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(500,510,590,490,540,530,550,510,390,480,530,690,570,460,540,510,520,520,580,480,410,530,540,670,570,400,510,570,470,640,650,500,340,450,600,680,630,480,400,520,470,610,670,500,290,470,660,650,570,500,400,500,340,530,680,480,340,460,630,650,550,470,240,430,390,570,700,620,280,480,560,560,560,550,140,380,390,500,750,680,280,360,590,580,490,610,170,320,440,510,770,660,300,350,580,620,490,640,150,290,370,560,780,690,310,280,590,590)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '0'
> 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/1d1ir1312927824.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/2mr2p1312927824.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/38l4l1312927824.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  0.248040501 -0.352495456 -0.437854826 -0.259773744
 [6]  0.129399243  0.524694365  0.212476646 -0.223280652 -0.366626644
[11] -0.287484817  0.142840416  0.793791077  0.280305416 -0.307912414
[16] -0.379628225 -0.221834825  0.027540090  0.432582322  0.266629143
[21] -0.140924814 -0.341245389 -0.248628676  0.065004706  0.617049048
[26]  0.294553248 -0.259056652 -0.344200440 -0.195445523 -0.033743697
[31]  0.327844748  0.282102600 -0.055048797 -0.298786230 -0.227798600
[36] -0.003200667  0.420280829  0.276033584 -0.174319428 -0.296404623
[41] -0.178632662 -0.073383431  0.209593364  0.287173218  0.023104187
[46] -0.240251226 -0.212332130 -0.044979256  0.266653004  0.230828636
[51] -0.133066931 -0.255752962 -0.124275561 -0.055648357  0.146445189
[56]  0.237945066  0.048520375 -0.201798304 -0.201440793 -0.058825977
[61]  0.161784684
> (mypacf <- c(rpacf$acf))
 [1]  0.248040501 -0.441161614 -0.267817564 -0.299765593 -0.032315916
 [6]  0.312033166 -0.058704379 -0.010339396 -0.090894456 -0.192189462
[11]  0.096140483  0.682583074 -0.088604220  0.152763957  0.217837510
[16]  0.091344416 -0.073196889 -0.023011399  0.024658960  0.052019274
[21] -0.189617394  0.012174964 -0.004056890 -0.046911911 -0.060469183
[26] -0.058018386  0.004220031 -0.063437295 -0.078628745 -0.063778087
[31] -0.059346066  0.023381861 -0.024116857 -0.038406399 -0.011488154
[36] -0.141631266 -0.012511505 -0.045707460 -0.082207866 -0.052174513
[41] -0.024415494 -0.115277124  0.048655020 -0.061909497  0.078870427
[46]  0.021212258  0.035825157  0.057134060  0.014911668 -0.138317911
[51]  0.010348387  0.039832418  0.003930037  0.066439556 -0.041470587
[56]  0.067981738 -0.010279786 -0.065481479 -0.003710403 -0.021087461
> 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/4577q1312927824.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/5jtho1312927824.tab") 
> 
> try(system("convert tmp/1d1ir1312927824.ps tmp/1d1ir1312927824.png",intern=TRUE))
character(0)
> try(system("convert tmp/2mr2p1312927824.ps tmp/2mr2p1312927824.png",intern=TRUE))
character(0)
> try(system("convert tmp/38l4l1312927824.ps tmp/38l4l1312927824.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.076   0.199   1.273