R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)

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> x <- c(1770,2203,2836,1976,2837,2150,2180,2631,1781,2327,2260,2051,2250,2102,2957,2485,2871,2447,2570,2622,1840,2682,2369,2119,2531,2214,3206,2709,2734,2348,2702,2642,2064,2647,2534,2297,2718,2321,3112,2664,2808,2668,2934,2616,2228,2463,2416,2407,2582,2101,3305,2818,2401,3019,2507,2948,2210,2467,2596,2451)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '2'
> par3 = '0'
> par2 = '1'
> par1 = '48'
> 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 (par2 == 0) {
+ x <- log(x)
+ } else {
+ x <- (x ^ par2 - 1) / par2
+ }
> if (par3 > 0) x <- diff(x,lag=1,difference=par3)
> if (par4 > 0) x <- diff(x,lag=par5,difference=par4)
> postscript(file="/var/www/rcomp/tmp/1ngf91323113640.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=paste('(lambda=',par2,', d=',par3,', D=',par4,', CI=', par7, ', CI type=',par6,')',sep=''))
> dev.off()
null device 
          1 
> postscript(file="/var/www/rcomp/tmp/2dpxu1323113640.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')
> dev.off()
null device 
          1 
> (myacf <- c(racf$acf))
 [1]  1.0000000000 -0.0681612389 -0.0303680188 -0.1584522758 -0.2038912616
 [6] -0.0029823691  0.0510807745 -0.0452514598 -0.0257157228  0.1308230219
[11]  0.1305517799 -0.1442432217 -0.2698207381 -0.2031792019 -0.0442110980
[16]  0.1876248467  0.2386022185 -0.0270843389  0.0589079351  0.0607480484
[21] -0.0859604090  0.0388465746 -0.1090657132  0.0284011399  0.0315709631
[26]  0.1334029287 -0.0095893536 -0.0099149135 -0.1145993811 -0.0936194788
[31]  0.0348388721 -0.0526490351  0.0668097947  0.0279221464 -0.0215577002
[36]  0.0001858854
> (mypacf <- c(rpacf$acf))
 [1] -0.068161239 -0.035177406 -0.163956427 -0.236283599 -0.062659769
 [6] -0.004936080 -0.129413841 -0.116110032  0.109859535  0.151915979
[11] -0.181017960 -0.342963061 -0.243565454 -0.158498292 -0.090179485
[16]  0.018122556 -0.110034799  0.010145757  0.093951987 -0.078466924
[21]  0.055394385  0.026480253  0.080376630 -0.118684401 -0.099240805
[26] -0.083430454  0.094985996 -0.005444728 -0.051146380  0.138315816
[31]  0.026697125  0.070384857  0.060240826 -0.026083318 -0.035572366
> lengthx <- length(x)
> sqrtn <- sqrt(lengthx)
> 
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/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/rcomp/tmp/3wjyk1323113640.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/rcomp/tmp/4xrzl1323113640.tab") 
> 
> try(system("convert tmp/1ngf91323113640.ps tmp/1ngf91323113640.png",intern=TRUE))
character(0)
> try(system("convert tmp/2dpxu1323113640.ps tmp/2dpxu1323113640.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
   0.58    0.07    0.64