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Type 'q()' to quit R. > x <- c(155.28,173.24,180.16,181.52,182.25,182.19,182,181.65,180.07,182.62,180.38,181.15,180.5,181.14,180.93,211.91,223.81,226.88,226.8,231.81,232.06,232.32,228.37,226.31,225.72,219.98,219.31,215.19,213.81,213.7,213.6,213.52,218.39,219.97,221.09,219.17,219.17,218.45,216.88,216.19,214.59,269.87,272.71,280.35,274.5,268.86,261.7,263.98,263.01,262.79,263.59,267,267.89,267.86,266.84,268.24,267.67,269.07,270.87,271.68,271.63,275.21,276.66,276.08,278.3,279.06,279.28,279.12,262.72,262.55,260.7,259.14,260.61,260.53,259.07,257.01,257.08,256.83,256.75,257.61,258.58,259.57,259.29,258.51) > 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/wessaorg/rcomp/tmp/1bk5z1384768583.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/2uex81384768583.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/3by4u1384768583.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 0.93761815 0.88300003 0.82804016 0.77863580 0.73229628 [7] 0.69111991 0.65014668 0.61136741 0.57119575 0.53103202 0.48866288 [13] 0.44738141 0.40743328 0.36781700 0.32273656 0.30103110 0.28762316 [19] 0.27437121 0.26046907 0.25225237 0.24354467 0.23502769 0.22063757 [25] 0.20380605 0.18462017 0.15638515 0.11192900 0.07284013 0.03054360 [31] -0.00791431 -0.04456368 -0.07994273 -0.11346240 -0.14295364 -0.16956173 [37] -0.19749857 -0.22543580 -0.25339678 -0.28548843 -0.32323853 -0.37510050 [43] -0.38099011 -0.38518542 -0.37092418 -0.36083655 -0.35530402 -0.35854234 [49] -0.36039359 -0.36347449 -0.36767286 -0.37194658 -0.37257888 -0.36933864 [55] -0.36093622 -0.35315187 -0.34336039 -0.33435881 -0.32574740 -0.31607008 [61] -0.30610128 > (mypacf <- c(rpacf$acf)) [1] 0.9376181465 0.0320358053 -0.0280836865 0.0158764642 0.0028640231 [6] 0.0190852951 -0.0160236845 -0.0040025484 -0.0293141224 -0.0238777132 [11] -0.0408994068 -0.0204241979 -0.0152809699 -0.0262223800 -0.0743832882 [16] 0.1566737040 0.0733600755 -0.0088701265 -0.0068949557 0.0478178537 [21] 0.0098843949 -0.0041252251 -0.0506664130 -0.0382885457 -0.0355342394 [26] -0.1099803690 -0.1838529421 -0.0136694100 -0.0678143515 -0.0545747735 [31] -0.0070987644 0.0009310579 -0.0033277031 0.0056080132 0.0316117006 [36] -0.0085223758 -0.0073838191 -0.0416526491 -0.0865374202 -0.1041533507 [41] -0.2303499695 0.2414146665 0.0053673995 0.1232951310 -0.0262732367 [46] -0.0403483195 -0.0520097721 0.0033618767 0.0178724843 -0.0127550775 [51] -0.0063087445 0.0028476622 0.0205767162 0.0323412362 -0.0362405157 [56] -0.0850906538 0.0777424023 0.0380686672 0.0542990476 -0.0070287472 > 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/4n62d1384768583.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/55y941384768583.tab") > > try(system("convert tmp/1bk5z1384768583.ps tmp/1bk5z1384768583.png",intern=TRUE)) character(0) > try(system("convert tmp/2uex81384768583.ps tmp/2uex81384768583.png",intern=TRUE)) character(0) > try(system("convert tmp/3by4u1384768583.ps tmp/3by4u1384768583.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.500 0.528 2.996