p 773 792 S R Meyers 2012 Seeing Red in Cyclic Stratigraphy Spectral Noise Estimation for Astrochronol ogy Paleoceanography 27 PA3228 doi 10 1029 2012PA002307 S R Meyers B B Sageman and M A Arthur 2012 Obliquity forcing of organic matter accumula tion during Oceanic Anoxic Event 2 Paleoceanography 27 PA3212 doi 10 1029 2012PA002286 See Also asm eAsmTrack and eha Examples use modelA as an example data modelA interpolate to even sampling interval modelAInterp linterp modelA perform EHA analysis save harmonic
57. mean Remove mean from data series T or F detrend Remove linear trend from data series T or F genplot Generate summary plots T or F verbose Verbose output T or F See Also cosTaper dpssTaper and gausTaper headn List Column Numbers for Each Variable Description Execute head function with column numbers indicated for each variable useful for functions such as autopl Usage headn dat Arguments dat Your data frame hilbert 31 hilbert Hilbert Transform of Stratigraphic Series Description Calculate instantaneous amplitude envelope via Hilbert Transform of stratigraphic series Usage hilbert dat padfac 2 demean T detrend F output T addmean F genplot T verbose T Arguments dat padfac demean detrend output addmean genplot verbose Examples Stratigraphic series to Hilbert Transform First column should be location e g depth second column should be data value Pad with zeros to padfac npts points where npts is the original number of data points Remove mean from data series T or F Remove linear trend from data series T or F Return results as new data frame T or F Add mean value to instantaneous amplitude T or F Generate summary plots T or F Verbose output T or F generate example series with 3 precession terms and noise ex lt cycles noisevar 0004 dt 5 tt bandpass precession terms using cosine tapered window res_ex lt
58. min xmax pl 1 sigID F genplot T verbose T Arguments dat Stratigraphic series for LOWSPEC First column should be location e g depth second column should be data value decimate Decimate statigraphic series to have this sampling interval via piecewise linear interpolation By default no decimation is performed tbw MTM time bandwidth product 2 or 3 permitted padfac Pad with zeros to padfac npts points where npts is the original number of data points detrend Remove linear trend from data series This detrending is performed following ART prewhitening T or F siglevel Significance level for peak identification setrho Define AR1 coefficient for pre whitening otherwise calculated If set to 0 no pre whitening is applied lowspan Span for LOWESS smoothing of prewhitened signal usually fixed to 1 If using value lt 1 the method is overly conservative with a reduced false positive rate lowspec 39 b_tun Robustness weight parameter for LOWSPEC By default this will be estimated internally output What should be returned as a data frame O nothing 1 spectrum background CLs 2 sig peaks CLpwr Plot LOWSPEC noise confidence levels on power spectrum T or F xmin Smallest frequency for plotting xmax Largest frequency for plotting pl Plot logarithm of spectral power 1 or linear spectral power 2 sigID Identify signficant frequencies on power and probabilty plots T or F genplot Generate summary plo
59. n age calculations including estimation of age uncertainties mean square weighted deviation and probability of fit following the approaches used in IsoPlot 3 70 Ludwig 2008 and ArArCALC Koppers 2002 The function accepts input in three formats 1 each date and its uncertainty can be entered as individual vectors dat and sd 2 a two column matrix can be input as dat with each date first column and its uncertainty second column 3 a six column matrix can be input as dat with each date its uncertainty the associated K Ca value Ar40 E and F uncertainty one or two sigma This option must be used if you wish to calculate and include the uncertainty associated with J The uncertainty is calculated and propagated following equation 18 of Koppers 2002 The following plots are produced 1 A normal Q Q plot for the dates in essence this is the same as IsoPlot s linearized probability plot 2 A cumulative Gaussian plot for the dates a k a cumulative probability plot This is derived by summing the individual normal distributions for each date 3 A plot of each date with its 2 sigma uncertainties In addition K Ca and Ar40 data are plotted if provided He xplot References A A P Koppers 2002 ArArCALC software for 40Ar 39Ar age calculations Computers amp Geo sciences v 28 p 605 619 K R Ludwig 2008 User s Manual for Isoplot 3 70 A Geochronological Toolkit for Microsoft Excel B
60. nplot T verbose T Arguments spec threshold pick fmin fmax dmin dmax xmin xmax ymin ymax h w ydir ncolors genplot verbose See Also Time frequency spectral results to evaluate Must have the following format column 1 frequency remaining columns 2 to n power amplitude or proba bility titles for columns 2 to n must be the location depth or height Note that this format is ouput by function eha Threshold level for filtering peaks By default all peak maxima reported Pick the peaks of interest using a graphical interface T or F Only activated if genplot T Minimum frequency for analysis Maximum frequency for analysis Minimum depth height for analysis NOT ACTIVATED YET Maximum depth height for analysis NOT ACTIVATED YET Minimum frequency for PLOTTING Maximum frequency for PLOTTING Minimum depth height for PLOTTING Maximum depth height for PLOTTING Height of plot in inches Width of plot in inches Direction for y axis in plots depth or height 1 values increase downwards slower plotting 1 values increase upwards Number of colors to use in plot Generate summary plots T or F Verbose output T or F eha and traceFreq trim 71 Examples Check to see if this is an interactive R session for compliance with CRAN standards YOU CAN SKIP THE FOLLOWING LINE IF YOU ARE USING AN INTERACTIVE SESSION if interactive Generate example series with 3 t
61. nplot T verbose T Arguments dat Stratigraphic series for tapering First column should be location e g depth second column should be data value If no data is identified will output a 256 point taper to evaluate the spectral properties of the window p Cosine tapered window parameter p is the percent of the data series tapered choose 0 1 When p 1 this is equivalent to a Hann taper rms Normalize taper to RMS 1 to preserve power for white process T or E demean Remove mean from data series T or F detrend Remove linear trend from data series T or F genplot Generate summary plots T or F verbose Verbose output T or F See Also dpssTaper gausTaper and hannTaper cycles Generate Harmonic Model Description Make a time series with specified harmonic components and noise Usage cycles freqs NULL phase NULL amp NULL start 0 end 499 dt 1 noisevar 0 genplot T verbose T delPts Arguments freqs phase amp start end dt noisevar genplot verbose Value 17 Vector with frequencies to model linear frequencies Vector with phases for each frequency phase in radians Phases are subtracted Vector with amplitudes for each frequency First time depth height for output Last time depth height for output Sampling interval Variance of additive Gaussian noise Generate summary plots T or F Verbose output T or F modeled time series Examples test signal on pg 38 of
62. nterpolate the tuned series modelATunedInterp linterp modelATuned Perform Evolutive Harmonic Analysis on the tuned series eha modelATunedInterp anchorTime Anchor a floating astrochronology to a radioisotopic age Description Anchor a floating astrochronology to a radioisotopic age The floating astrochronology is centered on a given floating time datum and assigned the anchored age Usage anchorTime dat time age timeDir 1 flipOut F verbose T genplot T Arguments dat Stratigraphic series First column should be location e g depth second col umn should be data value time Floating time datum to center record on Units should be ka age Radioisotopic age or othwerwise for anchoring at floating time datum Units should be ka timeDir Direction of floating time in input record 1 elapsed time towards present 2 elapsed time away from present flipOut Flip the output sort so the ages are presented in decreasing order T or F genplot Generate summary plots T or F verbose Verbose output T or F 6 arl ar Generate AR 1 surrogates Description Generate AR 1 surrogates Implement shuffling algorithm of Meyers 2012 if desired Usage ar1 npts 1024 dt 1 mean 0 sdev 1 rho 0 9 shuffle F nsim 1 genplot verbose Arguments npts number of time series data points dt sampling interval mean mean value for ARI surrogate series sdev standard deviati
63. on for AR1 surrogate series rho AR 1 coefficient shuffle Apply secondary shuffle of Gaussian deviates before AR modeling nsim Number of ARI surrogate series to generate genplot generate summary plots T or F verbose verbose output T or F Details These simulations use the random number generator of Matsumoto and Nishimura 1998 If shuffle T the algorithm from Meyers 2012 pg 11 is applied 1 two sets of random sequences of same the length are generated 2 the first random sequence is then sorted and finally 3 the permutation vector of the sorted sequence is used to reorder the second random number sequence This is done to guard against potential shortcomings in random number generation that are specific to spectral estimation References S R Meyers 2012 Seeing red in cyclic stratigraphy Spectral noise estimation for astrochronology Paleoceanography v 27 PA3328 arletp 7 arletp AR 1 ETP simulation Routine Description Simulate a combined AR 1 ETP signal plot spectrum and confidence levels Usage arletp etpdat NULL nsim 100 rho 0 9 wtAR 1 sig 90 tbw 2 padfac 5 ftest F fmax 0 1 speed 0 5 p1 2 graphfile 0 Arguments etpdat Eccentricity tilt precession astronmical series First column time second column ETP If not entered will use default series from Laskar et al 2004 spanning 0 1000 kyr nsim Number of simulations rho AR 1 coefficient for noise modeling wtAR Multiplicative fact
64. or for ART noise 1 eqivalent to ETP variance sig Demarcate what confidence level percent on plots tbw MTM time bandwidth product padfac Pad with zeros to padfac npts points where npts is the number of data points ftest Include MTM harmonic f test results T or F fmax Maximum frequency for plotting speed Set the amount of time to pause before plotting new graph in seconds pl Plot log power 1 or linear power 2 graphfile Output a pdf or jpg image of each plot O no 1 pdf 2 jpeg If yes there will be no output to screen Individual graphic files will be produced for each simluation for assembling into a movie Details Note Setting wtAR 1 will provide equal variance contributions from the etp model and the arl model More generally set wtAR to the square root of the desired variance contribution wtAR 0 5 will generate an ART model with variance that is 25 of the etp model Note You may use the function etp to generate eccentricity tilt precession models References Laskar J Robutel P Joutel F Gastineau M Correia A C M Levrard B 2004 A long term numerical solution for the insolation quantities of the Earth Astron Astrophys Volume 428 261 285 8 arcsinT See Also getLaskar and etp Examples run simulations using the default settings arletp compare with a second model generate etp model spanning 0 2000 ka with sampling interval of 5 ka exl etp tmin 0 tmax 2000 dt 5
65. original red surrogate lines res2 1 res2 2 co1l red 1wd 2 1ty 4 taner 65 taner Apply Taner Bandpass or Lowpass Filter to Stratigraphic Series Description Apply Taner bandpass or lowpass filter to stratigraphic series Usage taner dat padfac 2 f low NULL fhigh NULL rol1 10 3 demean T detrend F addmean T output 1 xmin xmax Nyq genplot T verbose T Arguments dat Stratigraphic series for bandpass filtering First column should be location e g depth second column should be data value padfac Pad with zeros to padfac npts points where npts is the original number of data points flow Lowest frequency cut off half power point If this value is not set NULL it will default to 1 fhigh which will create a lowpass filter fhigh Highest frequency cut off half power point roll Roll off rate in dB octave Typical values are 1043 to 10112 but can be larger demean Remove mean from data series T or F detrend Remove linear trend from data series T or F addmean Add mean value to bandpass result T or F output Output 1 filtered series 2 bandpass filter window xmin Smallest frequency for plotting xmax Largest frequency for plotting genplot Generate summary plots T or F verbose Verbose output T or F Value bandpassed stratigraphic series References http www rocksolidimages com pdf attrib_revisited htm _Toc328470897 See Also bandpass lowpass noKernel no
66. ot T 10 Arguments freq target fper rayleigh nyquist sedmin sedmax numsed linLog iter output genplot Details asm A vector of candidate astronomical cycles observed in your data spectrum cy cles m Maximum allowed is 500 A vector of astronomical frequencies to evaluate 1 ka These must be in order of increasing frequency e g el e2 e3 01 02 p1 p2 Maximum allowed is 50 frequencies A vector of uncertainties on each target frequency 1 ka Values should be from 0 1 representing uncertainty as a percent of each target frequency The order of the uncertainties must follow that of the target vector By default no uncertainty is assigned Rayleigh frequency cycles m Nyquist frequency cycles m Minimum sedimentation rate for investigation cm ka Maximum sedimentation rate for investigation cm ka Number of sedimentation rates to investigate in ASM optimization grid Maxi mum allowed is 500 Use linear or logarithmic scaling for sedimentation rate grid spacing O linear 1 log Number of Monte Carlo simulations for significance testing Maximum allowed is 100 000 Return output as a new data frame T or F Generate summary plots T or F This function will caculate the Average Spectral Misfit between a data spectrum and astronomical target spectrum following the approach outlined in Meyers and Sageman 2007 and the improve ments of Meyers et al 2012 Value A dat
67. pplied a rectangular window res periodogram ex output 1 padfac pad save the frequency grid and the power for plotting freq res 1 pwr_rect res 3 now compare with results obtained after applying four different tapers Hann cosine taper DPSS with a time bandwidth product of 1 and DPSS with a time bandwidth product of 3 50 periodogram pwr_hann periodogram hannTaper ex demean FALSE output 1 padfac pad 3 pwr_cos periodogram cosTaper ex p 3 demean FALSE output 1 padfac pad 3 pwr_dpss1 periodogram dpssTaper ex thbw 1 demean FALSE output 1 padfac pad 3 pwr_dpss3 periodogram dpssTaper ex tbw 3 demean FALSE output 1 padfac pad 3 now plot the results ymin min rbind log pwr_rect 11 log pwr_hann 11 log pwr_cos 1 log pwr_dpss1 1 log pwr_dpss3 1 ymax max rbind log pwr_rectL 1 log pwr_hannL 1 log pwr_cos 1 log pwr_dpss1 1 log pwr_dpss3 1 p1 2 plot freqL 1 log pwr_rectL 1 type 1 ylim c ymin ymax lwd 2 ylab log Power xlab Frequency cycles ka main Comparison of rectangle black 30 cosine blue and Hann orange taper cex main 1 lines freqL 1 log pwr_hannL 1 col orange lwd 2 lines fregL 1 log pwr_cos 1 col blue points c 1 100 1 40 1 29 1 21 1 19 1 14 1 10 1 5 1 4 1 3 rep ymax 10 cex 5 col purple plot freqL 1 log pwr_rectL 1 type 1 ylim c ymin ymax lwd 2 ylab log Power
68. rguments n Number of plots per page 1 25 When specified this parameter takes prece dence and the default settings for r and c are used the r and c options below are ignored r Number of rows of plots c Number of columns of plots 52 plotEha h Height of new page a k a device w Width of new page a k a device mar A numerical vector of the form c bottom left top right which gives the margin size specified in inches file PDF file name in quotes If a file name is not designated then the plot is output to the screen instead title Plot title must be in quotes plotEha Create color time frequency plots from eha results Description Create color time frequency plots from eha results Usage plotEha spec xmin NULL xmax NULL ymin NULL ymax NULL h 6 w 4 ydir 1 p1 0 norm NULL Arguments spec xmin xmax ymin ymax ydir pl norm xaxis yaxis xaxis c Frequency cycles ka yaxis c Time ka ncolors 100 colorscale F filetype 0 output T verbose T Time frequency spectral results to evaluate Must have the following format column 1 frequency remaining columns 2 to n power amplitude or proba bility titles for columns 2 to n must be the location depth or height Note that this format is ouput by function eha Minimum frequency for PLOTTING Maximum frequency for PLOTTING Minimum depth height for PLOTTING Maximum depth height for PLOTTING Height of plot in in
69. ries e g convert stratigraphic depth series to height series relative to a defined datum Usage flip dat begin 0 genplot T verbose T Arguments dat Stratigraphic series First column should be location e g depth second col umn should be data value begin Depth height value to assign to new first stratigraphic datum genplot Generate summary plots T or F verbose Verbose output T or F freq2sedrate Convert record of local spatial frequency from EHA to sedimentation rate curve Description Convert record of local spatial frequency from EHA to sedimentation rate curve Usage freq2sedrate freqs period NULL ydir 1 genplot T verbose T Arguments freqs Data frame containing depth height in first column meters and spatial frequen cies in second column cycles m period Temporal period of spatial frequency ka ydir Direction for y axis in plots depth height 1 values increase downwards slower 1 values increase upwards genplot Generate summary plots T or F verbose Verbose output T or F 28 getColor gausTaper Apply Gaussian Taper to Stratigraphic Series Description Apply a Gaussian taper to a stratigraphic series Usage gausTaper dat alpha 3 rms T demean T detrend F genplot T verbose T Arguments dat Stratigraphic series for tapering First column should be location e g depth second column should be data value If no data is identified will output a 256 point taper to
70. s 100 genplot T verbose T Arguments spec Time frequency spectral results to evaluate or alternatively eAsm results to evaluate For time frequency results must have the following format column 1 frequency remaining columns 2 to n power amplitude or probability titles for columns 2 to n must be the location depth or height Note that this format is ouput by function eha For eAsm results must have the following format column sedimentation rate remaining columns 2 to n Ho SL or ASM titles for columns 2 to n must be the location depth or height get Record to extract height depth time If no value given graphical interface is activated xmin Minimum frequency or sedimentation rate for PLOTTING xmax Maximum frequency or sedimentation rate for PLOTTING ymin Minimum depth height for PLOTTING ymax Maximum depth height for PLOTTING h Height of plot in inches w Width of plot in inches ydir Direction for y axis in plots depth or height 1 values increase downwards slower plotting 1 values increase upwards pl An option for the color plots 0 do nothing 1 plot log of value useful for plot ting power 2 normalize to maximum value useful for plotting amplitude ncolors Number of colors to use in plot genplot Generate summary plots T or F verbose Verbose output T or F See Also eha flip 27 flip Flip stratigraphic series Description Flip the stratigraphic order of your data se
71. sage detrend dat genplot T verbose T divTrend 19 Arguments dat Stratigraphic series for linear detrending First column should be location e g depth second column should be data value genplot Generate summary plots T or F verbose Verbose output T or F See Also arcsinT demean divTrend logT prewhiteAR and prewhiteAR1 divTrend Divide by Linear Trend in Stratigraphic Series Description Divide data series value by linear trend observed in stratigraphic series Usage divTrend dat genplot T verbose T Arguments dat Stratigraphic series for div trending First column should be location e g depth second column should be data value genplot Generate summary plots T or F verbose Verbose output T or F See Also arcsinT demean detrend logT prewhiteAR and prewhiteAR1 dpssTaper Apply DPSS Taper to Stratigraphic Series Description Apply a single Discrete Prolate Spheroidal Sequence DPSS taper to a stratigraphic series Usage dpssTaper dat tbw 1 num 1 rms T demean T detrend F genplot T verbose T 20 eAsm Arguments dat Stratigraphic series for tapering First column should be location e g depth second column should be data value If no data is identified will output a 256 point taper to evaluate the spectral properties of the window tbw Time bandwidth product for the DPSS num Which one of the DPSS would you like to use rms Normalize taper to RMS 1 to pres
72. sociated with each date in dat as one or two sigma This option is ignored if dat has more than one column wtMean 75 unc What is the uncertainty on your input dates 1 one sigma or 2 2 sigma DEFAULT is one sigma This also applies to the F uncertainty and the J value uncertainty if specified lambda Total decay constant of K40 in units of 1 year The default value is 5 463e 10 year Min et al 2000 d Neutron fluence parameter Jsd Uncertainty for J value neutron fluence parameter as one or two sigma CI Which convention would you like to use for the 95 confidence intervals 1 ISOPLOT Ludwig 2008 2 ArArCALC Koppers 2002 cull Would you like to remove one or more dates using the graphical interface T or F del A vector of indices indicating dates to remove from weighted mean calculation If specified this takes precedence over cull sort Sort dates O no 1 sort into increasing order 2 sort into decreasing order output Return weighted mean results as new data frame T or F idPts Identify datum number on each point T or F size Multiplicative factor to increase or decrease size of symbols and fonts unit The time unit for your results 1 Ma 2 Ka setAr Set the Ar40 level to be illustrated on the plot The default is 95 color Color to use for symbols Default is black genplot Generate summary plots T or F verbose Verbose output T or F Details This function performs weighted mea
73. ssive AR filter Appropriate AR order can be au tomatically determined using the Akaike Information Criterion or alternatively the order may be predefined Usage prewhiteAR dat order 0 method mle aic T genplot T verbose T 54 prewhiteAR l Arguments dat Stratigraphic series for prewhitening First column should be location e g depth second column should be data value for prewhitening Series must have uniform sampling interval order AR order for prewhitening if aic F or alternatively the maximum AR order to investigate 1f aic T If order is set to lt 0 will evaluate up to maximum default order this varies based on method method Method for AR parameter estimation yule walker burg ols mle yw aic Select model using AIC if F will use order AIC is only strictly valid if method is mle genplot Generate summary plots T or F verbose Verbose output T or F References Akaike H 1974 A new look at the statistical model identification IEEE Trans Autom Control 19 716 723 doi 10 1109 TAC 1974 1100705 See Also ar arcsinT bandpass demean detrend divTrend logT lowpass noKernel and prewhiteAR1 prewhiteAR1 Prewhiten Stratigraphic Series with AR filter using Standard or Un biased Estimate of rho Description Prewhiten stratigraphic series using autoregressive 1 AR1 filter Rho can be estimated using the standard approach or following a bias correction Usage
74. t Arguments filename Desired filename in quotes result csv output Data frame to write to file 74 wtMean writeT Write Tab delimited File Description Write data frame as file with tab delimited values Usage writeT filename output Arguments filename Desired filename in quotes result tab output Data frame to write to file wtMean Ar Ar Geochronology calculate weighted mean age age uncertainty and other associated statistics plots with interactive graphics for data culling Description The wtMean function will calculate weighted mean age age uncertainty and other helpful statis tics plots with interactive graphics for data culling The function includes the option to generate results using the approach of IsoPlot 3 70 Ludwig 2008 or ArArCALC Koppers 2002 Usage wtMean dat sd NULL unc 1 lambda 5 463e 10 NULL Jsd NULL CI 2 cul1 T de1l NULL sort 1 output F idPts T size NULL unit 1 setAr 95 color black genplot T verbose T Arguments dat dat must contain one of the following 1 a vector of dates for weighted mean calculation 2 a matrix with two columns date and uncertainty one or two sigma or 3 a matrix with six columns as follows date date uncertainty one or two sigma K Ca Ar40 F and F uncertainty one or two sigma NOTE F is the ratio Ar40 Ar39K see Koppers 2002 See details for more information sd Vector of uncertainties as
75. tests must occur on a local power spectrum high which is parameterized as occurring above the local LOWSPEC background estimate See Meyers 2012 for futher information on the algorithm In this implementation the robustness criterion b in EQ 6 of Ruckstuhl et al 2001 has been optimized for 2 and 3 pi DPSS using a span of 1 By default the robustness criterion will be estimated Both b and the span can be expliclty set using parameters bh mun and lowspan Note that it is permissible to decrease lowspan from its default value but this will result in an overly conservative false positive rate However it may be necessary to reduce lowspan to provide an approporiate background fit for some stratigraphic data Another option is to decimate the data series prior to spectral estimation Value If option is selected a data frame containing the following is returned Frequency Prewhitened power LOWSPEC background LOWSPEC CL F test CL If option 2 is selected the significant frequencies are returned as described above A0 modelA References W S Cleveland 1979 Locally weighted regression and smoothing scatterplots Journal of the American Statistical Association v 74 p 829 836 S R Meyers 2012 Seeing Red in Cyclic Stratigraphy Spectral Noise Estimation for Astrochronol ogy Paleoceanography 27 PA3228 doi 10 1029 2012PA002307 A F Ruckstuhl M P Jacobson R W Fi
76. than this threshold genplot Generate summary plots T or F verbose Verbose output T or F See Also delPts idPts iso and trim trough Identify minima of troughs in series filter at desired threshold value Description Identify minima of troughs in any 1D or 2D series filter at desired threshold value Usage trough dat level genplot T verbose T Arguments dat 1 or 2 dimensional series If 2 dimesions first column should be location e g depth second column should be data value level Threshold level for filtering troughs By default all trough minima reported genplot Generate summary plots T or F verbose Verbose output T or F tune 73 Examples ex cycles genplot FALSE trough ex level 0 02 tune Tune Stratigraphic Series Description Tune stratigraphic series from space to time using specified control points Usage tune dat controlPts extrapolate F genplot T verbose T Arguments dat Stratigraphic series for tuning First column should be location e g depth second column should be data value controlPts Tuning control points A data frame or matrix containing two columns depth time extrapolate Extrapolate sedimentation rates above and below tuned interval T or F genplot Generate summary plots T or F verbose Verbose output T or F writeCSV Write CSV File Description Write data frame as file with comma separated values Usage writeCSV filename outpu
77. tratigraphic series for piecewise linear interpolation First column should be location e g depth second column should be data value dt New sampling interval start Start interpolating at what time depth height value By default the first value of the stratigraphic series will be used genplot Generate summary plots T or F verbose Verbose output T or F logT Log Transformation of Stratigraphic Series Description Log transformation of stratigraphic series Usage logT dat c 0 opt 1 genplot T verbose T lowpass 37 Arguments dat Stratigraphic series for log transformation First column should be location e g depth second column should be data value for transformation Cc Constant to add prior to log transformation Default 0 opt 1 use natural logarithm 2 use log10 Default 1 genplot Generate summary plots T or F verbose Verbose output T or F See Also arcsinT demean detrend divTrend prewhiteAR and prewhiteAR1 lowpass Lowpass Filter Stratigraphic Series Description Lowpass filter stratigraphic series using rectangular Gaussian or tapered cosine window cosine window is experimental Usage lowpass dat padfac 2 fcut NULL win 0 demean T detrend F addmean T alpha 3 p 25 xmin 0 xmax Nyq genplot T verbose T Arguments dat Stratigraphic series for lowpass filtering First column should be location e g depth second column should be data value padfac Pad with zeros
78. ts T or F verbose Verbose output T or F Details LOWSPEC is a robust method for spectral background estimation designed for the identification of potential astronomical signals that are imbedded in red noise Meyers 2012 The complete algoritm implemented here is as follows 1 initial pre whitening with ART filter default or other filter as appropriate e g see function prewhiteAR 2 power spectral estimation via the multitaper method Thomson 1982 3 robust locally weighted estimation of the spectral background using the LOWESS based Cleveland 1979 procedure of Ruckstuhl et al 2001 4 assignment of confidence levels using a Chi square distribution NOTE If you choose to pre whiten before running LOWSPEC rather than using the default ART pre whitening specify setrho 0 Candidiate astronomical cycles are subsequently idenitified via isolation of those frequencies that achieve the required e g 90 percent LOWSPEC confidence level and MTM harmonic F test con fidence level Allowance is made for the smoothing inherent in the MTM power spectral estimate as compared to the MTM harmonic spectrum That is an F test peak is reported if it achieves the required MTM harmonic confidence level while also achieving the required LOWSPEC confidence level within half the power spectrum bandwidth resolution One additional criterion is included to further reduce the false positive rate a requirement that significant F
79. ty for J value neutron fluence parameter as one or two sigma CI Which convention would you like to use for the 95 confidence intervals 1 ISOPLOT Ludwig 2008 2 ArArCALC Koppers 2002 cull Would you like to remove one or more dates using the graphical interface T or F del A vector of indices indicating dates to remove from weighted mean calculation If specified this takes precedence over cull output Return weighted mean results as new data frame T or F idPts Identify datum number on each point T or F size Multiplicative factor to increase or decrease size of symbols and fonts The default is 1 4 unit The time unit for your results 1 Ma 2 Ka setAr Set the Ar40 level to be illustrated on the plot The default is 95 color Color to use for symbols Default is black genplot Generate summary plots T or F verbose Verbose output T or F 62 stepHeat Details This function performs weighted mean age calculations including estimation of age uncertainties mean square weighted deviation and probability of fit The following plots are produced 1 Ar40 versus Ar39 released 2 K Ca versus Ar39 released 3 Ar Ar age spectrum with 2 sigma uncertainties for each step and weighted mean with 95 confidence interval in red If the J value and its uncertainty are input stepHeat will calculate and include the uncertainty associated with J The uncertainty is calculated and propagated followin
80. uencies and their probabilities only for one spectrum Plot logarithm of spectral power 1 or linear spectral power 2 Label for x axis Default Frequency Label for y axis Default Location Plotting options 0 no plots 1 power amplitude f test probability 2 data series power amplitude probability 3 data series power normalized ampli tude maximum in each window normalized to unity normalized amplitude fil tered at specified siglevel 4 data series normalized power maximum in each window normalized to unity normalized amplitude maximum in each window normalized to unity normalized amplitude filtered at specified siglevel Verbose output T or F 24 etp See Also extract trackFreq and traceFreq Examples as an example evaluate the modelA data modelA interpolate to even sampling interval of 0 075 m exl 1linterp modelA dt 0 075 perform EHA with a time bandwidth parameter of 2 using an 7 95 meter window 0 15 m step and pad to 1000 points set labels for plots optional eha ex1 tbw 2 win 7 95 step 0 15 pad 1000 xlab Frequency cycles m ylab Height m for comparison generate spectrum for entire record using time bandwidth parameter of 3 and pad to 5000 points start by making a new plot pl 1 eha ex1 tbw 3 win 38 pad 5000 xlab Frequency cycles m gt etp Generate Eccentricity Tilt Precession Models Description Calculate eccentr
81. umn must be location e g depth xmin Minimum depth height time for isolation If xmin is not specified it will be selected using a graphical interface xmax Maximum depth height time for isolation If xmax is not specified it will be selected using a graphical interface col If you are using the graphical interface to select xmin xmax which column would you like to plot default 2 logx Plot x axis using logarithmic scaling T or F logy Plot y axis using logarithmic scaling T or F genplot Generate summary plots T or F verbose Verbose output T or F See Also delPts idPts trim and trimAT linage 35 linage Tune stratigraphic series to an astronomical target using graphical interface Description Tune stratigraphic series to an astronomical target using graphical interface similar to Analyseries Linage routine Paillard et al 1996 Usage linage dat target extrapolate F xmin NULL xmax NULL tmin NULL tmax NULL size 1 plotype 1 output 1 genplot T Arguments dat Stratigraphic series for tuning with two columns First column is depth height target Astronomical tuning target series First column is time extrapolate Extrapolate sedimentation rates above and below tuned interval T or F xmin Minimum height depth to plot xmax Maximum height depth to plot tmin Minimum time value to plot tmax Maximum time value to plot size Multiplicative factor to increase or decrease size of symbols and

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