R version 3.3.3 (2017-03-06) -- "Another Canoe"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(86.88,90.65,90.68,89.64,102.62,101.84,92.51,94.29,94.68,96.94,94.03,89.65,84.9,89.07,89.8,93.22,92.23,98.41,96.63,89.8,90,92.13,93.27,90.81,85.42,88.28,88.73,90.18,92.74,96.13,94.85,94.25,96.94,101.22,98.71,95.51,93.91,98.17,97.59,99.64,107.88,108.49,100.25,99.27,101.73,101.25,97.09,94.74,94.53,93.48,96.05,106.22,98.33,99.86,93.78,88.96,83.77,89.46,86.78,88.4,87.19,92.23,95.99,104.75,105.63,108.71,96.4,93.31,93.77,98.7,95.04,95.61)
> 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. 1.2.327 (Mon, 30 Nov 2015 06:58:35 +0000)
> #Author: root
> #To cite this work: Wessa P., (2015), (Partial) Autocorrelation Function (v1.0.12) 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)
> x <- na.omit(x)
> 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/1b3v51489860838.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/2ablm1489860838.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/3ype31489860838.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  0.682836000  0.388645094  0.185712024  0.085354045
 [6] -0.021722566 -0.011636892 -0.073649836 -0.064347448 -0.077353637
[11]  0.074565930  0.187932294  0.230322435  0.085480539 -0.090455125
[16] -0.285355258 -0.296161281 -0.265663970 -0.165744552 -0.149330600
[21] -0.132968963 -0.109028771 -0.015695449  0.090734915  0.141403747
[26]  0.033516557 -0.090169882 -0.148327455 -0.084069767 -0.048317905
[31]  0.009997684  0.039721346  0.052422013  0.047348785  0.075620451
[36]  0.123748872  0.146549886 -0.016583878 -0.153233795 -0.204279366
[41] -0.166693611 -0.135159568 -0.052868812 -0.026229472 -0.007352992
[46] -0.005634185  0.094497270  0.121017928  0.060496382
> (mypacf <- c(rpacf$acf))
 [1]  0.682836000 -0.145427806 -0.036289338  0.021753129 -0.116999960
 [6]  0.124534605 -0.173551726  0.088706346 -0.066911872  0.280784348
[11]  0.053874575 -0.023504060 -0.199161591 -0.217657087 -0.149054019
[16]  0.045279550  0.032501065  0.082921752 -0.041403310 -0.069270810
[21] -0.010763504 -0.061296296  0.135087658 -0.054251538 -0.025007237
[26] -0.038878409  0.076316294  0.099055337 -0.146726740  0.014488267
[31] -0.025816426  0.063054423  0.033535164 -0.083167158  0.060111162
[36] -0.008840884 -0.229172255  0.004743229 -0.064945675  0.068733067
[41] -0.081295546  0.055426527  0.041037318 -0.039087619  0.057093754
[46]  0.016040947 -0.039623987 -0.078869946
> 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/41jc91489860838.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/5x59s1489860838.tab") 
> 
> try(system("convert tmp/1b3v51489860838.ps tmp/1b3v51489860838.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ablm1489860838.ps tmp/2ablm1489860838.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ype31489860838.ps tmp/3ype31489860838.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.262   0.085   1.367