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Type 'q()' to quit R. > x <- c(19064,18993,18921,18772,20246,20168,19064,18330,18401,18401,18480,18622,18843,18843,18701,18330,20246,20538,20097,19064,19506,18843,19142,19285,19434,19064,19142,18622,20246,20759,20318,19506,20389,19434,20318,20246,20467,19655,20538,20467,21792,21493,20318,19726,20538,19434,20246,20389,20688,20026,20389,20610,21422,20759,19876,18921,19805,17375,18551,19213,19876,18921,18921,18921,19434,18701,17739,16934,17518,15238,16635,17447,17596,16784,16855,16635,17375,16855,15830,15089,16342,13621,15388,16193,16193,15238,14355,14284,15089,14355,12959,11997,13030,10601,12809,13984,14355,13543,12517,13251,13543,13322,11113,10088,10821,8613,10893,11705,12367,11263,10230,10821,11113,10529,8321,7359,8242,5813,8463,10088) > par8 = '' > par7 = '0.95' > par6 = 'White Noise' > par5 = '12' > par4 = '0' > par3 = '0' > par2 = '1' > par1 = '60' > par8 <- '' > par7 <- '0.95' > par6 <- 'White Noise' > par5 <- '12' > par4 <- '0' > par3 <- '0' > par2 <- '1' > par1 <- '60' > #'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/www/rcomp/tmp/1qqeb1344867517.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/www/rcomp/tmp/2bqfj1344867517.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/www/rcomp/tmp/3cxvv1344867517.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 0.94790270 0.90526650 0.85572917 0.82620574 0.78568019 [7] 0.75796764 0.73618879 0.72365381 0.70318234 0.69210227 0.68757109 [13] 0.70013293 0.64795997 0.60883582 0.56351155 0.53636291 0.49797273 [19] 0.47383529 0.45269086 0.44000952 0.41829281 0.40339409 0.39332445 [25] 0.39689828 0.34807438 0.31176278 0.26871560 0.24335965 0.20637754 [31] 0.18441923 0.16033689 0.14527907 0.11903014 0.10227925 0.08818946 [37] 0.08556447 0.04215289 0.01252690 -0.02312118 -0.04234608 -0.07005548 [43] -0.08184472 -0.09661699 -0.10509792 -0.12495868 -0.13603322 -0.14763354 [49] -0.15225762 -0.18899059 -0.21575438 -0.24709827 -0.26520359 -0.28610998 [55] -0.28967782 -0.29637289 -0.29848687 -0.31074077 -0.31241640 -0.31423313 [61] -0.31141019 > (mypacf <- c(rpacf$acf)) [1] 0.947902704 0.066485325 -0.082604086 0.161755865 -0.095006059 [6] 0.075233458 0.103347820 0.049089316 -0.040338340 0.088259354 [11] 0.102412095 0.170862837 -0.617564204 0.140117179 0.079109014 [16] -0.101788354 0.062871591 0.038416397 -0.041004810 0.026510362 [21] -0.026506963 0.084726623 -0.085814386 -0.059736140 -0.132116511 [26] 0.009564056 0.006385721 -0.014107807 -0.004642954 0.005718883 [31] -0.091585886 0.033591062 -0.043549144 0.058511435 -0.071713151 [36] -0.075129215 0.014526850 0.007308771 -0.021754896 0.017297289 [41] 0.014217720 0.003879167 -0.029020003 -0.021908450 0.016656330 [46] -0.028062043 -0.067435236 -0.046561609 0.012667734 -0.087237450 [51] 0.019648908 -0.004770787 0.042575686 0.017944565 -0.042268339 [56] -0.000409937 0.024530475 -0.024164463 0.035737065 -0.053952244 > lengthx <- length(x) > sqrtn <- sqrt(lengthx) > > #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/www/rcomp/tmp/4y2t51344867517.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/www/rcomp/tmp/56i241344867517.tab") > > try(system("convert tmp/1qqeb1344867517.ps tmp/1qqeb1344867517.png",intern=TRUE)) character(0) > try(system("convert tmp/2bqfj1344867517.ps tmp/2bqfj1344867517.png",intern=TRUE)) character(0) > try(system("convert tmp/3cxvv1344867517.ps tmp/3cxvv1344867517.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 0.920 0.410 1.346