R version 3.0.1 (2013-05-16) -- "Good Sport"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(67.66,68,68.02,68.11,68.41,68.4,68.4,68.55,68.54,68.99,68.97,68.98,68.98,68.94,69.21,69.21,69.67,69.66,69.66,69.66,69.77,70.32,70.34,70.38,70.38,70.29,70.42,70.29,70.59,70.64,70.64,70.68,70.78,70.9,71.04,71.15,71.15,71.15,71.07,71.17,71.24,71.23,71.23,71.23,71.24,71.28,71.52,71.52,71.52,71.6,71.61,71.78,71.66,71.86,71.86,71.82,71.8,72.22,72.51,72.56,72.56,72.78,72.88,73.05,73.02,73.08,73.08,73.24,73.82,74,74.37,74.38)
> 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/fisher/rcomp/tmp/1wrhi1369318768.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/2nyk01369318768.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/3nx731369318768.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  0.934627131  0.872729350  0.814454863  0.759432106
 [6]  0.718729598  0.676087760  0.631766797  0.586244736  0.538033043
[11]  0.499483721  0.460047961  0.422070241  0.378878314  0.333776909
[16]  0.296543134  0.265373272  0.241125775  0.213166540  0.183721803
[21]  0.155476999  0.125433201  0.107064680  0.087897585  0.069388788
[26]  0.049344117  0.025124722  0.006966326 -0.013760548 -0.030519324
[31] -0.047917975 -0.067414482 -0.087211366 -0.105216659 -0.120672714
[36] -0.135958525 -0.151229527 -0.168203266 -0.186055514 -0.203174952
[41] -0.217532347 -0.230455315 -0.244323607 -0.261094161 -0.277124092
[46] -0.288825672 -0.303239621 -0.312125460 -0.325467801
> (mypacf <- c(rpacf$acf))
 [1]  0.934627131 -0.006313834 -0.003721373 -0.005003515  0.083935618
 [6] -0.032988186 -0.033323406 -0.032676806 -0.039520160  0.042175375
[11] -0.033412206 -0.014736986 -0.069138057 -0.032239720  0.025933397
[16]  0.021279195  0.024482636 -0.050218325 -0.016370522 -0.007726890
[21] -0.028741528  0.053416289 -0.029430410 -0.006243319 -0.028935163
[26] -0.030795820  0.013131600 -0.047330205  0.004861453 -0.031000816
[31] -0.011925171 -0.033332788 -0.003081595 -0.012917075 -0.028660006
[36] -0.009132626 -0.035049530 -0.014970028 -0.031342186 -0.006036955
[41] -0.016123061 -0.034538536 -0.035341405 -0.025000151  0.011432870
[46] -0.057602508  0.008897484 -0.053868944
> 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/45yh81369318769.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/5wgig1369318769.tab") 
> 
> try(system("convert tmp/1wrhi1369318768.ps tmp/1wrhi1369318768.png",intern=TRUE))
character(0)
> try(system("convert tmp/2nyk01369318768.ps tmp/2nyk01369318768.png",intern=TRUE))
character(0)
> try(system("convert tmp/3nx731369318768.ps tmp/3nx731369318768.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.967   0.316   2.264