R version 3.0.1 (2013-05-16) -- "Good Sport"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(293544,282672,298980,239184,309852,304416,326160,337032,375084,326160,309852,385956,326160,244620,288108,217440,304416,250056,331596,298980,315288,353340,347904,413136,298980,250056,277236,201132,288108,222876,315288,298980,266364,380520,342468,391392,293544,271800,244620,201132,266364,239184,326160,315288,271800,364212,337032,434880,347904,212004,212004,212004,250056,250056,337032,309852,277236,347904,320724,462060,364212,212004,222876,184824,255492,293544,369648,364212,293544,342468,304416,434880,331596,266364,239184,179388,266364,320724,375084,353340,260928,375084,293544,451188,375084,271800,250056,168516,266364,255492,385956,385956,293544,380520,282672,440316,375084,277236,212004,146772,288108,277236,364212,418572,309852,347904,260928,451188)
> 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/wessaorg/rcomp/tmp/1vfex1377077370.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/2ox5e1377077370.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/3owdv1377077370.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  0.33666252  0.16905725 -0.23179385 -0.38278568 -0.13150358
 [7] -0.33855635 -0.07743736 -0.35464083 -0.21790430  0.13326714  0.29163814
[13]  0.82222175  0.31966267  0.19079636 -0.21197686 -0.37862457 -0.12342631
[19] -0.30249947 -0.04399535 -0.27173381 -0.18108454  0.08735818  0.21648687
[25]  0.66566936  0.31637728  0.19246186 -0.18306308 -0.35035793 -0.14991405
[31] -0.27506762 -0.05108291 -0.20154335 -0.12951734  0.07878343  0.15858565
[37]  0.55652305  0.27727644  0.13592507 -0.14519105 -0.30542524 -0.16485147
[43] -0.27174131 -0.02770578 -0.12625996 -0.07366218  0.07088212  0.10835015
[49]  0.42218738
> (mypacf <- c(rpacf$acf))
 [1]  0.336662520  0.062837726 -0.346809981 -0.279258418  0.207077515
 [6] -0.405723303 -0.104223905 -0.444313821 -0.286190688  0.159992314
[11]  0.123630981  0.571131985 -0.098578314 -0.005205219  0.026664331
[16] -0.017875920  0.121990272  0.018679707 -0.015145064  0.166723489
[21] -0.017056543 -0.054663650 -0.002628757 -0.055507551  0.129475168
[26] -0.016448122 -0.066876401  0.058532151 -0.089156269 -0.003810126
[31] -0.113288553 -0.012498323  0.110422372  0.001264829 -0.085368564
[36]  0.075844136 -0.152473746 -0.135010592  0.083280592 -0.003349235
[41] -0.017895679 -0.100266328  0.062624878  0.056929005 -0.027335742
[46] -0.106975923 -0.058907510 -0.030832523
> 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/4uyno1377077370.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/5ghg31377077370.tab") 
> 
> try(system("convert tmp/1vfex1377077370.ps tmp/1vfex1377077370.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ox5e1377077370.ps tmp/2ox5e1377077370.png",intern=TRUE))
character(0)
> try(system("convert tmp/3owdv1377077370.ps tmp/3owdv1377077370.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.537   0.601   3.108