R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(5745,4549,5074,3602,2732,2589,2148,2330,2752,3241,4517,6550,6778,6240,5570,3558,3299,2447,2380,2378,2947,3651,4816,6436,7090,4682,4198,3860,3056,2563,2568,2472,2821,4015,4686,5418,5649,4572,4695,3766,2900,2528,2549,2478,2828,4139,5390,5621,5291,5272,4677,3520,2842,2723,2581,2429,2606,3787,4630,5505,5577,4911,4701,3557,2921,2734,2636,2433,2640,3794,4745,5698,5909,5119,5200,3876,3104,2251,2386,2794,2967,3392,4741,5909,5901,4962,4751,3909,3130,2860,2568,2540,2894,4216,4530,5144,6206,5645,4601,3645,3140,2264,2557,2431,2747,4587,4512,5313,6011,5328,5014,3630,3102,2739,2877,2659,2957,3785,4785,5757,5458,5427,5018,3498,3204,2763,2589,2591,2805,3278,4615,5524,6167,5380,5377,3603,2774,2470,2407,2512,2451,3134,4210,4859,5022,4584,4267,3022,2777,2428,2389,2496,2820,3854,4748,5666,5293,4905,4920,3854,2659,2491,2455,2472,3030,3987,4453,5417)
> 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. 1.0.44 ()
> #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/www/html/rcomp/tmp/1gy301292692162.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/www/html/rcomp/tmp/298231292692162.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/www/html/rcomp/tmp/398231292692162.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  0.803316592  0.443069164 -0.011395897 -0.451669628
 [6] -0.757133052 -0.868158796 -0.748508282 -0.433508999  0.001791208
[11]  0.433733644  0.753782297  0.870127703  0.712436941  0.383594094
[16] -0.039253455 -0.417872240 -0.689482175 -0.784874675 -0.667999817
[21] -0.385849890  0.014362576  0.413460234  0.681862863  0.776447297
[26]  0.641141685  0.351356372 -0.027046590 -0.389987911 -0.638775512
[31] -0.725396533 -0.619669551 -0.363228303  0.005507018  0.362332411
[36]  0.616128608  0.709177702  0.588139429  0.330991365 -0.016339630
[41] -0.350123485 -0.580391587 -0.663650118 -0.568290635 -0.339386548
[46] -0.011167178  0.316942080  0.556793180  0.650698614
> (mypacf <- c(rpacf$acf))
 [1]  0.803316592 -0.570223815 -0.468788930 -0.391446757 -0.258879333
 [6] -0.268580433 -0.151481746 -0.067200413  0.085756784  0.110870083
[11]  0.179701327  0.138307079 -0.224425986 -0.017848225 -0.009822328
[16]  0.221479185  0.026657282 -0.023684445 -0.026833864 -0.108289293
[21]  0.043330770  0.108562941 -0.052526511  0.118047134 -0.023353623
[26]  0.073706627 -0.030842058 -0.107583553  0.066547633 -0.003937216
[31]  0.033706902 -0.102266676 -0.023523467 -0.071734736  0.003333662
[36]  0.068849904 -0.081880065 -0.033770039 -0.015720825 -0.025429895
[41]  0.039250048 -0.021456583 -0.018735228 -0.073706278 -0.042673541
[46]  0.017386064 -0.005415812  0.055707346
> lengthx <- length(x)
> sqrtn <- sqrt(lengthx)
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/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/www/html/rcomp/tmp/4vqjr1292692162.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/www/html/rcomp/tmp/5grzf1292692162.tab") 
> 
> try(system("convert tmp/1gy301292692162.ps tmp/1gy301292692162.png",intern=TRUE))
character(0)
> try(system("convert tmp/298231292692162.ps tmp/298231292692162.png",intern=TRUE))
character(0)
> try(system("convert tmp/398231292692162.ps tmp/398231292692162.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  0.738   0.499   2.445