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(530.3,527.76,521.41,1601.93,1577.49,1551.43,1551.43,1516.88,1485.95,1438.22,1385.06,1329.49,1329.49,1276.16,1242.34,1181.59,1160.21,1135.18,1135.18,1084.96,1077.35,1061.13,1029.98,1013.08,1013.08,996.04,975.02,951.89,944.4,932.47,932.47,920.44,900.18,886.9,869.74,859.03,859.03,844.99,834.82,825.62,816.92,813.21,813.21,811.03,804.16,788.62,778.76,765.91,765.91,753.85,742.22,732.11,729.94,731.22,731.22,729.11,726.94,720.52,709.36,703.21)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> 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/1thip1333574234.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/2a2oe1333574234.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/3apkq1333574234.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]  1.000000e+00 -1.227115e-02 -8.771389e-03  1.725415e-02 -1.171176e-02
 [6] -1.138296e-02 -2.373378e-02 -3.406454e-02 -3.526220e-02  1.275364e-02
[11] -3.548960e-02 -1.893762e-02 -4.172877e-02 -9.429795e-03 -1.317485e-02
[16]  8.826230e-03 -3.721788e-02  1.260674e-03 -5.338986e-03 -2.095458e-02
[21] -8.766308e-03  6.233559e-03 -8.879023e-03 -1.286192e-02 -1.415601e-02
[26] -1.338601e-03 -6.041153e-03  4.478963e-03 -6.498942e-03 -1.456968e-02
[31] -7.297008e-03 -1.198725e-02 -6.134001e-03  2.610431e-03 -9.772421e-03
[36] -6.360846e-03 -4.750810e-03 -5.533423e-03 -1.373863e-03  1.680811e-03
[41] -1.060509e-04 -4.902550e-03 -1.271047e-02 -8.131952e-03 -1.119464e-02
[46]  4.980254e-05 -1.124532e-02 -1.096461e-02 -9.602390e-03 -2.859932e-03
[51] -1.096814e-04 -1.644108e-03 -3.849543e-03 -3.966088e-03 -7.821115e-03
[56] -1.239418e-02 -8.030990e-03  1.349413e-04  4.154390e-05
> (mypacf <- c(rpacf$acf))
 [1] -1.227115e-02 -8.923314e-03  1.703997e-02 -1.137400e-02 -1.137059e-02
 [6] -2.452267e-02 -3.450823e-02 -3.640393e-02  1.174547e-02 -3.549916e-02
[11] -1.994075e-02 -4.589338e-02 -1.232331e-02 -1.760406e-02  6.254411e-03
[16] -4.132078e-02 -3.383922e-03 -1.390741e-02 -2.476852e-02 -1.655756e-02
[21]  2.491328e-03 -1.513653e-02 -1.850508e-02 -2.379297e-02 -4.707400e-03
[26] -1.396849e-02  3.440318e-05 -1.328179e-02 -1.961585e-02 -1.639351e-02
[31] -1.772882e-02 -1.339644e-02 -1.702953e-03 -1.617917e-02 -1.352628e-02
[36] -1.418479e-02 -1.096718e-02 -8.666553e-03 -4.665205e-03 -7.014734e-03
[41] -1.175588e-02 -2.206411e-02 -1.510910e-02 -1.836937e-02 -6.350590e-03
[46] -1.840376e-02 -1.867808e-02 -1.958633e-02 -1.075328e-02 -8.944175e-03
[51] -9.065050e-03 -1.289355e-02 -1.359893e-02 -1.966346e-02 -2.199963e-02
[56] -1.800678e-02 -8.564817e-03 -1.059822e-02
> 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/4udul1333574234.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/50xki1333574234.tab") 
> 
> try(system("convert tmp/1thip1333574234.ps tmp/1thip1333574234.png",intern=TRUE))
character(0)
> try(system("convert tmp/2a2oe1333574234.ps tmp/2a2oe1333574234.png",intern=TRUE))
character(0)
> try(system("convert tmp/3apkq1333574234.ps tmp/3apkq1333574234.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.061   0.230   1.314