R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(1.38,1.96,1.36,1.24,1.35,1.23,1.09,1.08,1.33,1.35,1.38,1.5,1.47,2.09,1.52,1.29,1.52,1.27,1.35,1.29,1.41,1.39,1.45,1.53,1.45,2.11,1.53,1.38,1.54,1.35,1.29,1.33,1.47,1.47,1.54,1.59,1.5,2,1.51,1.4,1.62,1.44,1.29,1.28,1.4,1.39,1.46,1.49,1.45,2.05,1.59,1.42,1.73,1.39,1.23,1.37,1.51,1.47,1.5,1.54,1.54,2.15,1.62,1.4,1.65,1.49,1.45,1.45,1.51,1.48,1.56,1.57,1.57,2.28,1.7,1.56,1.8,1.56,1.51,1.46,1.51,1.55,1.57,1.64,1.58,2.16,1.77,1.54,1.64,1.53,1.49,1.43,1.52,1.56,1.59,1.64)
> 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/1edxn1413562855.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/2w87m1413562855.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/3miqg1413562855.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 -0.393421466 -0.185935057  0.283915628 -0.139210666
 [6]  0.008850466 -0.142209256  0.006733728 -0.130182233  0.263258037
[11] -0.143457707 -0.364140680  0.850907168 -0.325843799 -0.174816644
[16]  0.267190183 -0.136044857  0.025260631 -0.107444474 -0.021408759
[21] -0.104331300  0.228776399 -0.133211644 -0.307272288  0.710471971
[26] -0.269010190 -0.136106400  0.225111999 -0.103520938  0.003810777
[31] -0.085867524 -0.018176119 -0.086797080  0.179368832 -0.098175672
[36] -0.259699889  0.578081170 -0.213464575 -0.115471046  0.194830426
[41] -0.083212172  0.008745925 -0.083957661 -0.003618711 -0.059417884
[46]  0.117879130 -0.080052049 -0.208359976  0.476621512
> (mypacf <- c(rpacf$acf))
 [1] -0.39342147 -0.40310888  0.03204714 -0.06393016  0.03536913 -0.27356675
 [7] -0.22392184 -0.50721574  0.02145946 -0.21561783 -0.74969271  0.25820921
[13] -0.02438615  0.03331553 -0.10279336 -0.11134423 -0.08476824  0.06529557
[19] -0.03963712  0.05243191 -0.04345321 -0.12743887 -0.02284112 -0.07260766
[25] -0.05441648  0.05431797 -0.07571373  0.08742779 -0.08794990 -0.11067470
[31] -0.02795361  0.02095938 -0.10314708  0.05716628 -0.07539506 -0.06459951
[37] -0.04695692 -0.12975302 -0.04462718 -0.08923481 -0.04520825 -0.07286929
[43] -0.06100952 -0.02203448 -0.03313032 -0.12978974 -0.06729597 -0.01098813
> 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/4unc71413562855.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/5w3gc1413562855.tab") 
> 
> try(system("convert tmp/1edxn1413562855.ps tmp/1edxn1413562855.png",intern=TRUE))
character(0)
> try(system("convert tmp/2w87m1413562855.ps tmp/2w87m1413562855.png",intern=TRUE))
character(0)
> try(system("convert tmp/3miqg1413562855.ps tmp/3miqg1413562855.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.159   0.199   1.377