R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(1.94,1.82,1.8,1.79,1.79,1.78,1.81,1.84,1.87,1.87,1.87,1.84,1.82,1.83,1.83,1.82,1.83,1.87,1.88,1.9,1.98,2.03,2.14,2.42,2.73,2.84,2.85,2.94,3.06,3.24,3.18,3.01,2.87,2.73,2.63,2.39,2.26,2.11,2.01,1.99,1.96,1.93,1.98,2.07,2.24,2.31,2.23,2.26,2.28,2.3,2.33,2.26,2.24,2.47,2.55,2.89,3.21,3.21,2.92,2.68,2.4,2.28,2.24,2.2,2.18,2.23,2.24,2.25,2.23,2.25,2.23,2.21,2.17,2.17,2.13,2.12,2.13,2.17,2.33,2.5,2.57,2.59,2.58,2.31)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '1'
> par1 = '60'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '12'
> par4 <- '0'
> par3 <- '1'
> par2 <- '1'
> par1 <- '60'
> #'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/1w4ed1387830728.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/2r2rw1387830728.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/340781387830728.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  0.63020034  0.30479498  0.08051051 -0.07940183 -0.12077103
 [7] -0.11099583 -0.20575203 -0.18149004 -0.13866729 -0.12227164 -0.08213009
[13] -0.08237166 -0.10874052 -0.12144304 -0.13630309 -0.14400787 -0.18049276
[19] -0.23928521 -0.18035702 -0.10263408 -0.03888641  0.03643591  0.16396822
[25]  0.20702495  0.24578286  0.20764082  0.10808337  0.05700229  0.08953681
[31]  0.03570463  0.10048041  0.14038697  0.07022347 -0.01463245 -0.06516048
[37] -0.07054972 -0.05495833 -0.08700865 -0.08641761 -0.07094473 -0.07628535
[43] -0.06440935 -0.09160880 -0.08681312 -0.03712378 -0.02192540 -0.01566605
[49]  0.02627748  0.03905599  0.05887025  0.03721654  0.01104217  0.01066182
[55] -0.03005506  0.02736490  0.06178547  0.07883648  0.03186684 -0.02101420
[61] -0.04042328
> (mypacf <- c(rpacf$acf))
 [1]  6.302003e-01 -1.532021e-01 -7.550683e-02 -1.015256e-01  1.552149e-02
 [6] -1.613822e-02 -2.232412e-01  6.723578e-02 -3.409339e-02 -6.066736e-02
[11] -2.490228e-02 -8.051353e-02 -4.784324e-02 -1.021690e-01 -6.932743e-02
[16] -7.058367e-02 -1.744788e-01 -1.717040e-01  9.401325e-03 -7.797352e-02
[21] -9.649527e-02 -5.064122e-02  1.308577e-01 -5.916502e-02 -1.217179e-02
[26] -4.100735e-02 -8.150612e-02 -1.543306e-02  6.621355e-02 -1.015110e-01
[31]  1.502111e-01  2.952130e-02 -8.098886e-02 -9.701478e-02 -3.916530e-02
[36]  7.681481e-02 -7.571803e-02 -7.225000e-02  7.598701e-02 -5.462549e-03
[41] -8.950801e-03 -1.691919e-02 -3.785950e-02  4.135117e-02 -6.211550e-03
[46] -1.877513e-02  3.390025e-03 -1.821037e-02  6.579274e-02  4.170768e-02
[51] -8.452348e-02 -4.865199e-02  5.115338e-02 -1.341198e-01  8.927420e-02
[56] -7.152436e-02  5.798869e-02 -1.143766e-01  5.052503e-03 -3.358537e-05
> 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/42x381387830729.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/5kwar1387830729.tab") 
> 
> try(system("convert tmp/1w4ed1387830728.ps tmp/1w4ed1387830728.png",intern=TRUE))
character(0)
> try(system("convert tmp/2r2rw1387830728.ps tmp/2r2rw1387830728.png",intern=TRUE))
character(0)
> try(system("convert tmp/340781387830728.ps tmp/340781387830728.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.772   0.609   3.352