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(96.86,96.89,96.9,96.94,96.88,96.89,96.89,96.95,97.03,97.29,97.37,97.41,97.41,97.32,97.33,97.38,97.47,97.5,97.5,97.58,97.7,97.9,97.98,98.03,98.03,97.94,98.12,98.19,98.34,98.42,98.43,98.45,98.77,99.24,99.46,99.54,99.55,99.24,99.43,99.47,99.57,99.62,99.64,99.75,99.85,100.28,100.52,100.57,100.57,100.27,100.27,100.18,100.16,100.18,100.18,100.59,100.69,101.06,101.15,101.16,101.16,100.81,100.94,101.13,101.29,101.34,101.35,101.7,102.05,102.48,102.66,102.72,102.73,102.18,102.22,102.37,102.53,102.61,102.62,103,103.17,103.52,103.69,103.73,99.57,99.09,99.14,99.36,99.6,99.65,99.8,100.15,100.45,100.89,101.13,101.17,101.21,101.1,101.17,101.11,101.2,101.15,100.92,101.1,101.22,101.25,101.39,101.43)
> 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/1ox7n1457987871.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/2a8qm1457987871.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/3di341457987871.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]  1.00000000  0.95621844  0.90344063  0.85094691  0.80466104  0.76356590
 [7]  0.72670311  0.69117815  0.65920302  0.63394895  0.61688493  0.60131896
[13]  0.57654427  0.54466061  0.50769478  0.47223621  0.44282065  0.42086480
[19]  0.40244943  0.38303724  0.36584947  0.35421498  0.34719577  0.34135024
[25]  0.32732099  0.28990620  0.24877508  0.20895690  0.17504598  0.14496174
[31]  0.12007685  0.09541291  0.07033317  0.04849408  0.02979016  0.01085597
[37] -0.01552571 -0.04679374 -0.08234910 -0.11424932 -0.13924038 -0.15993137
[43] -0.17838902 -0.19652882 -0.21402653 -0.22889297 -0.23930598 -0.24709846
[49] -0.26124658
> (mypacf <- c(rpacf$acf))
 [1]  0.956218437 -0.127420162 -0.013942578  0.043568342  0.021453267
 [6]  0.016489676 -0.009917386  0.024994246  0.059575756  0.071528823
[11] -0.003600275 -0.109257604 -0.059949612 -0.051792756  0.003391040
[16]  0.038290691  0.051347638  0.017780305 -0.026428564  0.013436980
[21]  0.036042081  0.025166528  0.009971600 -0.077462757 -0.241059345
[26] -0.001636691 -0.011153800  0.001751072 -0.015112011  0.027094987
[31] -0.024062378 -0.046110694 -0.017788704 -0.031569081 -0.020946687
[36] -0.050463381 -0.023761119 -0.044655596  0.007425212  0.010002181
[41] -0.039526806 -0.015550212 -0.008629108 -0.029217372 -0.025341602
[46]  0.007471238  0.013486747 -0.051904111
> 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/44pvf1457987871.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/5h1yg1457987871.tab") 
> 
> try(system("convert tmp/1ox7n1457987871.ps tmp/1ox7n1457987871.png",intern=TRUE))
character(0)
> try(system("convert tmp/2a8qm1457987871.ps tmp/2a8qm1457987871.png",intern=TRUE))
character(0)
> try(system("convert tmp/3di341457987871.ps tmp/3di341457987871.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.265   0.199   1.477