R version 3.2.2 (2015-08-14) -- "Fire Safety"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(103.4,103.49,103.51,103.27,103.35,103.34,103.07,103.08,103.1,103.13,103.13,103.18,103.2,103.21,103,102.46,102.52,102.55,102.78,102.81,102.81,102.68,102.72,102.73,102.87,102.93,103.2,102.62,102.18,101.19,100.91,100.72,100.86,100.89,100.47,100.45,100.64,100.63,100.66,100.38,99.68,99.71,99.63,99.63,99.71,99.77,99.76,99.79,98.13,98.13,97.87,97.72,97.72,97.6,97.31,97.31,97.44,96.94,96.94,96.94)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '1'
> par1 = '48'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '12'
> par4 <- '0'
> par3 <- '1'
> 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/wessaorg/rcomp/tmp/1fbpy1445694522.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/2tofz1445694522.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/32jvj1445694522.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  0.057888233  0.042004338 -0.118828585 -0.183321479
 [6] -0.090757521  0.083959322 -0.106184796  0.055870846  0.111014554
[11] -0.064931538 -0.023199460 -0.018941731  0.031259355  0.133647206
[16] -0.042283171 -0.166981217 -0.004407557 -0.031210313  0.250051863
[21]  0.079112162  0.108677181 -0.148015231  0.002911805 -0.050425584
[26]  0.024382833 -0.030567240 -0.026959601 -0.014681066 -0.072417068
[31] -0.101834088 -0.058127258 -0.068494601  0.117884884  0.069050981
[36] -0.022311415 -0.012222515 -0.019763128 -0.044914816 -0.045698839
[41] -0.020528807 -0.056148434  0.084111193 -0.014812622 -0.053224525
[46]  0.042934424 -0.045562858 -0.045592824  0.013442965
> (mypacf <- c(rpacf$acf))
 [1]  0.057888233  0.038783255 -0.124012411 -0.174392843 -0.065684393
 [6]  0.098174886 -0.155041135  0.008431391  0.125497687 -0.090732141
[11] -0.062410557  0.010502856  0.099791267  0.090177545 -0.117963740
[16] -0.139879108  0.067705645  0.007174721  0.232668107 -0.005169339
[21]  0.106801391 -0.155855664  0.033756982  0.116746755  0.039986468
[26] -0.053715785 -0.117528596 -0.018098711 -0.056195094 -0.085894856
[31] -0.026703634 -0.153656292  0.007139193  0.006429430  0.005506367
[36]  0.033642952 -0.023353075 -0.149782184 -0.091882303 -0.025195818
[41]  0.077667067 -0.006007342 -0.060513633 -0.140848186  0.072348826
[46]  0.023873760 -0.057748427  0.011950008
> 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/487xr1445694522.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/573ui1445694522.tab") 
> 
> try(system("convert tmp/1fbpy1445694522.ps tmp/1fbpy1445694522.png",intern=TRUE))
character(0)
> try(system("convert tmp/2tofz1445694522.ps tmp/2tofz1445694522.png",intern=TRUE))
character(0)
> try(system("convert tmp/32jvj1445694522.ps tmp/32jvj1445694522.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.122   0.196   1.332