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(540,520,550,440,570,560,600,620,690,600,570,710,600,450,530,400,560,460,610,550,580,650,640,760,550,460,510,370,530,410,580,550,490,700,630,720,540,500,450,370,490,440,600,580,500,670,620,800,640,390,390,390,460,460,620,570,510,640,590,850,670,390,410,340,470,540,680,670,540,630,560,800,610,490,440,330,490,590,690,650,480,690,540,830,690,500,460,310,490,470,710,710,540,700,520,810,690,510,390,270,530,510,670,770,570,640,480,830)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '2'
> 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/10yxg1313737367.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/23rzf1313737367.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/3z1cc1313737367.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  0.046228221 -0.155225064 -0.087582050 -0.077096549
 [6] -0.209619693 -0.004009401 -0.005639543  0.128560834  0.142564592
[11]  0.070817919  0.083437032 -0.357657379 -0.206237054  0.019626817
[16]  0.012115081  0.010872010  0.218659971  0.010192200  0.132613655
[21] -0.015382896 -0.081106363  0.006016087  0.010399710 -0.163272563
[26]  0.193830470  0.154657569 -0.029043939 -0.080883194 -0.080546511
[31]  0.021766124 -0.038632755 -0.140797558  0.017171508  0.058178988
[36] -0.067326523  0.134132981 -0.017772045 -0.140511414  0.037528710
[41]  0.145169080 -0.119357928 -0.016919417 -0.062776376  0.121812488
[46]  0.027760445 -0.058805075 -0.005228795 -0.021262392 -0.060089131
[51]  0.028786153 -0.009773098 -0.082573989  0.084889336 -0.007759181
[56]  0.050937138 -0.008202972 -0.060570910 -0.070864523  0.055968914
[61] -0.005644676
> (mypacf <- c(rpacf$acf))
 [1]  0.046228221 -0.157699123 -0.073978442 -0.097006661 -0.237932813
 [6] -0.031271478 -0.107925705  0.079699057  0.088333777  0.051505303
[11]  0.155570803 -0.366669002 -0.095861883 -0.046153795 -0.082774874
[16]  0.015186678  0.029870356 -0.067271108  0.149745498 -0.009000378
[21]  0.100057531  0.164688962  0.109622576 -0.192488658  0.136496257
[26]  0.098589925 -0.025140476 -0.086995743 -0.041606406  0.058850810
[31]  0.018758529 -0.120035348  0.020259745 -0.009751250 -0.012995507
[36] -0.067858019 -0.018634793 -0.013902486  0.021339294  0.057767553
[41] -0.172238725 -0.027723418 -0.091094272 -0.006566760 -0.037098896
[46] -0.060737697 -0.026844871 -0.162542101  0.032122768 -0.010467718
[51] -0.033020079  0.032866749 -0.049135811  0.055407930  0.038557090
[56] -0.057315675  0.019692985 -0.054013656  0.011003647  0.018025157
> 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/4x0l31313737367.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/5rbd51313737367.tab") 
> 
> try(system("convert tmp/10yxg1313737367.ps tmp/10yxg1313737367.png",intern=TRUE))
character(0)
> try(system("convert tmp/23rzf1313737367.ps tmp/23rzf1313737367.png",intern=TRUE))
character(0)
> try(system("convert tmp/3z1cc1313737367.ps tmp/3z1cc1313737367.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.000   0.203   1.392