R version 3.2.3 (2015-12-10) -- "Wooden Christmas-Tree"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(87.16,87.16,87.16,87.16,87.16,87.16,87.16,87.16,87.16,89.24,89.24,89.24,89.24,89.24,89.24,89.24,89.24,89.24,89.24,89.24,89.24,91,91,91,91,91,91,91,91,91,91,91,91,92.51,92.51,92.51,92.51,92.51,92.51,92.51,92.51,92.51,92.51,92.51,92.51,96.67,96.67,96.67,96.67,96.67,96.67,96.67,96.67,96.67,96.67,96.67,96.67,96.19,96.19,96.19,96.19,96.19,96.19,96.19,96.19,96.19,96.19,96.19,96.19,99.13,99.13,99.13,99.13,99.13,99.13,99.13,99.13,99.13,99.13,99.13,99.13,99.58,99.58,99.58,99.58,99.58,99.58,99.58,99.58,99.58,99.58,99.58,99.58,101.27,101.27,101.27,101.25,101.25,101.25,101.25,101.25,101.25,101.25,101.25,101.25,102.55,102.55,102.55,102.55,102.55,102.55,102.55,102.55,102.55,102.55,102.55,102.55,132.09,132.09,132.09)
> 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. 1.2.327 (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/18stb1457978557.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/2bltt1457978557.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/3x9q91457978557.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 -0.019086059 -0.019245110 -0.006997985 -0.007119324
 [6] -0.007278374 -0.007437425 -0.007596475 -0.007755526 -0.007067374
[11] -0.007197415 -0.007356466  0.050362019 -0.007674567 -0.007833617
[16] -0.007455077 -0.007604086 -0.007763137 -0.007922187 -0.008081238
[21] -0.008240288 -0.008317098 -0.007816979 -0.007976030  0.078021717
[26] -0.008302556 -0.008461607 -0.007974340 -0.008067786 -0.008226836
[31] -0.008385887 -0.008544937 -0.008703988 -0.008226942 -0.008385993
[36] -0.008545043  0.025239748 -0.008863144 -0.009022195 -0.008980970
[41] -0.009150731 -0.009309781 -0.009468832 -0.009627882 -0.009786933
[46] -0.008193561 -0.008352611 -0.008511662  0.100792242
> (mypacf <- c(rpacf$acf))
 [1] -0.019086059 -0.019616533 -0.007752712 -0.007791270 -0.007865530
 [6] -0.008090326 -0.008321389 -0.008558024 -0.007950419 -0.008131863
[11] -0.008344272  0.049374563 -0.006522566 -0.006705185 -0.007764924
[16] -0.007982743 -0.008206725 -0.008422059 -0.008641712 -0.008865121
[21] -0.009106254 -0.008718897 -0.008929021  0.074103046 -0.006119612
[26] -0.006267241 -0.007835344 -0.007987961 -0.008192648 -0.008377839
[31] -0.008569141 -0.008765030 -0.008467436 -0.008834247 -0.009026161
[36]  0.015761563 -0.008460384 -0.008643578 -0.009294781 -0.009530041
[41] -0.009740639 -0.009947862 -0.010158598 -0.010375735 -0.008936858
[46] -0.009315661 -0.009484183  0.090924589
> 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/4m4431457978557.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/5c6tt1457978557.tab") 
> 
> try(system("convert tmp/18stb1457978557.ps tmp/18stb1457978557.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bltt1457978557.ps tmp/2bltt1457978557.png",intern=TRUE))
character(0)
> try(system("convert tmp/3x9q91457978557.ps tmp/3x9q91457978557.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.276   0.229   1.511