R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(125326,122716,116615,113719,110737,112093,143565,149946,149147,134339,122683,115614,116566,111272,104609,101802,94542,93051,124129,130374,123946,114971,105531,104919,104782,101281,94545,93248,84031,87486,115867,120327,117008,108811,104519,106758,109337,109078,108293,106534,99197,103493,130676,137448,134704,123725,118277,121225,120528,118240,112514,107304,100001,102082,130455,135574,132540,119920,112454,109415,109843,106365,102304,97968,92462,92286,120092,126656,124144,114045,108120,105698,111203,110030,104009,99772,96301,97680,121563,134210,133111,124527,117589,115699)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '0'
> par2 = '1'
> par1 = '60'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '12'
> par4 <- '0'
> par3 <- '0'
> par2 <- '1'
> par1 <- '60'
> #'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/fisher/rcomp/tmp/1we2x1384971977.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/fisher/rcomp/tmp/20fj21384971977.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/fisher/rcomp/tmp/30bex1384971977.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  0.737809894  0.310323200 -0.003512481 -0.112219854
 [6] -0.071197706 -0.028836612 -0.103952439 -0.201123466 -0.182134926
[11]  0.022641711  0.346223040  0.544241738  0.321426505 -0.036124761
[16] -0.286341495 -0.366992761 -0.320407515 -0.280667430 -0.330296776
[21] -0.398979564 -0.362652847 -0.166445010  0.134582102  0.328633698
[26]  0.183754326 -0.076159212 -0.250969978 -0.278713976 -0.197816909
[31] -0.125699437 -0.130508752 -0.158351421 -0.109830025  0.064810728
[36]  0.316000313  0.478677223  0.366060053  0.150882712 -0.006162268
[41] -0.051848983 -0.014243894  0.016412056 -0.018759917 -0.068200371
[46] -0.050628900  0.067304154  0.239577879  0.346780646  0.240481383
[51]  0.053058924 -0.093468460 -0.151474098 -0.136747048 -0.109415850
[56] -0.135257816 -0.166144384 -0.149457952 -0.059350663  0.066409508
[61]  0.153760470
> (mypacf <- c(rpacf$acf))
 [1]  0.737809894 -0.513655530  0.086165859  0.052700724  0.029884838
 [6] -0.117741228 -0.217237294  0.057936628  0.127256051  0.264466823
[11]  0.299503523 -0.012621311 -0.671630697  0.247387895 -0.057095996
[16] -0.237700164 -0.131528054 -0.128384837  0.103361960 -0.115118766
[21] -0.022659544 -0.072955762 -0.028494940 -0.004044300 -0.019470521
[26] -0.063676590 -0.124310820  0.120641285 -0.026921340  0.028510368
[31] -0.039981402  0.058311338  0.019417187 -0.009155135 -0.017792855
[36]  0.045410080  0.021407818 -0.023367920  0.017685113 -0.095446232
[41]  0.008566687  0.004561023 -0.093083167  0.015065792 -0.028764055
[46] -0.026167179 -0.063568987  0.029391559 -0.021079582 -0.002470855
[51] -0.078841205  0.017368386  0.026456011  0.056517392 -0.051072332
[56]  0.025907574 -0.064303085  0.029848033 -0.026556293 -0.031875944
> lengthx <- length(x)
> sqrtn <- sqrt(lengthx)
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/444hb1384971977.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/fisher/rcomp/tmp/5heql1384971977.tab") 
> 
> try(system("convert tmp/1we2x1384971977.ps tmp/1we2x1384971977.png",intern=TRUE))
character(0)
> try(system("convert tmp/20fj21384971977.ps tmp/20fj21384971977.png",intern=TRUE))
character(0)
> try(system("convert tmp/30bex1384971977.ps tmp/30bex1384971977.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.917   0.448   2.343