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) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- c(726,784,884,696,893,674,703,799,793,799,1022,758,1021,944,915,864,1022,891,1087,822,890,1092,967,833,1104,1063,1103,1039,1185,1047,1155,878,879,1133,920,943,938,900,781,1040,792,653,866,679,799,760,699,762,671,679,862,624,516,650,583,444,562,540,524,683) > 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/1wdwe1476884992.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/23xzm1476884992.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/37i331476884992.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 0.628289805 0.651855348 0.691637187 0.551457681 [6] 0.472126555 0.512168865 0.307757760 0.362898792 0.315408440 [11] 0.199507329 0.230959066 0.204743743 0.019226213 0.120688440 [16] 0.005046151 -0.078113083 -0.061731092 -0.133379970 -0.269847629 [21] -0.217076864 -0.255064439 -0.350881114 -0.304189967 -0.350302051 [26] -0.409015140 -0.324423439 -0.402870802 -0.425325374 -0.329388524 [31] -0.382630220 -0.347167918 -0.222873990 -0.271605800 -0.233649526 [36] -0.143099148 -0.179454492 -0.148559538 -0.099628069 -0.158569310 [41] -0.075718075 -0.086085004 -0.090527224 -0.037596468 -0.035292983 [46] -0.034032004 0.027125479 0.030209885 0.055282571 > (mypacf <- c(rpacf$acf)) [1] 0.628289805 0.424793809 0.381355714 -0.030953012 -0.193942233 [6] 0.044911748 -0.251432300 0.117033460 0.043152015 -0.050068158 [11] 0.056064551 -0.022859436 -0.256667266 0.019782629 -0.098725596 [16] -0.024685170 -0.024649993 -0.054608487 -0.181060082 -0.133282371 [21] 0.172855934 -0.069338227 0.017540307 -0.024151616 -0.132514594 [26] 0.025826107 -0.044695815 -0.054709450 0.108133986 0.043311811 [31] 0.133225117 0.098726648 -0.001643805 -0.040604180 -0.135355352 [36] 0.050337117 -0.086238265 -0.084052689 -0.019446599 0.035254022 [41] -0.072702385 0.017992934 -0.066064109 -0.092297331 0.055203268 [46] -0.014517494 -0.020248957 0.051622570 > 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/420kq1476884992.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/5jbmz1476884992.tab") > > try(system("convert tmp/1wdwe1476884992.ps tmp/1wdwe1476884992.png",intern=TRUE)) character(0) > try(system("convert tmp/23xzm1476884992.ps tmp/23xzm1476884992.png",intern=TRUE)) character(0) > try(system("convert tmp/37i331476884992.ps tmp/37i331476884992.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 1.215 0.089 1.327