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)

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> x <- c(1.38,1.96,1.36,1.24,1.35,1.23,1.09,1.08,1.33,1.35,1.38,1.5,1.47,2.09,1.52,1.29,1.52,1.27,1.35,1.29,1.41,1.39,1.45,1.53,1.45,2.11,1.53,1.38,1.54,1.35,1.29,1.33,1.47,1.47,1.54,1.59,1.5,2,1.51,1.4,1.62,1.44,1.29,1.28,1.4,1.39,1.46,1.49,1.45,2.05,1.59,1.42,1.73,1.39,1.23,1.37,1.51,1.47,1.5,1.54,1.54,2.15,1.62,1.4,1.65,1.49,1.45,1.45,1.51,1.48,1.56,1.57)
> 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/fisher/rcomp/tmp/1q98v1353948278.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/fisher/rcomp/tmp/2m03q1353948278.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/fisher/rcomp/tmp/3g11a1353948278.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]  1.000000000 -0.395868285 -0.188152418  0.286350855 -0.133162161
 [6]  0.003098897 -0.139857016  0.010223437 -0.131378129  0.251313112
[11] -0.129984939 -0.336764081  0.796241831 -0.318952942 -0.161352753
[16]  0.268866116 -0.129195071  0.005257446 -0.108601432  0.012533573
[21] -0.094479761  0.174463051 -0.094317152 -0.271979386  0.624682837
[26] -0.245130939 -0.126574616  0.220282769 -0.088762797 -0.025767360
[31] -0.074656739  0.023400141 -0.076939392  0.119408587 -0.071363083
[36] -0.207697427  0.475976574 -0.180150658 -0.108425912  0.182065246
[41] -0.063006072 -0.020717587 -0.063520210  0.024164885 -0.051991699
[46]  0.075460281 -0.060014786 -0.129520847  0.326153848 -0.113299385
[51] -0.087698241  0.123400229 -0.037990818 -0.020572533 -0.033234044
[56]  0.000746680 -0.010644225  0.032864052 -0.025252830 -0.065389339
[61]  0.154824789
> (mypacf <- c(rpacf$acf))
 [1] -0.395868285 -0.408951621  0.027790629 -0.056617231  0.038012810
 [6] -0.270384928 -0.225223259 -0.506812343 -0.004804536 -0.206960213
[11] -0.686730775  0.198045768 -0.066189340  0.003781051 -0.090662200
[16] -0.108237745 -0.123838198 -0.069103517 -0.070808615  0.078356516
[21] -0.051529551 -0.071749276 -0.094799923 -0.109886851 -0.053625856
[26]  0.032984620 -0.060598535  0.063348899 -0.126330495 -0.055471547
[31] -0.047638149  0.008830515  0.006522215  0.002403692 -0.036935276
[36] -0.044674140 -0.048460316 -0.045566039 -0.022476611 -0.083302217
[41]  0.004954396 -0.051878977 -0.061345315 -0.038474666 -0.054539566
[46] -0.075313181  0.075653500 -0.067719070  0.077192214  0.003894492
[51] -0.022860650 -0.059729956 -0.062190262  0.003324764 -0.005834534
[56]  0.006036369 -0.040069619  0.055441431  0.107749267 -0.031811798
> lengthx <- length(x)
> sqrtn <- sqrt(lengthx)
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/4c84n1353948278.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/fisher/rcomp/tmp/5zgwp1353948278.tab") 
> 
> try(system("convert tmp/1q98v1353948278.ps tmp/1q98v1353948278.png",intern=TRUE))
character(0)
> try(system("convert tmp/2m03q1353948278.ps tmp/2m03q1353948278.png",intern=TRUE))
character(0)
> try(system("convert tmp/3g11a1353948278.ps tmp/3g11a1353948278.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.833   0.452   2.269