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(339,139,186,155,153,222,102,107,188,162,185,24,394,209,248,254,202,258,215,309,240,258,276,48,455,345,311,346,310,297,300,274,292,304,186,14,321,206,160,217,204,246,234,175,364,328,158,40,556,193,221,278,230,253,240,252,228,306,206,48,557,279,399,364,306,471,293,333,316,329,265,61,679,428,394,352,387,590,177,199,203,255,261,115)
> 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/wessaorg/rcomp/tmp/17jx51384347217.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/2wwtx1384347217.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/3xsxl1384347217.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  0.03538262  0.13731179  0.24468430  0.17389764  0.24235718
 [7] -0.14720104  0.11931936  0.03814408  0.08127002 -0.01423071 -0.19947416
[13]  0.52748063 -0.16072445 -0.05014135  0.02938102 -0.03357418  0.06097324
[19] -0.19514864 -0.03939554 -0.02748216 -0.02108526 -0.12075623 -0.22874678
[25]  0.30155465 -0.17319673 -0.08959088 -0.03528398  0.02460516  0.09862167
[31] -0.11475896  0.01732716  0.05958760  0.08966983 -0.05144403 -0.10030404
[37]  0.27865177 -0.12901497 -0.02976188 -0.02886530  0.02845250  0.06090302
[43] -0.09670338  0.06757281  0.03850985  0.09096520  0.03182214 -0.05129121
[49]  0.26367646 -0.06129774  0.02451211 -0.02496628 -0.01804711  0.01724552
[55] -0.18188902 -0.02547002 -0.10773022 -0.06195195 -0.07634762 -0.19401046
[61]  0.06125387
> (mypacf <- c(rpacf$acf))
 [1]  0.035382617  0.136230415  0.240424920  0.160499577  0.204758834
 [6] -0.259290728 -0.022182607 -0.066351142  0.121218458 -0.012151933
[11] -0.177050406  0.544456906 -0.256439168 -0.095055595 -0.139863906
[16] -0.091229061 -0.118858079  0.149296855 -0.059708291  0.143682570
[21] -0.070673693 -0.131629037 -0.012507849 -0.011508547  0.009238835
[26]  0.035799155 -0.031241628  0.161190910  0.077221043 -0.074539059
[31]  0.062825815 -0.078305528  0.006163349 -0.013415690  0.052160121
[36]  0.028784682 -0.077621249 -0.068435407  0.038919830 -0.081024643
[41] -0.023521605  0.084140382  0.123129443  0.006768470  0.013291474
[46]  0.108303394 -0.083122877 -0.009693992  0.028193302  0.013287138
[51] -0.058258605 -0.068205024 -0.096644474 -0.106546364 -0.059861251
[56] -0.080890973  0.016863816 -0.028850198  0.007890881 -0.124224853
> 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/48irj1384347217.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/5fcpg1384347217.tab") 
> 
> try(system("convert tmp/17jx51384347217.ps tmp/17jx51384347217.png",intern=TRUE))
character(0)
> try(system("convert tmp/2wwtx1384347217.ps tmp/2wwtx1384347217.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xsxl1384347217.ps tmp/3xsxl1384347217.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.059   0.423   2.460