R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(56,55,54,52,72,71,56,46,47,47,48,50,44,38,33,33,52,54,39,22,31,31,38,42,41,31,36,34,51,47,31,19,30,33,36,40,32,25,28,29,55,55,40,38,44,41,49,59,61,47,43,39,66,68,63,68,67,59,68,78,82,70,62,68,94,102,100,104,103,93,110,114,120,102,95,103,122,139,135,135,137,130,148,148,145,128,131,133,146,163,151,157,152,149,172,167,160,150,160,165,171,179,171,176,170,169,194,196,188,174,186,191,197,206,197,204,201,190,213,213)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '1'
> par2 = '1'
> par1 = '48'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '12'
> par4 <- '0'
> par3 <- '1'
> par2 <- '1'
> 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/17op61387555567.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/2bhia1387555567.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/3zm0s1387555567.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  0.018190006 -0.301551669 -0.309156180 -0.140527363
 [6]  0.190589805  0.300289429  0.175738229 -0.088540227 -0.331934526
[11] -0.285888778  0.064448407  0.798302053  0.003108007 -0.252634169
[16] -0.244529143 -0.117383518  0.127198372  0.273419366  0.152546242
[21] -0.086777069 -0.325868109 -0.188686183  0.079716622  0.605600059
[26] -0.041587756 -0.189371546 -0.144711769 -0.139904425  0.032879526
[31]  0.280425991  0.155867837 -0.049950140 -0.330335914 -0.180369387
[36]  0.077255799  0.424226456 -0.026537904 -0.115171358 -0.068634643
[41] -0.162793209 -0.057507415  0.274686959  0.183495055 -0.016834668
[46] -0.316502351 -0.159468271  0.037960668  0.317408828
> (mypacf <- c(rpacf$acf))
 [1]  0.018190006 -0.301982464 -0.326383628 -0.303639549 -0.070846707
 [6]  0.095256320  0.211307722  0.185574701 -0.031037733 -0.240178746
[11] -0.236193077  0.676580414 -0.030577354  0.081455125  0.126368283
[16]  0.183731110 -0.103533007  0.016031428 -0.035266182 -0.123478241
[21] -0.097072136  0.175701894 -0.047163303 -0.066670470 -0.053610501
[26]  0.101191932  0.054781413 -0.126098151 -0.165972729  0.074117577
[31]  0.059560206  0.105795994 -0.058347559 -0.134804859 -0.083172006
[36] -0.121155707  0.009678127 -0.132796658 -0.005794289  0.023643282
[41]  0.086215890  0.061820223  0.089714066 -0.034726150 -0.019779765
[46]  0.039302103 -0.024702563 -0.015321829
> 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/4a6491387555567.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/5sp401387555567.tab") 
> 
> try(system("convert tmp/17op61387555567.ps tmp/17op61387555567.png",intern=TRUE))
character(0)
> try(system("convert tmp/2bhia1387555567.ps tmp/2bhia1387555567.png",intern=TRUE))
character(0)
> try(system("convert tmp/3zm0s1387555567.ps tmp/3zm0s1387555567.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.849   0.586   3.402