R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(58109,57087,56064,54019,74712,73689,58109,47763,48785,48785,49808,51964,45718,39462,34339,34339,54019,56064,40484,22859,32183,32183,39462,43663,42640,32183,37417,35362,52987,48785,32183,19782,31160,34339,37417,41507,33205,26038,29116,30138,57087,57087,41507,39462,45718,42640,50942,61288,63343,48785,44685,40484,68567,70622,65388,70622,69589,61288,70622,80968,85169,72667,64365,70622,97570,105872,103827,107916,106894,96548,114173,118374,124519,105872,98593,106894,126675,144300,140099,140099,142154,134976,153634,153634,150455,132820,135999,138054,151579,169204,156701,162958,157724,154656,178538,173304,166025,155679,166025,171259,177505,185806,177505,182628,176381,175359,201285,203441,195140,180583,192984,198208,204464,213788,204464,211743,208564,197185,221066,221066)
> 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/13wfx1471215528.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/2efp11471215528.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/3pk3w1471215528.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  0.01881828 -0.30195234 -0.30955479 -0.14099170  0.19083469
 [7]  0.30091133  0.17589409 -0.08905763 -0.33219858 -0.28608822  0.06521247
[13]  0.79803265  0.00369085 -0.25309139 -0.24486040 -0.11794744  0.12776409
[19]  0.27374993  0.15246302 -0.08704546 -0.32578163 -0.18867893  0.07985126
[25]  0.60559535 -0.04128901 -0.18974569 -0.14488892 -0.14057077  0.03367686
[31]  0.28072477  0.15550902 -0.05002182 -0.33009076 -0.18060494  0.07727996
[37]  0.42438741 -0.02609713 -0.11571950 -0.06879341 -0.16347258 -0.05671958
[43]  0.27498031  0.18347099 -0.01688579 -0.31652204 -0.15994118  0.03816143
[49]  0.31764685
> (mypacf <- c(rpacf$acf))
 [1]  0.018818279 -0.302413556 -0.326420776 -0.304045228 -0.070775453
 [6]  0.095370385  0.211231716  0.185050591 -0.031133669 -0.240332248
[11] -0.235164223  0.676211614 -0.030315526  0.081388141  0.126620649
[16]  0.182845767 -0.102570367  0.015194218 -0.035660041 -0.122823871
[21] -0.095902718  0.175434862 -0.048515304 -0.064562185 -0.054794938
[26]  0.100363828  0.054765345 -0.125405313 -0.166679884  0.074833653
[31]  0.059926102  0.106191825 -0.059265034 -0.134827990 -0.082749138
[36] -0.121372609  0.009597764 -0.133515354 -0.005237135  0.023996983
[41]  0.085498133  0.061591513  0.090510724 -0.034850464 -0.019565635
[46]  0.039174458 -0.023386989 -0.016664556
> 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/4ernv1471215528.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/5jn1a1471215528.tab") 
> 
> try(system("convert tmp/13wfx1471215528.ps tmp/13wfx1471215528.png",intern=TRUE))
character(0)
> try(system("convert tmp/2efp11471215528.ps tmp/2efp11471215528.png",intern=TRUE))
character(0)
> try(system("convert tmp/3pk3w1471215528.ps tmp/3pk3w1471215528.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.169   0.102   1.285