R version 2.15.2 (2012-10-26) -- "Trick or Treat" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- c(6.11,6.13,6.15,6.15,6.16,6.18,6.21,6.22,6.23,6.26,6.28,6.28,6.29,6.32,6.36,6.37,6.38,6.38,6.4,6.41,6.42,6.43,6.44,6.47,6.47,6.48,6.51,6.54,6.56,6.57,6.6,6.62,6.65,6.71,6.76,6.78,6.8,6.83,6.86,6.86,6.87,6.88,6.9,6.92,6.93,6.94,6.96,6.98,6.99,7.01,7.06,7.07,7.08,7.08,7.1,7.11,7.22,7.24,7.25,7.26,7.27,7.3,7.32,7.34,7.35,7.36,7.39,7.41,7.43,7.46,7.47,7.5) > par8 = '' > par7 = '0.95' > par6 = 'White Noise' > par5 = '12' > par4 = '0' > par3 = '0' > par2 = '1' > par1 = '48' > par8 <- '' > par7 <- '0.95' > par6 <- 'White Noise' > par5 <- '12' > par4 <- '0' > par3 <- '0' > par2 <- '1' > par1 <- '48' > #'GNU S' R Code compiled by R2WASP v. 1.2.291 () > #Author: root > #To cite this work: Wessa P., (2012), (Partial) Autocorrelation Function (v1.0.11) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_autocorrelation.wasp/ > #Source of accompanying publication: > # > if (par1 == 'Default') { + par1 = 10*log10(length(x)) + } else { + par1 <- as.numeric(par1) + } > par2 <- as.numeric(par2) > par3 <- as.numeric(par3) > par4 <- as.numeric(par4) > par5 <- as.numeric(par5) > if (par6 == 'White Noise') par6 <- 'white' else par6 <- 'ma' > par7 <- as.numeric(par7) > if (par8 != '') par8 <- as.numeric(par8) > ox <- x > if (par8 == '') { + if (par2 == 0) { + x <- log(x) + } else { + x <- (x ^ par2 - 1) / par2 + } + } else { + x <- log(x,base=par8) + } > if (par3 > 0) x <- diff(x,lag=1,difference=par3) > if (par4 > 0) x <- diff(x,lag=par5,difference=par4) > postscript(file="/var/wessaorg/rcomp/tmp/157nd1353081819.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > op <- par(mfrow=c(2,1)) > plot(ox,type='l',main='Original Time Series',xlab='time',ylab='value') > if (par8=='') { + mytitle <- paste('Working Time Series (lambda=',par2,', d=',par3,', D=',par4,')',sep='') + mysub <- paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') + } else { + mytitle <- paste('Working Time Series (base=',par8,', d=',par3,', D=',par4,')',sep='') + mysub <- paste('(base=',par8,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep='') + } > plot(x,type='l', main=mytitle,xlab='time',ylab='value') > par(op) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/2tyzn1353081819.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > racf <- acf(x, par1, main='Autocorrelation', xlab='time lag', ylab='ACF', ci.type=par6, ci=par7, sub=mysub) > dev.off() null device 1 > postscript(file="/var/wessaorg/rcomp/tmp/353171353081819.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > rpacf <- pacf(x,par1,main='Partial Autocorrelation',xlab='lags',ylab='PACF',sub=mysub) > dev.off() null device 1 > (myacf <- c(racf$acf)) [1] 1.00000000 0.95965658 0.91987373 0.87976090 0.83953278 0.79887130 [7] 0.75828934 0.71893984 0.67867593 0.63768050 0.59763525 0.55793987 [13] 0.51833593 0.47800046 0.43788763 0.39874090 0.35962994 0.32591004 [19] 0.29120814 0.25712644 0.22184806 0.18600514 0.14967717 0.11514623 [25] 0.08155752 0.04681984 0.01236443 -0.02070346 -0.05295236 -0.08507800 [31] -0.11700089 -0.14744879 -0.17747925 -0.20652373 -0.23377914 -0.25777448 [37] -0.28003244 -0.30125672 -0.32075952 -0.33673985 -0.34988152 -0.36156014 [43] -0.37239194 -0.38133528 -0.38951131 -0.39668943 -0.40216596 -0.40546778 [49] -0.40781146 > (mypacf <- c(rpacf$acf)) [1] 0.959656579 -0.013496433 -0.024849013 -0.022854719 -0.027361462 [6] -0.021661366 -0.007395153 -0.034270383 -0.033523314 -0.013052350 [11] -0.020515940 -0.024181268 -0.035131511 -0.025085180 -0.015663614 [16] -0.026814697 0.039985214 -0.037786524 -0.020302921 -0.042446821 [21] -0.036807902 -0.036909787 -0.006992590 -0.020257622 -0.046902032 [26] -0.029150786 -0.016670257 -0.023253148 -0.033528559 -0.033429605 [31] -0.018385035 -0.030878751 -0.019321381 -0.017188026 0.004012075 [36] -0.015743308 -0.021738363 -0.015181428 0.012337273 0.005275280 [41] -0.013303627 -0.020516037 -0.006877688 -0.018784196 -0.015676165 [46] -0.008052642 -0.001433410 -0.015812268 > lengthx <- length(x) > sqrtn <- sqrt(lengthx) > > #Note: the /var/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/rcomp/createtable") > > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Autocorrelation Function',4,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Time lag k',header=TRUE) > a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','ACF(k)','click here for more information about the Autocorrelation Function'),header=TRUE) > a<-table.element(a,'T-STAT',header=TRUE) > a<-table.element(a,'P-value',header=TRUE) > a<-table.row.end(a) > for (i in 2:(par1+1)) { + a<-table.row.start(a) + a<-table.element(a,i-1,header=TRUE) + a<-table.element(a,round(myacf[i],6)) + mytstat <- myacf[i]*sqrtn + a<-table.element(a,round(mytstat,4)) + a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/408uc1353081819.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Partial Autocorrelation Function',4,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Time lag k',header=TRUE) > a<-table.element(a,hyperlink('http://www.xycoon.com/basics.htm','PACF(k)','click here for more information about the Partial Autocorrelation Function'),header=TRUE) > a<-table.element(a,'T-STAT',header=TRUE) > a<-table.element(a,'P-value',header=TRUE) > a<-table.row.end(a) > for (i in 1:par1) { + a<-table.row.start(a) + a<-table.element(a,i,header=TRUE) + a<-table.element(a,round(mypacf[i],6)) + mytstat <- mypacf[i]*sqrtn + a<-table.element(a,round(mytstat,4)) + a<-table.element(a,round(1-pt(abs(mytstat),lengthx),6)) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/wessaorg/rcomp/tmp/560hs1353081819.tab") > > try(system("convert tmp/157nd1353081819.ps tmp/157nd1353081819.png",intern=TRUE)) character(0) > try(system("convert tmp/2tyzn1353081819.ps tmp/2tyzn1353081819.png",intern=TRUE)) character(0) > try(system("convert tmp/353171353081819.ps tmp/353171353081819.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.824 0.677 3.484