R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(123,146,156,127,128,147,128,139,130,118,147,98,141,138,130,145,123,116,90,110,102,109,111,93,120,81,84,87,110,90,108,101,87,118,82,86,103,93,83,91,69,95,96,105,121,101,111,130,134,161,186,244,145,170,164,124,154,126,173,140,142,129,171,107,98,185,142,135,126,126,134,119,134,133,129,96,150,113,99,164,127,148,166,115,199,141,149,131,171,178,181,129,112,186,153,116,190,169,165,160,202,155,257,171,168,202,189,132)
> 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. 1.2.291 ()
> #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/1rear1413362932.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/2eiz21413362932.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/37t231413362932.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 -0.532960722  0.008401355  0.140538447 -0.071841396
 [6]  0.004174359 -0.016480851  0.001625433 -0.046608110  0.203207067
[11] -0.229155122 -0.009720408  0.211175171 -0.331285184  0.289690727
[16] -0.128030607  0.014027331  0.013476979  0.069323039 -0.124978251
[21]  0.044184248  0.059994591 -0.142403760  0.200414302 -0.097255896
[26] -0.108099202  0.165731127 -0.209716402  0.134983223 -0.007220303
[31] -0.064494849  0.057290187  0.040287620 -0.112911616  0.028178194
[36]  0.093653192 -0.137896166  0.123436789 -0.003106004 -0.069596361
[41]  0.097408617 -0.051584373 -0.074980847  0.089386663 -0.008134059
[46] -0.103040690  0.183418670 -0.127456510 -0.008957668
> (mypacf <- c(rpacf$acf))
 [1] -0.532960722 -0.385005478 -0.095851431 -0.016835069  0.009021799
 [6] -0.044460810 -0.060220905 -0.131556332  0.191976808  0.013370310
[11] -0.179379535  0.022198187 -0.279124077  0.046252358  0.005375886
[16]  0.039155954 -0.033802044  0.063224773 -0.022040162 -0.008208412
[21] -0.035157759 -0.061082497  0.049771383  0.059581363 -0.078293913
[26] -0.096525443 -0.252558929 -0.092593415  0.057974117 -0.060288518
[31]  0.030759073 -0.063622912 -0.105844569 -0.035859535  0.002937891
[36] -0.028328736 -0.016503374 -0.042799346  0.032249828 -0.007613684
[41]  0.052103582 -0.086995827 -0.061984634 -0.018513672 -0.101573775
[46]  0.040410163 -0.054531438  0.007355351
> 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/4k99h1413362932.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/5f9il1413362932.tab") 
> 
> try(system("convert tmp/1rear1413362932.ps tmp/1rear1413362932.png",intern=TRUE))
character(0)
> try(system("convert tmp/2eiz21413362932.ps tmp/2eiz21413362932.png",intern=TRUE))
character(0)
> try(system("convert tmp/37t231413362932.ps tmp/37t231413362932.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.159   0.242   1.404