R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(8378669.00,7557530.00,8656721.00,7729873.00,7067002.00,7222189.00,6758161.00,6745665.00,8203660.00,8799755.00,7995151.00,6844694.00,7400186.00,6146183.00,6793027.00,5815146.00,5993505.00,5838016.00,5926815.00,5642890.00,7120621.00,7781743.00,7638921.00,5886070.00,7358890.00,6981189.00,8423532.00,6819313.00,6727221.00,6923349.00,7578240.00,7228898.00,8988846.00,8404694.00,9601659.00,8213138.00,8434646.00,8466539.00,9106270.00,8438555.00,7723821.00,7538413.00,7199881.00,8168314.00,9045790.00,8544483.00,9020709.00,7932021.00,8435986.00,7920357.00,8333659.00,7415547.00,7770392.00,8188878.00,8092465.00,7188528.00,8152373.00,9025069.00,9233973.00,6916290.00,8171721.00,7012501.00,8779456.00,7308709.00,8084547.00,8255978.00,7658071.00,7371877.00,8780827.00,10116778.00,9567175.00,7455902.00)
> 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/17qa81413658532.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/2yz8t1413658532.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/3t2n31413658532.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 -0.36104888  0.11076518 -0.39978942  0.29357398 -0.16365210
 [7]  0.13933067 -0.19319970  0.23342009 -0.28832973  0.14977198 -0.25708035
[13]  0.55325398 -0.20816901  0.12760836 -0.29628462  0.16444144 -0.13255898
[19]  0.11228228 -0.10860051  0.12286529 -0.23667880  0.13798053 -0.15244886
[25]  0.34207744 -0.16422429  0.16540713 -0.23203540  0.11789378 -0.19095321
[31]  0.13380555 -0.10819759  0.23177097 -0.24501648  0.07649317 -0.14200203
[37]  0.35925801 -0.15645589  0.10400316 -0.17131187  0.07767711 -0.09182979
[43]  0.01360064 -0.05026461  0.18843222 -0.12283497  0.01175788 -0.15600278
[49]  0.27587693
> (mypacf <- c(rpacf$acf))
 [1] -0.361048878 -0.022527741 -0.422261242  0.013275153 -0.102924775
 [6] -0.092904003 -0.104377334  0.076253432 -0.240040923 -0.110002423
[11] -0.259546969  0.319092396  0.138943121  0.084185715  0.124280960
[16] -0.042609167 -0.035701649 -0.072052399  0.011117707 -0.069829124
[21] -0.137866129 -0.063879122 -0.081856089 -0.034795019 -0.059135293
[26]  0.067753883 -0.006189877  0.014227610 -0.103185908 -0.062629837
[31] -0.110919288  0.131218678  0.039294037 -0.079697989 -0.017166831
[36]  0.095394998  0.017691170 -0.061814706  0.088266596 -0.099733906
[41]  0.087112117 -0.124912528 -0.028229554 -0.010968094  0.003544178
[46]  0.011416551 -0.095273850 -0.011838481
> 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/4htgx1413658532.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/5bl511413658532.tab") 
> 
> try(system("convert tmp/17qa81413658532.ps tmp/17qa81413658532.png",intern=TRUE))
character(0)
> try(system("convert tmp/2yz8t1413658532.ps tmp/2yz8t1413658532.png",intern=TRUE))
character(0)
> try(system("convert tmp/3t2n31413658532.ps tmp/3t2n31413658532.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.176   0.208   1.399