R version 2.15.3 (2013-03-01) -- "Security Blanket"
Copyright (C) 2013 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(108.56,108.71,116.73,118.88,119.6,119.62,119.64,119.74,119.74,119.74,119.9,119.9,119.9,119.9,119.9,121.02,122.95,123.62,123.67,123.81,123.83,123.83,123.83,123.83,123.89,123.89,124.44,125.51,125.89,126.12,126.25,126.25,126.3,126.31,126.38,125.51,126.82,126.86,126.86,127.28,128.72,128.77,128.84,128.88,128.88,128.88,128.88,128.88,128.89,128.9,128.92,129.05,129.83,130.54,130.82,130.91,131.04,131.07,131.15,131.2,131.2,131.42,131.45,131.7,134.24,135.17,135.51,135.65,136.02,136.07,136.13,136.07)
> 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/fisher/rcomp/tmp/1d3jj1366647992.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/2fblb1366647992.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/373n61366647992.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  0.894890827  0.778713687  0.718544061  0.673208111
 [6]  0.633174311  0.593660340  0.555177311  0.520824006  0.495960824
[11]  0.471051275  0.445683894  0.418307139  0.386279015  0.346426159
[16]  0.298550223  0.256180123  0.227634178  0.203556720  0.181050528
[21]  0.162715298  0.146834490  0.130469945  0.113362591  0.094323761
[26]  0.069589593  0.038457971  0.008776848 -0.014553752 -0.037219667
[31] -0.057948605 -0.077761668 -0.097270545 -0.110729184 -0.120802103
[36] -0.135171400 -0.156948936 -0.166011045 -0.183451214 -0.207221394
[41] -0.228828854 -0.240191801 -0.251672062 -0.262383035 -0.272028660
[46] -0.280276375 -0.284565904 -0.287026067 -0.290572311
> (mypacf <- c(rpacf$acf))
 [1]  0.894890827 -0.111040118  0.221993003  0.004075392  0.064028457
 [6] -0.001988123  0.013690772  0.009070989  0.040361971 -0.007701327
[11]  0.013918402 -0.018619615 -0.026906551 -0.059847689 -0.070400677
[16] -0.019504508  0.011874112 -0.009520355  0.007154578  0.010792408
[21]  0.004701994 -0.006692114 -0.008358549 -0.016425681 -0.036983933
[26] -0.048820309 -0.021448483 -0.009393546 -0.029566713 -0.013058022
[31] -0.029554517 -0.021593825  0.003558149 -0.008852171 -0.026606157
[36] -0.042944119  0.049352030 -0.083406801 -0.013385419 -0.041376064
[41]  0.019185803 -0.047126821  0.001397188 -0.029725538 -0.003807016
[46] -0.011490536 -0.004264819 -0.016369110
> 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/452no1366647993.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/5pil61366647993.tab") 
> 
> try(system("convert tmp/1d3jj1366647992.ps tmp/1d3jj1366647992.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fblb1366647992.ps tmp/2fblb1366647992.png",intern=TRUE))
character(0)
> try(system("convert tmp/373n61366647992.ps tmp/373n61366647992.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.491   0.276   1.756