R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(1.26,1.27,1.24,1.25,1.27,1.25,1.26,1.27,1.26,1.26,1.28,1.27,1.28,1.27,1.26,1.27,1.27,1.28,1.27,1.26,1.3,1.31,1.28,1.29,1.31,1.29,1.29,1.32,1.3,1.29,1.31,1.29,1.33,1.35,1.32,1.33,1.34,1.34,1.33,1.33,1.35,1.32,1.35,1.32,1.36,1.37,1.34,1.32,1.34,1.32,1.33,1.35,1.33,1.33,1.35,1.33,1.36,1.39,1.37,1.37,1.39,1.37,1.39,1.39,1.39,1.37,1.38,1.37,1.41,1.41,1.42,1.42)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '0'
> par2 = '1'
> par1 = '60'
> 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. 1.2.291 ()
> #Author: root
> #To cite this work: Wessa P., (2012), (Partial) Autocorrelation Function (v1.0.11) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_autocorrelation.wasp/
> #Source of accompanying publication: 
> #
> 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/fisher/rcomp/tmp/1368k1356711841.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/fisher/rcomp/tmp/2fq731356711841.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/fisher/rcomp/tmp/3sk991356711841.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  0.873986723  0.816631726  0.786530398  0.709364081
 [6]  0.683560447  0.673375262  0.614133473  0.574703005  0.545230608
[11]  0.481795947  0.465531097  0.455660377  0.390338924  0.339378057
[16]  0.289779874  0.248777079  0.235761705  0.209224319  0.168535989
[21]  0.159189378  0.129716981  0.110831586  0.109765898  0.111530398
[26]  0.079332635  0.051537386  0.008962264 -0.030258560 -0.043064291
[31] -0.074528302 -0.102323550 -0.134416492 -0.138626834 -0.154577219
[36] -0.175139762 -0.181761006 -0.214797345 -0.226554857 -0.262631027
[41] -0.293885395 -0.278808526 -0.306603774 -0.304734451 -0.306219427
[46] -0.321540881 -0.313801537 -0.302603075 -0.332285115 -0.341212439
[51] -0.339133473 -0.369968553 -0.381201957 -0.369793850 -0.382180294
[56] -0.384189378 -0.374563242 -0.369025157 -0.337491265 -0.321156534
[61] -0.316666667
> (mypacf <- c(rpacf$acf))
 [1]  0.873986723  0.223500143  0.164860600 -0.140527978  0.122751574
 [6]  0.117501444 -0.109533011 -0.050817863  0.008432094 -0.082779889
[11]  0.088634972  0.053110893 -0.159301968 -0.131187690 -0.064118507
[16]  0.105470889  0.068644861 -0.068928160 -0.073617655  0.092903628
[21]  0.018419018  0.060046839 -0.025782893  0.057040430 -0.116236146
[26] -0.053425281 -0.081252706 -0.059440509 -0.033117750 -0.065619658
[31] -0.004683875 -0.089363926  0.141901823  0.002291492 -0.065480186
[36] -0.042303648 -0.063739729  0.113184426 -0.101703368 -0.085924838
[41]  0.124883547 -0.132933293  0.148632565 -0.071030311 -0.042060558
[46]  0.005648338 -0.006560909 -0.105772838 -0.059879590 -0.023351375
[51] -0.017690670 -0.027194035 -0.002046205  0.024130337 -0.057897332
[56]  0.064923656  0.079633811  0.051471798 -0.064333258  0.010771631
> lengthx <- length(x)
> sqrtn <- sqrt(lengthx)
> 
> #Note: the /var/fisher/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/fisher/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/fisher/rcomp/tmp/4dry81356711841.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/fisher/rcomp/tmp/5zq1z1356711841.tab") 
> 
> try(system("convert tmp/1368k1356711841.ps tmp/1368k1356711841.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fq731356711841.ps tmp/2fq731356711841.png",intern=TRUE))
character(0)
> try(system("convert tmp/3sk991356711841.ps tmp/3sk991356711841.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.856   0.509   2.361