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(1.93,2.02,1.85,1.77,1.81,1.67,1.55,1.62,1.79,1.73,1.77,1.95,2.08,2.26,2.02,1.9,1.97,1.76,1.93,1.91,1.96,1.99,1.98,1.96,1.95,2.26,2.07,2.02,2.07,1.88,1.75,1.78,1.87,1.94,2.03,2.13,2.04,2.18,2.02,1.99,2.09,1.88,1.8,1.77,1.85,1.9,2.03,2.02,2.09,2.3,2.16,2.02,2.31,1.98,1.74,1.82,2.07,2.04,2.07,2.13,2.14,2.43,2.26,2.11,2.19,2.04,2.04,2.05,2.08,1.98,2.07,2.12,2.15,2.35,2.19,2.17,2.3,2.09,1.95,1.89,1.95,1.98,1.95,2.06)
> 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/fisher/rcomp/tmp/11wz11384955469.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/2xgpg1384955469.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/3xkwj1384955469.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  0.684054680  0.462834049  0.354114658  0.109497600
 [6] -0.045808703 -0.125100409 -0.105441820 -0.042838337  0.145381275
[11]  0.212637356  0.315571414  0.520801208  0.359882583  0.265937709
[16]  0.200882753 -0.029020211 -0.169808795 -0.279846926 -0.265096224
[21] -0.178676876  0.003832225  0.064700047  0.183751532  0.395833335
[26]  0.259712455  0.168855215  0.136283351 -0.057625923 -0.211756487
[31] -0.264951346 -0.228055484 -0.199359071 -0.074164545  0.008236651
[36]  0.085845770  0.257437881  0.197473593  0.143637743  0.107775324
[41] -0.026037383 -0.137160114 -0.231240436 -0.233622273 -0.217348399
[46] -0.143232253 -0.110024734 -0.027624042  0.128334533  0.063997608
[51]  0.001436625 -0.022875869 -0.114915393 -0.182273990 -0.224430005
[56] -0.226361688 -0.241971708 -0.200417205 -0.167133653 -0.133977997
[61] -0.047577782
> (mypacf <- c(rpacf$acf))
 [1]  6.840547e-01 -9.579123e-03  7.712246e-02 -3.006290e-01 -4.437846e-02
 [6] -6.293804e-02  1.781204e-01  7.005118e-02  3.361624e-01 -1.371846e-01
[11]  2.472470e-01  2.157608e-01 -3.235243e-01  1.063109e-01 -1.201182e-01
[16] -1.544855e-01  3.212695e-03 -1.979979e-01  1.811841e-01  9.926207e-02
[21]  6.210446e-02 -8.915927e-02  1.366958e-01 -6.444022e-04 -1.132025e-01
[26] -7.274072e-02  6.918428e-02 -8.514083e-02 -7.824555e-02  1.657140e-01
[31]  6.827371e-02 -7.687525e-02 -2.830045e-02 -1.212895e-02 -8.789558e-02
[36]  6.817265e-03  7.484622e-02 -3.035226e-03 -1.104773e-02  4.666939e-02
[41]  3.184053e-02 -1.123510e-01  2.478151e-02 -5.624872e-02 -1.289705e-01
[46] -1.010060e-01  8.577732e-02 -2.049061e-02  5.803334e-03 -7.264403e-02
[51] -9.273167e-02  4.016417e-02  8.426898e-02  1.217584e-05 -9.443481e-02
[56] -3.478086e-02 -9.795684e-02 -4.784524e-02  3.261608e-03 -4.073885e-02
> 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/43n661384955469.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/5kycy1384955469.tab") 
> 
> try(system("convert tmp/11wz11384955469.ps tmp/11wz11384955469.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xgpg1384955469.ps tmp/2xgpg1384955469.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xkwj1384955469.ps tmp/3xkwj1384955469.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.056   0.473   2.507