R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(77.34,78.15,78.31,78.71,78.68,78.85,78.97,79.47,80.6,82.24,83.25,84.55,85.91,86.84,87.44,87.79,88.08,88.38,88.53,88.79,88.85,89.26,89.31,89.39,89.76,89.94,89.99,90.08,89.95,90.2,89.7,89.5,89.25,89.13,89.07,89.06,89.15,89.38,89.4,89.51,89.62,89.65,89.68,89.92,90.26,90.89,91.08,91.13,91.83,92.66,93.45,93.95,94.12,94.31,94.25,94.51,94.58,94.85,95.31,95.75,96.06,96.4,96.57,96.47,96.34,96.22,96.2,96.71,97.05,97.82,98.22,98.5,98.94,99.5,99.89,100,100.1,100.16,100.05,100.03,100,100.32,100.53,100.49,100.38,100.22,100.5,100.57,100.6,100.23,100.29,100.01,100.13,100.22,100.2,100.34,100.73,100.29,101.11,101.09,101.01,101.27,101.57,101.69,101,101.43,101.13,101.23)
> 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. 1.2.327 (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/19hud1476876038.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/28uw91476876038.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/3wcgt1476876038.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  0.965277673  0.930641037  0.893373965  0.855980597
 [6]  0.815509371  0.774246650  0.732545344  0.691579964  0.652963639
[11]  0.618543907  0.587597988  0.559200456  0.534839057  0.512956645
[16]  0.492434779  0.472704642  0.453774709  0.435116820  0.416966140
[21]  0.398871388  0.380828533  0.364006921  0.347570089  0.330749637
[26]  0.314301731  0.297834581  0.281243023  0.265040613  0.247814338
[31]  0.230853272  0.211673566  0.191337855  0.169649887  0.146984589
[36]  0.123960968  0.101035700  0.078055523  0.055282500  0.032202868
[41]  0.009898773 -0.012428622 -0.034693434 -0.057633404 -0.080693608
[46] -0.103468168 -0.124965342 -0.146245054 -0.167691478
> (mypacf <- c(rpacf$acf))
 [1]  0.965277673 -0.016412163 -0.056496751 -0.021685921 -0.064031924
 [6] -0.034185160 -0.026783239 -0.012415273  0.012834050  0.039344011
[11]  0.028918640  0.013545524  0.034721730  0.011778330 -0.005514016
[16] -0.010647015 -0.009262072 -0.012243189 -0.004586837 -0.007657128
[21] -0.006166546  0.013836811  0.001344399 -0.012954921 -0.002402603
[26] -0.010180998 -0.013771754 -0.005700239 -0.026946988 -0.008422337
[31] -0.042108515 -0.030699790 -0.031336172 -0.028131664 -0.017413977
[36] -0.013268213 -0.016282543 -0.015114112 -0.025718399 -0.013640944
[41] -0.027391429 -0.028296395 -0.039220948 -0.032310609 -0.024418102
[46] -0.008430863 -0.022055117 -0.028713513
> 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/4ofz81476876038.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/5d4gs1476876038.tab") 
> 
> try(system("convert tmp/19hud1476876038.ps tmp/19hud1476876038.png",intern=TRUE))
character(0)
> try(system("convert tmp/28uw91476876038.ps tmp/28uw91476876038.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wcgt1476876038.ps tmp/3wcgt1476876038.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.159   0.076   1.247