R version 3.3.3 (2017-03-06) -- "Another Canoe"
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Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(101.16,100.81,100.94,101.13,101.29,101.34,101.35,101.7,102.05,102.48,102.66,102.72,102.73,102.18,102.22,102.37,102.53,102.61,102.62,103,103.17,103.52,103.69,103.73,99.57,99.09,99.14,99.36,99.6,99.65,99.8,100.15,100.45,100.89,101.13,101.17,101.21,101.1,101.17,101.11,101.2,101.15,100.92,101.1,101.22,101.25,101.39,101.43,101.95,101.92,102.05,102.07,102.1,102.16,101.63,101.43,101.4,101.6,101.72,101.73,102.67,102.59,102.69,102.93,103.02,103.06,102.47,102.4,102.42,102.51,102.61,102.78)
> 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. 1.2.327 (Mon, 30 Nov 2015 06:58:35 +0000)
> #Author: root
> #To cite this work: Wessa P., (2015), (Partial) Autocorrelation Function (v1.0.12) 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)
> x <- na.omit(x)
> 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/1g6g61495047512.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/2scll1495047512.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/3ulte1495047512.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  0.870407321  0.731561399  0.590472034  0.452460696
 [6]  0.312534154  0.178618422  0.058161893 -0.055962019 -0.158911757
[11] -0.247991411 -0.336245248 -0.433925588 -0.390150098 -0.338827890
[16] -0.289064100 -0.246454517 -0.203395637 -0.166431709 -0.132007788
[21] -0.091311029 -0.053119696 -0.028891708 -0.004089577  0.026174067
[26]  0.023475545  0.020972663  0.019640313  0.014377070  0.006806728
[31]  0.002886000 -0.007523734 -0.031617913 -0.060832327 -0.080589621
[36] -0.100039248 -0.122818005 -0.114868742 -0.108679221 -0.103529702
[41] -0.092514880 -0.078290225 -0.066665048 -0.053635110 -0.033758236
[46] -0.010486154  0.008633934  0.027507810  0.052138090
> (mypacf <- c(rpacf$acf))
 [1]  0.870407321 -0.107460652 -0.088383924 -0.075275677 -0.103859483
 [6] -0.080647080 -0.056983975 -0.091472323 -0.076369933 -0.069515676
[11] -0.130747104 -0.189287912  0.473815412 -0.075007281 -0.075659314
[16] -0.048287778 -0.057784029 -0.068435057  0.020052366  0.006280779
[21] -0.031763472 -0.049497309 -0.047244877 -0.110305708  0.216633927
[26] -0.012771970 -0.044126098 -0.067363138 -0.029660075 -0.045974514
[31] -0.019353882 -0.035845123 -0.050768189 -0.037402295 -0.043583320
[36] -0.113839584  0.220385432 -0.047304769 -0.045434462 -0.039981245
[41] -0.022838704 -0.041115799  0.014942535 -0.024577348 -0.033229857
[46] -0.003242317 -0.024054747 -0.078992961
> 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/4qbx21495047512.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/5xoia1495047512.tab") 
> 
> try(system("convert tmp/1g6g61495047512.ps tmp/1g6g61495047512.png",intern=TRUE))
character(0)
> try(system("convert tmp/2scll1495047512.ps tmp/2scll1495047512.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ulte1495047512.ps tmp/3ulte1495047512.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  2.375   0.329   2.741