R version 3.0.1 (2013-05-16) -- "Good Sport"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(84728,84412,84092,83430,89981,89635,84728,81466,81781,81781,82133,82764,83746,83746,83115,81466,89981,91279,89319,84728,86692,83746,85075,85710,86372,84728,85075,82764,89981,92261,90301,86692,90617,86372,90301,89981,90964,87355,91279,90964,96852,95523,90301,87670,91279,86372,89981,90617,91946,89004,90617,91599,95208,92261,88337,84092,88021,77221,82448,85390,88337,84092,84092,84092,86372,83115,78839,75261,77857,67724,73933,77541,78204,74595,74910,73933,77221,74910,70355,67062,72630,60537,68390,71968,71968,67724,63799,63484,67062,63799,57595,53319,57911,47115,56928,62150,63799,60191,55631,58893,60191,59208,49391,44835,48093,38280,48413,52022,54964,50057,45466,48093,49391,46795,36982,32706,36631,25835,37613,44835)
> 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. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., (2012), (Partial) Autocorrelation Function (v1.0.11) 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)
> 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/1u2f81375799372.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/2zmlt1375799372.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/3orks1375799372.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  0.94790238  0.90526697  0.85572849  0.82620581  0.78567941
 [7]  0.75796603  0.73618798  0.72365371  0.70318187  0.69210223  0.68757000
[13]  0.70013200  0.64795920  0.60883566  0.56351062  0.53636243  0.49797173
[19]  0.47383387  0.45269020  0.44000973  0.41829299  0.40339590  0.39332619
[25]  0.39690037  0.34807568  0.31176424  0.26871632  0.24336092  0.20637827
[31]  0.18441905  0.16033717  0.14528006  0.11903081  0.10228122  0.08819115
[37]  0.08556652  0.04215424  0.01252790 -0.02312078 -0.04234591 -0.07005467
[43] -0.08184491 -0.09661709 -0.10509802 -0.12495884 -0.13603215 -0.14763229
[49] -0.15225551
> (mypacf <- c(rpacf$acf))
 [1]  0.947902382  0.066495621 -0.082620819  0.161769416 -0.095014670
 [6]  0.075222997  0.103374747  0.049081778 -0.040350607  0.088274217
[11]  0.102395605  0.170866954 -0.617557544  0.140106752  0.079121083
[16] -0.101808100  0.062886467  0.038425175 -0.041022358  0.026522332
[21] -0.026510687  0.084745928 -0.085818557 -0.059760502 -0.132117620
[26]  0.009574504  0.006387182 -0.014106124 -0.004645646  0.005709566
[31] -0.091581278  0.033600279 -0.043553700  0.058511260 -0.071716936
[36] -0.075133877  0.014539862  0.007294533 -0.021743977  0.017285747
[41]  0.014243626  0.003859414 -0.029038517 -0.021886810  0.016662740
[46] -0.028074010 -0.067426230 -0.046564615
> 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/48ck91375799372.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/5jclj1375799372.tab") 
> 
> try(system("convert tmp/1u2f81375799372.ps tmp/1u2f81375799372.png",intern=TRUE))
character(0)
> try(system("convert tmp/2zmlt1375799372.ps tmp/2zmlt1375799372.png",intern=TRUE))
character(0)
> try(system("convert tmp/3orks1375799372.ps tmp/3orks1375799372.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.897   0.448   2.342