R version 2.15.3 (2013-03-01) -- "Security Blanket"
Copyright (C) 2013 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,-2,-5,-4,-6,-2,-2,-2,-2,2,1,-8,-1,1,-1,2,2,1,-1,-2,-2,-1,-8,-4,-6,-3,-3,-7,-9,-11,-13,-11,-9,-17,-22,-25,-20,-24,-24,-22,-19,-18,-17,-11,-11,-12,-10,-15,-15,-15,-13,-8,-13,-9,-7,-4,-4,-2,0,-2,-3,1,-2,-1,1,-3,-4,-9,-9,-7,-14,-12,-16,-20,-12,-12,-10,-10,-13,-16,-14,-17,-24,-25)
> 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/1dmav1369232631.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/2xprb1369232631.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/36fw01369232631.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 -0.164101788 -0.058431639  0.157056832 -0.077242096
 [6]  0.082776720 -0.066107957  0.021497509  0.015442365 -0.180523874
[11] -0.005249292  0.160168311 -0.045113737  0.063713222 -0.071552076
[16]  0.077604737 -0.041193611 -0.053330446  0.044827784 -0.001840683
[21]  0.039159964 -0.169021195 -0.116823537  0.120169895 -0.170951607
[26] -0.017349491  0.018303908 -0.114848260 -0.058707812 -0.148940253
[31]  0.047749095  0.038318097 -0.039982893  0.062453137  0.037624638
[36]  0.026698988  0.016507778  0.017062607  0.117952444 -0.099544525
[41] -0.054575317 -0.014051412 -0.090690245  0.033250746 -0.006266582
[46]  0.110974241  0.037543758 -0.182208075  0.192602649  0.091843214
[51] -0.068805411  0.094589258 -0.029698073 -0.038059622  0.031365198
[56] -0.013950351  0.047537347 -0.038559029 -0.102364464  0.088668396
[61] -0.024924437
> (mypacf <- c(rpacf$acf))
 [1] -0.164101788 -0.087723374  0.136944678 -0.034110977  0.087310085
 [6] -0.071610319  0.027743307 -0.013690864 -0.158503983 -0.083063181
[11]  0.152110738  0.044390027  0.091796879 -0.087041042  0.070573117
[16] -0.082509672 -0.020920649 -0.060606706  0.044894210  0.090335201
[21] -0.129058663 -0.211114046  0.033977389 -0.145305128 -0.014907841
[26] -0.076209518 -0.034374057 -0.130750131 -0.163549594 -0.128590536
[31] -0.039619577  0.061635136  0.089348831  0.045093009  0.092416855
[36] -0.026577564  0.002482939  0.034765822 -0.021342699  0.007895628
[41] -0.019556801 -0.104778377 -0.094408173 -0.024699875  0.100313636
[46]  0.013517300 -0.163809389  0.042450553  0.023326820 -0.105788780
[51] -0.084872565 -0.001026373 -0.051275018  0.064562098 -0.007661326
[56] -0.040255324 -0.041704840 -0.082661400  0.036525810  0.053121981
> 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/4iclf1369232631.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/5nfsg1369232631.tab") 
> 
> try(system("convert tmp/1dmav1369232631.ps tmp/1dmav1369232631.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xprb1369232631.ps tmp/2xprb1369232631.png",intern=TRUE))
character(0)
> try(system("convert tmp/36fw01369232631.ps tmp/36fw01369232631.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.040   0.460   2.472