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Type 'q()' to quit R. > x <- c(2.08,2.09,2.36,2.99,2.75,1.58,1.69,1.3,1.97,1.84,1.96,1.86,2.75,2.62,2.41,3.61,2.03,1.45,1.4,1.3,1.58,2.1,2.27,2.54,2.55,2.05,2.32,2.6,2.1,1.61,1.55,1.12,1.39,2.18,1.94,2.27,2.41,2.2,2.58,2.9,2.12,1.34,1.07,0.86,1,1.54,1.29,1.44,2.6,2.77,3.31,3.2,2.07,1.42,1.43,1.28,1.59,1.68,2.01,2.52,2.74,3.06,2.69,2.32,1.67,1.04,0.98,0.86,0.97,1.3,1.82) > par8 = '' > par7 = '0.95' > par6 = 'White Noise' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '1' > par1 = '36' > par8 <- '' > par7 <- '0.95' > par6 <- 'White Noise' > par5 <- '12' > par4 <- '0' > par3 <- '1' > par2 <- '1' > par1 <- '36' > #'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/1dzj91353588563.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/2e5ap1353588563.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/34dqg1353588563.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] 1.0000000000 0.0928433922 -0.0381588898 0.0281222362 -0.3359236443 [6] -0.2417599495 -0.0442477758 -0.2590823088 -0.2746706251 0.1648820150 [11] -0.0242861019 0.3121310949 0.5102817278 0.0679237431 -0.1214778064 [16] 0.0507057971 -0.2198203795 -0.2423589005 0.0157419831 -0.2394726662 [21] -0.0856250152 0.1049822805 -0.0001063929 0.1551815145 0.3198020314 [26] 0.0766735896 -0.0163890960 0.0643222763 -0.1441283122 -0.1473459958 [31] -0.0095184258 -0.1645312417 -0.1672794700 0.0582175814 -0.0486028964 [36] 0.1836308385 0.2942189934 > (mypacf <- c(rpacf$acf)) [1] 0.092843392 -0.047185519 0.036609446 -0.348274247 -0.192696505 [6] -0.057497308 -0.297043788 -0.471400628 -0.112948708 -0.368857137 [11] -0.022266102 0.137191990 0.006297223 -0.281323988 0.049973687 [16] -0.014650058 -0.108667175 -0.050495754 0.047643799 0.038700314 [21] -0.077336495 -0.200174485 -0.176400292 -0.173406913 -0.014950780 [26] -0.067033151 -0.114707932 -0.019766397 0.005103715 0.003572541 [31] -0.068465764 -0.157802951 -0.009197141 -0.098484996 0.108227254 [36] 0.023423754 > 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/4a3511353588563.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/5vzz91353588563.tab") > > try(system("convert tmp/1dzj91353588563.ps tmp/1dzj91353588563.png",intern=TRUE)) character(0) > try(system("convert tmp/2e5ap1353588563.ps tmp/2e5ap1353588563.png",intern=TRUE)) character(0) > try(system("convert tmp/34dqg1353588563.ps tmp/34dqg1353588563.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 2.533 0.534 3.050