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(12374.6,12864.7,14905.8,12259.7,14088.9,14243.7,12732.8,11612,14176.6,14452.6,14512.7,12645.1,13820.5,13644.7,15684.1,13568.3,14531.1,15320.1,14344.2,12899.4,14462,16044.7,14731.2,12798.3,14213.1,14683.3,14652,15623.1,14880.4,15765.7,15433.1,12402.6,15639.8,14861.7,11699.4,10651.9,10086.9,10676.9,11332.1,10756.1,10450.5,11930.2,11419.9,9713.1,12608.5,12357.2,12107.9,11627.2,11105.9,11841.6,14290.8,13271.7,12909.4,14924.1,13257.4,12184.4,15035.5,14401,14165,13375.6,14210.8,15017.5,17157.8,15106.2,16696.1,16035.9,15418.9,13763.9,15595.2,15183.1,15515.9,14142.8,15012.7,16293.2,17771.4,15582.8,16049.9,16105.8,15623.6,14254.9,15266.8,16671,15665.4,13949.5,15146.9,15172.9,16981.4,16553.8,16438.5,15895.1,16989,13803.5,16678.3,17315.1,15895.4,14912.1)
> 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/1aert1425512981.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/2n47f1425512981.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/3ox4r1425512981.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  0.679976063  0.561697098  0.595205796  0.597797189
 [6]  0.488586904  0.479608406  0.380487454  0.409221887  0.283678820
[11]  0.181037176  0.259971466  0.396226899  0.171983607  0.042989158
[16]  0.053357920  0.088231550  0.011323553 -0.004297563 -0.047618530
[21] -0.012148775 -0.127265298 -0.180029365 -0.101457757 -0.007676131
[26] -0.142632195 -0.231526995 -0.206764655 -0.094174515 -0.133507990
[31] -0.112571865 -0.109703420 -0.079492344 -0.130731884 -0.158896713
[36] -0.115750851 -0.017479728 -0.115777165 -0.232151070 -0.187731052
[41] -0.114236293 -0.152139073 -0.128162167 -0.144088262 -0.128429331
[46] -0.162980889 -0.187376368 -0.164303808 -0.054004236
> (mypacf <- c(rpacf$acf))
 [1]  0.679976063  0.184753790  0.304655904  0.181021028 -0.060336304
 [6]  0.084950856 -0.190815966  0.146227922 -0.254373545 -0.126543183
[11]  0.235969814  0.318077938 -0.275886762 -0.320688577 -0.111595683
[16]  0.097709183  0.048098375  0.046518530 -0.032221177  0.015179621
[21] -0.075009107  0.004708246 -0.036197760 -0.055368115  0.014470747
[26] -0.017029124  0.033930821  0.165244708  0.008771355  0.072573210
[31] -0.127261773 -0.102215454  0.043018976 -0.071075822 -0.067785102
[36] -0.026979718  0.047865814 -0.090602188  0.029258521 -0.070056487
[41] -0.053486061  0.075648717 -0.036119511  0.041920277 -0.077276968
[46]  0.021910864 -0.028979537  0.005594158
> 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/4vy4f1425512981.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/53z141425512981.tab") 
> 
> try(system("convert tmp/1aert1425512981.ps tmp/1aert1425512981.png",intern=TRUE))
character(0)
> try(system("convert tmp/2n47f1425512981.ps tmp/2n47f1425512981.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ox4r1425512981.ps tmp/3ox4r1425512981.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.189   0.212   1.414