R version 3.2.2 (2015-08-14) -- "Fire Safety"
Copyright (C) 2015 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> x <- c(989,1215,2911,2372,2013,2050,1580,1407,903,709,490,206,1101,1189,2877,2489,2145,1837,1613,1296,849,642,475,224,920,1263,2999,2988,2163,2391,1556,1089,976,626,392,203,1052,1034,2353,3075,2309,2009,1464,1099,1035,792,406,187,862,822,2128,2264,1987,1728,1311,1152,945,704,526,361,1035,869,2698,2367,1926,1843,1404,1314,1007,865,587,339,1143,1807,2380,2337,2117,1789,1569,1305,952,810,473,278,993,1038,2257,2284,1747,1515,1233,882,1029,707,391,239,592,692,2127,1854,1468,1535,1203,880,821,604,315,139,528,654,1895,1598,1519,1242,1027,762,735,485,281,131,651,611,1898,1385,1047,1008,843,833,711,444,315,204,473,566,1611,1301,1154,1158,862,801,559,404,223,158,548,647,1757,1326,1308,1175,992,808,758,553,310,146)
> 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/wessaorg/rcomp/tmp/1d0071448816699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=13.888888888889,height=8.3333333333333) 
> 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/26xba1448816699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=13.888888888889,height=8.3333333333333) 
> 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/3j29t1448816699.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=13.888888888889,height=8.3333333333333) 
> 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  0.729402649  0.432436666  0.070511180 -0.246238036
 [6] -0.407989119 -0.472242634 -0.418845995 -0.267124982  0.029724876
[11]  0.398270328  0.670199299  0.882361492  0.667026629  0.393110442
[16]  0.050089426 -0.240714946 -0.388538697 -0.452215677 -0.407293897
[21] -0.270267328 -0.001153448  0.329763162  0.578397427  0.771726889
[26]  0.583859437  0.334459592  0.028102208 -0.231309600 -0.369658764
[31] -0.421931778 -0.394770723 -0.276045926 -0.025151752  0.275563473
[36]  0.489161454  0.662312012  0.493151885  0.271388935 -0.005563543
[41] -0.225669033 -0.348453762 -0.401365409 -0.373397069 -0.269155984
[46] -0.052634569  0.217282930  0.417286986  0.558654029
> (mypacf <- c(rpacf$acf))
 [1]  0.729402649 -0.212815308 -0.350974786 -0.228673381  0.004582860
 [6] -0.096056126 -0.067958556  0.031022064  0.336248686  0.440445538
[11]  0.256215477  0.554214093 -0.385186545 -0.030125862 -0.087862879
[16]  0.108320305  0.073343179  0.058324789 -0.019097039 -0.075726832
[21] -0.050810930 -0.170494746 -0.003030227  0.131656068 -0.079245036
[26] -0.020066069 -0.010979429  0.027486810 -0.059031784  0.051582125
[31] -0.093411650  0.025850795  0.075485563 -0.019906914 -0.106923098
[36]  0.022198719 -0.080676737  0.014229140 -0.038924473  0.069487913
[41] -0.004678004 -0.031922876 -0.007222101 -0.042129332 -0.044831715
[46] -0.022349273  0.101841309 -0.062893756
> 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/4wk6w1448816699.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/5g4pe1448816699.tab") 
> 
> try(system("convert tmp/1d0071448816699.ps tmp/1d0071448816699.png",intern=TRUE))
character(0)
> try(system("convert tmp/26xba1448816699.ps tmp/26xba1448816699.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j29t1448816699.ps tmp/3j29t1448816699.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.516   0.272   1.795