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)

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> x <- c(7.14,7.11,7.21,7.25,7.25,7.45,7.45,7.49,7.47,7.49,7.47,7.51,7.52,7.5,7.54,7.54,7.57,7.53,7.54,7.63,7.61,7.64,7.68,7.7,7.72,7.71,7.77,7.83,7.85,7.89,8.01,8.18,8.25,8.31,8.34,8.38,8.36,8.34,8.34,8.3,8.35,8.36,8.34,8.39,8.23,8.14,8.04,8.05,7.99,8.17,8.22,8.12,8.11,8.12,8.11,8.03,8.03,7.96,7.96,7.87,7.9,7.87,7.94,7.82,7.83,7.86,7.81,7.76,7.75,7.75,7.74,7.72)
> 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/fisher/rcomp/tmp/1synu1363711411.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/2g6zr1363711411.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/3lqso1363711411.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  0.119297338  0.221020621  0.129046810  0.135662846
 [6] -0.045288429  0.039127529  0.149914636  0.070416794  0.161591617
[11] -0.010487075  0.075680249  0.076966697 -0.019742849 -0.137800948
[16] -0.009303903  0.005983410  0.017964264 -0.029797612  0.166192462
[21] -0.052886260  0.008077493  0.064063944 -0.032887170  0.001438703
[26] -0.028099904  0.030880981  0.037627867  0.006503971 -0.001469679
[31] -0.009151435  0.010871613 -0.156662585 -0.089141273 -0.057774605
[36] -0.027057150 -0.095996468 -0.145675788  0.060062552 -0.183283666
[41] -0.133567912 -0.123192477 -0.060125071 -0.057021832  0.042633026
[46] -0.004575257 -0.040998545  0.039874419 -0.050861358
> (mypacf <- c(rpacf$acf))
 [1]  0.1192973383  0.2097742427  0.0882706248  0.0757515790 -0.1139791438
 [6] -0.0023576223  0.1702542977  0.0533067824  0.1175290549 -0.1105298029
[11] -0.0158334376  0.0971396717 -0.0512081048 -0.1719292585 -0.0309115094
[16]  0.0367899541  0.1088019504 -0.0497458747  0.1134682458 -0.1063303547
[21] -0.0036573290  0.1563189699 -0.0550369086 -0.0295583828 -0.0442994724
[26]  0.0143857431  0.1351557388 -0.1132839622 -0.0379115675 -0.0321261971
[31] -0.0001650582 -0.0694756224 -0.0913374931 -0.0487108142  0.0552048309
[36] -0.0288750244 -0.1266037513  0.0439765974 -0.1264060634 -0.0431494414
[41] -0.0059138133 -0.0093347318  0.0597741544  0.1378454869  0.0065362052
[46] -0.1237339270  0.0197785119  0.0535905500
> 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/4c94m1363711411.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/5k7o51363711411.tab") 
> 
> try(system("convert tmp/1synu1363711411.ps tmp/1synu1363711411.png",intern=TRUE))
character(0)
> try(system("convert tmp/2g6zr1363711411.ps tmp/2g6zr1363711411.png",intern=TRUE))
character(0)
> try(system("convert tmp/3lqso1363711411.ps tmp/3lqso1363711411.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.802   0.278   2.060