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(24514,24442,24364,24222,25689,25618,24514,23780,23851,23851,23922,24072,24514,24735,25105,25397,26722,26573,25468,23851,24143,24442,24364,24735,24442,24955,25176,25247,26872,26573,25468,23851,24143,23922,24293,25105,25027,24884,25247,25468,26722,26793,25468,23559,23409,23851,23481,24663,24663,24222,24806,25176,26430,26793,25247,23409,23409,22818,22376,23338,22968,22084,22676,23189,24735,25326,23702,22526,22526,22084,21792,22376,21643,21493,21864,22376,23922,24222,22305,20909,20246,19584,19213,19947,19506,19584,19947,20246,21714,21935,19584,18480,17375,16635,16122,16855,16492,17076,17297,17518,18480,19064,16122,15388,13543,12367,11997,13030,12439,13179,13179,13251,14134,14725,11854,10821,9126,8022,7437,8905)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '0'
> par3 = '0'
> par2 = '1'
> par1 = '48'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '12'
> par4 <- '0'
> par3 <- '0'
> 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/1qvht1407837725.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/24gm01407837725.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/3hjjs1407837725.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  0.948778819  0.879430443  0.814489031  0.759703845
 [6]  0.719597846  0.693055126  0.678784266  0.669116298  0.661720395
[11]  0.658521943  0.657278922  0.641908838  0.596163857  0.534543870
[16]  0.475839516  0.427374845  0.395069617  0.373092521  0.364497543
[21]  0.359272883  0.353551507  0.351007472  0.351059456  0.338361050
[26]  0.301107043  0.250408342  0.203461942  0.164171948  0.137389219
[31]  0.121580722  0.116938447  0.115497856  0.109950615  0.107806119
[36]  0.108227369  0.098726556  0.070157566  0.030071459 -0.007873278
[41] -0.039850675 -0.061314890 -0.072496709 -0.073226375 -0.071592921
[46] -0.074852196 -0.076151651 -0.073324699 -0.078486414
> (mypacf <- c(rpacf$acf))
 [1]  0.948778819 -0.207884825  0.040676384  0.048476812  0.091778973
 [6]  0.079397143  0.090850596  0.031369297  0.047957201  0.077319453
[11]  0.050780262 -0.114158150 -0.251562139 -0.099167628  0.007516830
[16]  0.006777346  0.040225866 -0.030355739  0.069832287  0.004931001
[21]  0.020347987  0.049237104  0.051339493 -0.066646230 -0.127501823
[26] -0.050398212  0.022222618 -0.033614099 -0.009017165 -0.025916446
[31]  0.024335495  0.005942637 -0.021240576  0.043022270  0.034092151
[36] -0.033339280 -0.060575582 -0.032125394  0.004640539 -0.033805791
[41] -0.011370374 -0.028973584  0.012675791 -0.007843359 -0.021645583
[46]  0.017931300  0.041800245 -0.025907607
> 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/4bszw1407837725.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/5g5mb1407837725.tab") 
> 
> try(system("convert tmp/1qvht1407837725.ps tmp/1qvht1407837725.png",intern=TRUE))
character(0)
> try(system("convert tmp/24gm01407837725.ps tmp/24gm01407837725.png",intern=TRUE))
character(0)
> try(system("convert tmp/3hjjs1407837725.ps tmp/3hjjs1407837725.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.145   0.232   1.385