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(655362,873127,1107897,1555964,1671159,1493308,2957796,2638691,1305669,1280496,921900,867888,652586,913831,1108544,1555827,1699283,1509458,3268975,2425016,1312703,1365498,934453,775019,651142,843192,1146766,1652601,1465906,1652734,2922334,2702805,1458956,1410363,1019279,936574,708917,885295,1099663,1576220,1487870,1488635,2882530,2677026,1404398,1344370,936865,872705,628151,953712,1160384,1400618,1661511,1495347,2918786,2775677,1407026,1370199,964526,850851,683118,847224,1073256,1514326,1503734,1507712,2865698,2788128,1391596,1366378,946295,859626)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '1'
> par3 = '0'
> par2 = '1'
> par1 = '60'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '12'
> par4 <- '1'
> 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/153o91386160397.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/244l21386160397.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/31w9r1386160397.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]  1.000000e+00 -3.166481e-01  1.903176e-01  8.747929e-02 -2.932563e-02
 [6]  7.620049e-02 -7.191902e-02 -1.764528e-01 -5.234527e-02 -4.345873e-02
[11] -2.293433e-01  1.805673e-01 -4.266669e-01  1.128046e-01 -2.853430e-03
[16] -1.001214e-02  1.263418e-02  6.757008e-02  1.702227e-01  3.282411e-02
[21]  3.203348e-02  1.360642e-01 -4.076396e-02  1.555221e-01 -8.288086e-02
[26] -1.386421e-02  7.818742e-03 -9.057782e-02  9.220450e-03 -4.317972e-02
[31] -1.974041e-01  6.405615e-02  7.036630e-02 -2.221025e-01  1.854050e-01
[36] -1.368962e-01  1.052304e-01  7.978154e-03  4.153867e-03  3.121926e-02
[41]  4.908741e-03 -1.375745e-02  7.482684e-02 -3.444568e-02 -7.222102e-02
[46]  8.968640e-02 -7.362931e-02  2.572777e-02 -4.091169e-02  2.840196e-03
[51]  1.153577e-04 -5.389560e-03 -9.905288e-03  6.132930e-05  3.755885e-04
[56] -5.980291e-04 -1.782076e-04 -1.018725e-03  5.551934e-04 -3.620793e-05
> (mypacf <- c(rpacf$acf))
 [1] -0.316648088  0.100086848  0.194677893  0.034242915  0.030075013
 [6] -0.070430173 -0.272882885 -0.222209683 -0.037029966 -0.184094277
[11]  0.150914339 -0.295595915 -0.176761145 -0.025234622  0.066028363
[16] -0.051199263  0.024827524  0.206611209 -0.024439106 -0.196181063
[21]  0.125377225 -0.131383759  0.244203716 -0.070410683 -0.027724514
[26]  0.005305070 -0.003510558  0.025104817  0.053877429 -0.027506270
[31]  0.054997007  0.036323784 -0.015275403 -0.074449977  0.088937042
[36] -0.039483346 -0.082713375  0.052888444 -0.031660477 -0.104061828
[41] -0.043038689 -0.012984547 -0.012959871 -0.030404927 -0.006173431
[46] -0.088201245  0.028428362  0.032264700 -0.133265485  0.017254660
[51]  0.049158475 -0.090021908 -0.039830165  0.023193789 -0.058577750
[56] -0.002544824 -0.013453873  0.028274568 -0.016511772
> 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/4zjzw1386160397.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/5mk051386160397.tab") 
> 
> try(system("convert tmp/153o91386160397.ps tmp/153o91386160397.png",intern=TRUE))
character(0)
> try(system("convert tmp/244l21386160397.ps tmp/244l21386160397.png",intern=TRUE))
character(0)
> try(system("convert tmp/31w9r1386160397.ps tmp/31w9r1386160397.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.848   0.803   3.595