R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(181896,181580,181234,180598,187123,186807,181896,178638,178954,178954,179269,179936,181580,179616,181580,179936,185158,187469,177656,175025,177305,176989,175025,175345,179269,178638,179269,179269,183545,184176,172398,172398,176989,174709,170785,172398,176327,174363,174047,169803,176007,177305,164545,164229,170785,167176,160967,163598,166509,167176,165212,161287,169456,169456,155078,154101,158025,150838,143616,145932,150838,146910,144283,138710,146247,146563,132190,131839,134470,126301,117465,121043,125950,120728,120412,115154,123670,125319,109266,105688,107968,99132,89981,92928,98470,91946,92928,89004,97172,98150,78524,77221,80799,71333,62817,65764,72950,64462,63799,57244,64462,66742,46448,46448,49391,41542,32706,37297,45466,36631,40244,35333,43186,45813,24853,23240,26502,18649,12444,15040)
> 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/15nqq1344979926.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/2ijgt1344979926.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/3mh461344979926.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 -0.12101633 -0.53395911  0.22140430  0.32943628 -0.05985135
 [7] -0.40754861 -0.04490286  0.32695071  0.18201307 -0.49356394 -0.08108663
[13]  0.86536779 -0.11779944 -0.50877266  0.21819230  0.31890105 -0.09247984
[19] -0.35770412 -0.02556375  0.29480391  0.13833107 -0.42661798 -0.05716038
[25]  0.73427934 -0.10700426 -0.45790092  0.21046114  0.27204645 -0.11867138
[31] -0.30998753 -0.01091328  0.24312772  0.09131839 -0.36313157 -0.04431740
[37]  0.60109783 -0.10322420 -0.39294433  0.19321800  0.23462937 -0.13119406
[43] -0.26398680  0.01265038  0.18514258  0.05592347 -0.30808804 -0.04656574
[49]  0.47758905
> (mypacf <- c(rpacf$acf))
 [1] -0.121016331 -0.556757754  0.078588158  0.120745894  0.243783101
 [6] -0.298244690 -0.254366056 -0.144809929  0.454984149 -0.239026041
[11]  0.055163317  0.675197092  0.124655567  0.124941962 -0.020249133
[16] -0.031330879 -0.098474523  0.129031239  0.030795600 -0.018811602
[21] -0.085833859  0.076272188 -0.040455509  0.013236203 -0.010787604
[26]  0.081894642 -0.013513335 -0.069596395 -0.088954497 -0.070498647
[31] -0.036655952 -0.047783984 -0.062443910 -0.074455059 -0.078765537
[36] -0.119161633 -0.068937695 -0.006518176 -0.010539378  0.047184266
[41]  0.005255138 -0.021903750  0.006997774 -0.026516255  0.021148886
[46] -0.032704213 -0.061996685 -0.115875568
> 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/4xncr1344979926.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/5klel1344979926.tab") 
> 
> try(system("convert tmp/15nqq1344979926.ps tmp/15nqq1344979926.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ijgt1344979926.ps tmp/2ijgt1344979926.png",intern=TRUE))
character(0)
> try(system("convert tmp/3mh461344979926.ps tmp/3mh461344979926.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.424   0.354   1.765