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(101.03,100.65,100.66,100.54,100.51,100.53,100.53,101.02,101.07,101.37,101.45,101.44,101.45,100.99,101.11,101.31,101.53,101.6,101.61,102.04,102.36,102.74,102.96,103.01,103.02,102.34,102.38,102.54,102.71,102.78,102.78,103.27,103.4,103.74,103.89,103.92,99.68,99.06,99.12,99.37,99.63,99.69,99.76,100.16,100.46,100.83,101.09,101.14,101.25,101.09,101.18,101.14,101.23,101.17,100.84,101.04,101.18,101.1,101.21,101.26,101.85,101.82,101.93,101.95,101.97,102.04,101.37,101.2,101.14,101.27,101.39,101.4)
> 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/1lfed1489759472.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/2vhgt1489759472.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/3vpjt1489759472.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  0.862927090  0.680334910  0.498963671  0.347495783
 [6]  0.218861251  0.112120036  0.030026382 -0.027242184 -0.055337024
[11] -0.054284414 -0.043763178 -0.069970575 -0.123099033 -0.195221007
[16] -0.258007512 -0.304992085 -0.329477164 -0.344190606 -0.363383555
[21] -0.367959428 -0.349809082 -0.320227955 -0.280187341 -0.256864148
[26] -0.223007723 -0.201044626 -0.173478003 -0.130288091 -0.082587416
[31] -0.020328923  0.012515753  0.037906051  0.060983713  0.089995715
[36]  0.125548283  0.150459312  0.146813058  0.138008646  0.127102415
[41]  0.115210457  0.103331866  0.079835309  0.058550452  0.038756307
[46]  0.023038588  0.014180404  0.008644978  0.012730447
> (mypacf <- c(rpacf$acf))
 [1]  0.862927090 -0.251836813 -0.077968839 -0.001665784 -0.053001602
 [6] -0.038479976 -0.013374558 -0.007086325  0.026326397  0.043596995
[11] -0.014910160 -0.169887293 -0.094717601 -0.105958903 -0.036897349
[16] -0.037955475 -0.013664757 -0.062037871 -0.111782885 -0.030323302
[21] -0.025060314 -0.064834645 -0.002431273 -0.098913431  0.046789590
[26] -0.089195184 -0.012639817  0.014784178 -0.025417915  0.074322170
[31] -0.129422437 -0.005558117 -0.015741160 -0.003475290  0.045124528
[36] -0.055604669 -0.070026356 -0.010128369 -0.035232153 -0.048738548
[41] -0.056542440 -0.049976594 -0.022904212 -0.012685952 -0.042054851
[46] -0.051500543 -0.026119079  0.005612124
> 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/40h3b1489759472.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/5ghu01489759472.tab") 
> 
> try(system("convert tmp/1lfed1489759472.ps tmp/1lfed1489759472.png",intern=TRUE))
character(0)
> try(system("convert tmp/2vhgt1489759472.ps tmp/2vhgt1489759472.png",intern=TRUE))
character(0)
> try(system("convert tmp/3vpjt1489759472.ps tmp/3vpjt1489759472.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.632   0.129   1.854