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(2.7,3,-0.3,1.1,1.7,1.6,3,3.3,6.7,5.6,6,4.8,5.9,4.3,3.7,5.6,1.7,3.2,3.6,1.7,0.5,2.1,1.5,2.7,1.4,1.2,2.3,1.6,4.7,3.5,4.4,3.9,3.5,3,1.6,2.2,4.1,4.3,3.5,1.8,0.6,-0.4,-2.5,-1.6,-1.9,-1.6,-0.7,-1.1,0.3,1.3,3.3,2.4,2,3.9,4.2,4.9,5.8,4.8,4.4,5.3,2.1,2,-0.9,0.1,-0.5,-0.1,0.7,-0.4,-1.5,-0.3,1,0.4,0.3,1.8,3,2.2,3.4,3.4,3.1,4.5,4.6,5.7,4.3,4.5)
> 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/wessaorg/rcomp/tmp/1rglg1384333572.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/2w1nw1384333572.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/3wj1m1384333572.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]  1.00000000  0.79591166  0.68900840  0.52602249  0.34337464  0.18619512
 [7]  0.01313303 -0.09849773 -0.24475104 -0.33677216 -0.42913983 -0.48350443
[13] -0.53813712 -0.46293687 -0.40182104 -0.29117436 -0.19244777 -0.05195181
[19]  0.10485173  0.18245823  0.34349233  0.37530814  0.43765979  0.41710694
[25]  0.36526629  0.31468761  0.24139994  0.17931311  0.09591657 -0.02494687
[31] -0.14824926 -0.25100696 -0.35766175 -0.39926930 -0.47532285 -0.42307353
[37] -0.37733678 -0.30500703 -0.20478308 -0.16065485 -0.04690228  0.04028345
[43]  0.12403636  0.16416288  0.20052964  0.19540818  0.18602347  0.14223853
[49]  0.11619969  0.07630529  0.01480694 -0.02154347 -0.09859090 -0.14420015
[55] -0.21130873 -0.18858829 -0.20561677 -0.18983392 -0.14013470 -0.12509726
[61] -0.09993134
> (mypacf <- c(rpacf$acf))
 [1]  0.795911662  0.151512408 -0.167182835 -0.213457419 -0.086919874
 [6] -0.139029678  0.002718736 -0.174638685 -0.077328597 -0.128683398
[11] -0.062879246 -0.164708882  0.229201719  0.013714947  0.074564224
[16] -0.095255318  0.152029884  0.119510958 -0.034677163  0.162532436
[21] -0.097394932  0.023694139 -0.115723837 -0.092493321  0.061064595
[26]  0.105372664  0.012604104 -0.006561409 -0.161522558 -0.046917886
[31] -0.072229366  0.049367102 -0.007607567 -0.136057142  0.062704824
[36]  0.021078832  0.031427427  0.011264453 -0.097193593 -0.021438541
[41]  0.068627642 -0.091848914 -0.122228748 -0.020305390 -0.122526010
[46] -0.073581688 -0.034458707  0.045090966  0.069909613 -0.068852980
[51]  0.024214331  0.031527485 -0.040317006 -0.008776777  0.069249159
[56] -0.040768580 -0.019729561 -0.074573184 -0.049354296 -0.058668707
> 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/42ex31384333572.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/55zon1384333572.tab") 
> 
> try(system("convert tmp/1rglg1384333572.ps tmp/1rglg1384333572.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w1nw1384333572.ps tmp/2w1nw1384333572.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wj1m1384333572.ps tmp/3wj1m1384333572.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.247   0.450   2.674