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(760,730,730,680,730,710,800,830,820,770,800,840,800,710,800,780,760,730,770,880,850,810,770,810,890,790,840,830,740,760,630,890,900,820,810,820,890,810,810,840,830,790,610,870,870,820,800,840,860,860,730,850,860,900,610,960,820,860,810,820,820,880,840,910,860,880,620,970,810,880,870,800,740,1010,850,980,880,870,660,940,860,880,1000,840,800,1060,790,930,920,840,690,940,1010,890,1000,820,800,1000,780,1010,950,830,670,1000,960,920,1040,860)
> 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. 1.0.44 ()
> #Author: Dr. Ian E. Holliday
> #To cite this work: Ian E. Holliday, 2009, YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: 
> #Technical description: 
> 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/1qgnm1312388699.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/2e5ow1312388699.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/3frf31312388699.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 -0.554676087  0.057680888 -0.017837790  0.047684615
 [6] -0.168950622  0.261141451 -0.173984602  0.079915213 -0.005128347
[11]  0.023036000 -0.440749213  0.760203523 -0.424716831  0.062164166
[16]  0.012211719 -0.023535259 -0.109410652  0.269360800 -0.244527315
[21]  0.133200758 -0.024682642  0.041181532 -0.351196295  0.526998987
[26] -0.262080636  0.033577710  0.019876159 -0.078718567 -0.007639096
[31]  0.190040525 -0.219312004  0.150484160 -0.044393204  0.011649497
[36] -0.226727135  0.325286095 -0.117151581 -0.028890646  0.077116231
[41] -0.140740976  0.089132960  0.054717978 -0.139866517  0.150954443
[46] -0.069702292  0.003915731 -0.125986200  0.192057215 -0.048851590
[51] -0.057080586  0.098380736 -0.136573954  0.096072404 -0.012920497
[56] -0.063614949  0.114157029 -0.054500332 -0.026956271 -0.028885132
[61]  0.056320613
> (mypacf <- c(rpacf$acf))
 [1] -5.546761e-01 -3.610750e-01 -2.899507e-01 -1.723169e-01 -3.887556e-01
 [6] -1.346066e-01 -1.770730e-01 -1.096080e-01 -1.216886e-02  9.359336e-02
[11] -7.232757e-01 -2.144678e-02  9.506343e-04 -6.196206e-02  3.106356e-02
[16] -1.216690e-01 -5.997022e-02  1.753245e-01  1.016000e-02  4.876057e-02
[21] -4.774939e-02  3.164585e-02  1.524242e-01 -1.088409e-01  4.176193e-02
[26]  3.593991e-02 -4.063195e-02 -1.000418e-01  1.918154e-02 -1.170981e-01
[31] -1.086112e-03  6.545552e-02  6.727235e-02 -3.690427e-02  2.010472e-02
[36] -4.917510e-02  7.489171e-02 -1.260692e-01  9.161481e-03 -6.703402e-02
[41]  1.147642e-01 -1.129289e-02 -9.948093e-02 -1.945875e-02 -5.539081e-02
[46] -4.643058e-02 -1.099639e-01 -1.861519e-02  9.883974e-05 -1.191306e-02
[51] -3.446110e-02  1.247789e-01 -2.022523e-02  8.574698e-03  6.112011e-03
[56]  1.125809e-02  3.435070e-02  2.040145e-02 -2.142813e-02 -1.356995e-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/45it41312388699.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/5mcde1312388699.tab") 
> 
> try(system("convert tmp/1qgnm1312388699.ps tmp/1qgnm1312388699.png",intern=TRUE))
character(0)
> try(system("convert tmp/2e5ow1312388699.ps tmp/2e5ow1312388699.png",intern=TRUE))
character(0)
> try(system("convert tmp/3frf31312388699.ps tmp/3frf31312388699.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.044   0.210   1.247