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(1.26,1.26,1.28,1.34,1.39,1.47,1.57,1.63,1.72,1.43,1.35,1.41,1.44,1.43,1.43,1.42,1.45,1.51,1.48,1.48,1.45,1.38,1.46,1.45,1.41,1.45,1.47,1.47,1.53,1.56,1.66,1.79,1.78,1.46,1.41,1.43,1.43,1.45,1.35,1.35,1.29,1.29,1.26,1.3,1.3,1.16,1.24,1.15,1.21,1.22,1.17,1.13,1.15,1.2,1.23,1.25,1.38,1.28,1.26,1.25,1.26,1.28,1.31,1.22,1.23,1.36,1.54,1.58,1.44,1.29,1.28,1.23,1.2,1.22,1.19,1.17,1.22,1.29,1.39,1.53,1.82,1.77,1.63,1.57)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '0'
> par2 = '1'
> par1 = '60'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '12'
> par4 <- '0'
> par3 <- '0'
> 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/155so1388412504.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/22jvy1388412504.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/39gs61388412504.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  0.841597538  0.616456536  0.418558236  0.271257866
 [6]  0.187602635  0.121761811  0.071981471  0.050266639  0.079261914
[11]  0.102967241  0.131553599  0.176268962  0.172209391  0.142410163
[16]  0.060779624 -0.025392103 -0.066850986 -0.083302947 -0.104431481
[21] -0.114039059 -0.104730366 -0.074545917 -0.006445486  0.051948698
[26] -0.006343743 -0.085206358 -0.158755264 -0.224427549 -0.245426776
[31] -0.256754015 -0.268564771 -0.283302045 -0.269172554 -0.227904249
[36] -0.187564296 -0.161318158 -0.181592387 -0.210079765 -0.236415357
[41] -0.252020750 -0.250693672 -0.240987114 -0.228866842 -0.204980817
[46] -0.172079835 -0.131666939 -0.051305081  0.075982192  0.151384552
[51]  0.167216020  0.137140498  0.078812138  0.037059830  0.013041249
[56] -0.019034650 -0.043747284 -0.024568224  0.019969205  0.054968926
[61]  0.068438071
> (mypacf <- c(rpacf$acf))
 [1]  0.841597538 -0.314794666  0.005183526  0.002508012  0.065603525
 [6] -0.080467354  0.011994404  0.047731243  0.141420393 -0.080145014
[11]  0.095668179  0.101272466 -0.135003692 -0.007560726 -0.163543219
[16]  0.029727600  0.050738851 -0.046731456 -0.080464071  0.068739472
[21] -0.023120792  0.053780427  0.079475911 -0.023287187 -0.356170363
[26]  0.073406527 -0.021593596 -0.064949998  0.019051722 -0.047597809
[31] -0.045780400 -0.112713922  0.056371532  0.085273295 -0.144053970
[36] -0.144818083 -0.027854825 -0.014468466  0.078325995 -0.059554251
[41] -0.074225548  0.006106236 -0.048335693  0.026778656  0.045906077
[46] -0.007065707  0.036497007  0.208486623  0.010778222 -0.046050334
[51] -0.040183976 -0.035907334 -0.070762252  0.019843822 -0.024316754
[56]  0.073320353  0.007308801 -0.060153126 -0.007962196 -0.096247197
> 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/4ptfs1388412504.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/5xm3j1388412504.tab") 
> 
> try(system("convert tmp/155so1388412504.ps tmp/155so1388412504.png",intern=TRUE))
character(0)
> try(system("convert tmp/22jvy1388412504.ps tmp/22jvy1388412504.png",intern=TRUE))
character(0)
> try(system("convert tmp/39gs61388412504.ps tmp/39gs61388412504.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  3.962   1.029   4.948