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(58608,46865,51378,46235,47206,45382,41227,33795,31295,42625,33625,21538,56421,53152,53536,52408,41454,38271,35306,26414,31917,38030,27534,18387,50556,43901,48572,43899,37532,40357,35489,29027,34485,42598,30306,26451,47460,50104,61465,53726,39477,43895,31481,29896,33842,39120,33702,25094,51442,45594,52518,48564,41745,49585,32747,33379,35645,37034,35681,20972,58552,54955,65540,51570,51145,46641,35704,33253,35193,41668,34865,21210,56126,49231,59723,48103,47472,50497,40059,34149,36860,46356,36577,23872,57276,56389,57657,62300,48929,51168,39636,33213,38127,43291,30600,21956,48033,46148,50736,48114,38390,44112,36287,30333,35908,40005,35263,26591,49771,47882,64830,57846,48188,54400,39778,37772,37214,43829,40701,29450,53597,53588,64172,53955,55509,48908,35331,38073,41776,42717,40736,49020,45099,44114,60487,48760,41281,48346,37025,31514,33977,42060,36036,22012)
> 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/fisher/rcomp/tmp/11n6q1384973474.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/2snc21384973474.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/3tdi91384973474.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  0.41612333  0.19486892  0.05959833 -0.14656377 -0.27575676
 [7] -0.44474964 -0.27540364 -0.18936950 -0.02414096  0.10898914  0.30350611
[13]  0.75055888  0.32167805  0.14029308 -0.04555781 -0.21530114 -0.28482135
[19] -0.48148537 -0.32979841 -0.19911316 -0.05955150  0.03454227  0.27028596
[25]  0.69199468  0.30272684  0.13596935 -0.04075672 -0.20103546 -0.26057829
[31] -0.45826678 -0.29073012 -0.18607222 -0.06956336  0.04206950  0.22169406
[37]  0.60395582  0.27728575  0.13656631 -0.01224295 -0.14595140 -0.21823862
[43] -0.39109108 -0.24232531 -0.14762087 -0.05976826  0.05910118  0.20398414
[49]  0.57138007
> (mypacf <- c(rpacf$acf))
 [1]  0.416123326  0.026256905 -0.036656387 -0.198176684 -0.184552834
 [6] -0.311480345  0.036741374 -0.053189079  0.083109458  0.011834926
[11]  0.195564574  0.649551746 -0.300837756 -0.134541288 -0.304962359
[16] -0.044319508  0.125382380 -0.084241660 -0.099260603  0.045779581
[21] -0.005892054 -0.064508275  0.102157224  0.203777467 -0.096012897
[26] -0.105113050 -0.068531920  0.020750198  0.067019262 -0.047414297
[31]  0.033403447 -0.054519508 -0.040312583  0.048084011 -0.114563142
[36]  0.022341203 -0.092132035  0.027091713  0.182863344  0.072432406
[41] -0.113477866  0.049117510 -0.045504817  0.047003844 -0.009973798
[46]  0.062207340 -0.047123533  0.065237732
> 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/4czp61384973474.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/5783v1384973474.tab") 
> 
> try(system("convert tmp/11n6q1384973474.ps tmp/11n6q1384973474.png",intern=TRUE))
character(0)
> try(system("convert tmp/2snc21384973474.ps tmp/2snc21384973474.png",intern=TRUE))
character(0)
> try(system("convert tmp/3tdi91384973474.ps tmp/3tdi91384973474.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.681   0.381   2.045