R version 3.0.1 (2013-05-16) -- "Good Sport"
Copyright (C) 2013 The R Foundation for Statistical Computing
Platform: i686-pc-linux-gnu (32-bit)

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> x <- c(1165010
+ ,1160665
+ ,1156265
+ ,1147162.5
+ ,1237238.75
+ ,1232481.25
+ ,1165010
+ ,1120157.5
+ ,1124488.75
+ ,1124488.75
+ ,1129328.75
+ ,1138005
+ ,1151507.5
+ ,1151507.5
+ ,1142831.25
+ ,1120157.5
+ ,1237238.75
+ ,1255086.25
+ ,1228136.25
+ ,1165010
+ ,1192015
+ ,1151507.5
+ ,1169781.25
+ ,1178512.5
+ ,1187615
+ ,1165010
+ ,1169781.25
+ ,1138005
+ ,1237238.75
+ ,1268588.75
+ ,1241638.75
+ ,1192015
+ ,1245983.75
+ ,1187615
+ ,1241638.75
+ ,1237238.75
+ ,1250755
+ ,1201131.25
+ ,1255086.25
+ ,1250755
+ ,1331715
+ ,1313441.25
+ ,1241638.75
+ ,1205462.5
+ ,1255086.25
+ ,1187615
+ ,1237238.75
+ ,1245983.75
+ ,1264257.5
+ ,1223805
+ ,1245983.75
+ ,1259486.25
+ ,1309110
+ ,1268588.75
+ ,1214633.75
+ ,1156265
+ ,1210288.75
+ ,1061788.75
+ ,1133660
+ ,1174112.5
+ ,1214633.75
+ ,1156265
+ ,1156265
+ ,1156265
+ ,1187615
+ ,1142831.25
+ ,1084036.25
+ ,1034838.75
+ ,1070533.75
+ ,931205
+ ,1016578.75
+ ,1066188.75
+ ,1075305
+ ,1025681.25
+ ,1030012.5
+ ,1016578.75
+ ,1061788.75
+ ,1030012.5
+ ,967381.25
+ ,922102.5
+ ,998662.5
+ ,832383.75
+ ,940362.5
+ ,989560
+ ,989560
+ ,931205
+ ,877236.25
+ ,872905
+ ,922102.5
+ ,877236.25
+ ,791931.25
+ ,733136.25
+ ,796276.25
+ ,647831.25
+ ,782760
+ ,854562.5
+ ,877236.25
+ ,827626.25
+ ,764926.25
+ ,809778.75
+ ,827626.25
+ ,814110
+ ,679126.25
+ ,616481.25
+ ,661278.75
+ ,526350
+ ,665678.75
+ ,715302.5
+ ,755755
+ ,688283.75
+ ,625157.5
+ ,661278.75
+ ,679126.25
+ ,643431.25
+ ,508502.5
+ ,449707.5
+ ,503676.25
+ ,355231.25
+ ,517178.75
+ ,616481.25)
> par8 = ''
> par7 = '0.95'
> par6 = 'White Noise'
> par5 = '12'
> par4 = '1'
> par3 = '0'
> par2 = '1'
> par1 = '48'
> par8 <- ''
> par7 <- '0.95'
> par6 <- 'White Noise'
> par5 <- '12'
> par4 <- '1'
> 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/145e31377008932.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/27z111377008932.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/3rgof1377008932.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  0.95483706  0.91767825  0.87054878  0.84122472  0.80316613
 [7]  0.75925008  0.72009846  0.68308020  0.65683176  0.63370937  0.62124201
[13]  0.60074852  0.58031114  0.56474216  0.54893724  0.53006862  0.50522119
[19]  0.48065213  0.46415389  0.45399005  0.42994715  0.39621655  0.35120922
[25]  0.31288227  0.27314408  0.23689484  0.19642480  0.15653573  0.11766610
[31]  0.07621108  0.04743337  0.01487450 -0.01390142 -0.03481100 -0.05271676
[37] -0.06750354 -0.09008486 -0.10722064 -0.12152162 -0.13365647 -0.14500618
[43] -0.15873954 -0.17369545 -0.18822381 -0.19761180 -0.21201169 -0.22345268
[49] -0.23691045
> (mypacf <- c(rpacf$acf))
 [1]  0.9548370564  0.0675580361 -0.1251062398  0.1622310881 -0.0805196977
 [6] -0.1403028895  0.0765677338 -0.0009626425  0.0629456280  0.0765976486
[11]  0.1023203006 -0.0843841551 -0.0401192410  0.0847847062 -0.0565575031
[16] -0.0699805767 -0.0017400235 -0.0200331455  0.0826266122  0.0930911158
[21] -0.1850543792 -0.1545285880 -0.1255557424 -0.0276774473 -0.0632308204
[26]  0.0216212979 -0.0005093594 -0.0323317165 -0.0167452742 -0.1299563077
[31]  0.0016023662 -0.0472667898 -0.0397525162  0.1540185394  0.0015088835
[36] -0.0192348180 -0.0257924245  0.0248781819  0.0006858834 -0.0784322612
[41]  0.0542963300  0.0141909942  0.0095903788  0.1156667535  0.0361261270
[46] -0.1317906286  0.0293592820 -0.0166149236
> 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/4q0511377008932.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/5vodl1377008932.tab") 
> 
> try(system("convert tmp/145e31377008932.ps tmp/145e31377008932.png",intern=TRUE))
character(0)
> try(system("convert tmp/27z111377008932.ps tmp/27z111377008932.png",intern=TRUE))
character(0)
> try(system("convert tmp/3rgof1377008932.ps tmp/3rgof1377008932.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.400   0.576   2.953