R version 2.15.3 (2013-03-01) -- "Security Blanket"
Copyright (C) 2013 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(59,57,62,50,40,46,39,35,31,35,33,47,34,31,36,24,22,17,8,12,5,6,5,8,15,16,17,23,24,27,31,40,47,43,60,64,65,65,55,57,57,57,65,69,70,71,71,73,68,65,57,41,21,21,17,9,11,6,-2,0,5,3,7,4,8,9,14,12,12,7,15,14,19,39,12)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '4'
> par4 = '0'
> par3 = '1'
> par2 = '1.0'
> par1 = '48'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '4'
> par4 <- '0'
> par3 <- '1'
> par2 <- '1.0'
> par1 <- '48'
> #'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/wessaorg/rcomp/tmp/1bdw11363730822.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/2fnef1363730822.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/3kj111363730822.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 -0.037890842  0.118836611  0.260663493 -0.071147956
 [6]  0.112151746  0.048906427 -0.026679855 -0.009428139 -0.039173293
[11] -0.061827290  0.035092199 -0.036961564  0.040098115 -0.067969712
[16] -0.120385672 -0.087142942 -0.038617659 -0.157123068 -0.085179338
[21] -0.068237779 -0.211624268 -0.077724332  0.048725319 -0.121282391
[26] -0.036713660 -0.007029567 -0.186785984 -0.087081616 -0.016127122
[31]  0.016589975 -0.005335971  0.012689503  0.024541231  0.085374829
[36]  0.008560981  0.140397560  0.070322269  0.046391733  0.078932531
[41] -0.077607858  0.011009232  0.026484893 -0.002117579 -0.035640112
[46]  0.056267901  0.052086171 -0.040761135  0.078577770
> (mypacf <- c(rpacf$acf))
 [1] -0.037890842  0.117569691  0.273255740 -0.064486011  0.043164768
 [6]  0.003517237 -0.009146763 -0.069174653 -0.046320152 -0.054531326
[11]  0.059376455 -0.001037708  0.067188006 -0.087381733 -0.126472531
[16] -0.134342451  0.020506645 -0.111806994 -0.057228224 -0.035878756
[21] -0.118035240 -0.085496831  0.118817315 -0.046606348 -0.071574326
[26] -0.054081719 -0.140527064 -0.164705431 -0.021261618  0.086299796
[31]  0.022450718 -0.003105275 -0.007030970  0.051089313 -0.077968381
[36]  0.005846333 -0.017791420  0.039326562 -0.021251906 -0.130948345
[41] -0.081071344 -0.067295672 -0.049124328 -0.046416195  0.024751909
[46]  0.030594139 -0.100056099 -0.029398523
> 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/4kcdc1363730822.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/5r0rw1363730822.tab") 
> 
> try(system("convert tmp/1bdw11363730822.ps tmp/1bdw11363730822.png",intern=TRUE))
character(0)
> try(system("convert tmp/2fnef1363730822.ps tmp/2fnef1363730822.png",intern=TRUE))
character(0)
> try(system("convert tmp/3kj111363730822.ps tmp/3kj111363730822.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.166   0.335   2.497