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Type 'q()' to quit R. > x <- c(276986 + ,260633 + ,291551 + ,275383 + ,275302 + ,231693 + ,238829 + ,274215 + ,277808 + ,299060 + ,286629 + ,232313 + ,294053 + ,267510 + ,309739 + ,280733 + ,287298 + ,235672 + ,256449 + ,288997 + ,290789 + ,321898 + ,291834 + ,241380 + ,295469 + ,258200 + ,306102 + ,281480 + ,283101 + ,237414 + ,274834 + ,299340 + ,300383 + ,340862 + ,318794 + ,265740 + ,322656 + ,281563 + ,323461 + ,312579 + ,310784 + ,262785 + ,273754 + ,320036 + ,310336 + ,342206 + ,320052 + ,265582 + ,326988 + ,300713 + ,346414 + ,317325 + ,326208 + ,270657 + ,278158 + ,324584 + ,321801 + ,343542 + ,354040 + ,278179 + ,330246 + ,307344 + ,375874 + ,335309 + ,339271 + ,280264 + ,293689 + ,341161 + ,345097 + ,368712 + ,369403 + ,288384 + ,340981 + ,319072 + ,374214 + ,344529 + ,337271 + ,281016 + ,282224 + ,320984 + ,325426 + ,366276 + ,380296 + ,300727 + ,359326 + ,327610 + ,383563 + ,352405 + ,329351 + ,294486 + ,333454 + ,334339 + ,358000 + ,396057 + ,386976 + ,307155 + ,363909 + ,344700 + ,397561 + ,376791 + ,337085 + ,299252 + ,323136 + ,329091 + ,346991 + ,461999 + ,436533 + ,360372 + ,415467 + ,382110 + ,432197 + ,424254 + ,386728 + ,354508 + ,375765 + ,367986 + ,402378 + ,426516 + ,433313 + ,338461 + ,416834 + ,381099 + ,445673 + ,412408 + ,393997 + ,348241 + ,380134 + ,373688 + ,393588 + ,434192 + ,430731 + ,344468 + ,411891 + ,370497 + ,437305 + ,411270 + ,385495 + ,341273 + ,384217 + ,373223 + ,415771 + ,448634 + ,454341 + ,350297 + ,419104 + ,398027 + ,456059 + ,430052 + ,399757 + ,362731 + ,384896 + ,385349 + ,432289 + ,468891 + ,442702 + ,370178 + ,439400 + ,393900 + ,468700 + ,438800 + ,430100 + ,366300 + ,391000 + ,380900 + ,431400 + ,465400 + ,471500 + ,387500 + ,446400 + ,421500 + ,504800 + ,492071 + ,421253 + ,396682 + ,428000 + ,421900 + ,465600 + ,525793 + ,499855 + ,435287 + ,479499 + ,473027 + ,554410 + ,489574 + ,462157 + ,420331) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '2' > par2 = '0.5' > par1 = 'FALSE' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > if (par1 == 'TRUE') par1 <- TRUE > if (par1 == 'FALSE') par1 <- FALSE > par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter > par3 <- as.numeric(par3) #degree of non-seasonal differencing > par4 <- as.numeric(par4) #degree of seasonal differencing > par5 <- as.numeric(par5) #seasonal period > par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial > par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial > par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial > par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial > armaGR <- function(arima.out, names, n){ + try1 <- arima.out$coef + try2 <- sqrt(diag(arima.out$var.coef)) + try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names))) + dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv')) + try.data.frame[,1] <- try1 + for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i] + try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2] + try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5) + vector <- rep(NA,length(names)) + vector[is.na(try.data.frame[,4])] <- 0 + maxi <- which.max(try.data.frame[,4]) + continue <- max(try.data.frame[,4],na.rm=TRUE) > .05 + vector[maxi] <- 0 + list(summary=try.data.frame,next.vector=vector,continue=continue) + } > arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){ + nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3] + coeff <- matrix(NA, nrow=nrc*2, ncol=nrc) + pval <- matrix(NA, nrow=nrc*2, ncol=nrc) + mylist <- rep(list(NULL), nrc) + names <- NULL + if(order[1] > 0) names <- paste('ar',1:order[1],sep='') + if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') ) + if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep='')) + if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep='')) + arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML') + mylist[[1]] <- arima.out + last.arma <- armaGR(arima.out, names, length(series)) + mystop <- FALSE + i <- 1 + coeff[i,] <- last.arma[[1]][,1] + pval [i,] <- last.arma[[1]][,4] + i <- 2 + aic <- arima.out$aic + while(!mystop){ + mylist[[i]] <- arima.out + arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector) + aic <- c(aic, arima.out$aic) + last.arma <- armaGR(arima.out, names, length(series)) + mystop <- !last.arma$continue + coeff[i,] <- last.arma[[1]][,1] + pval [i,] <- last.arma[[1]][,4] + i <- i+1 + } + list(coeff, pval, mylist, aic=aic) + } > arimaSelectplot <- function(arimaSelect.out,noms,choix){ + noms <- names(arimaSelect.out[[3]][[1]]$coef) + coeff <- arimaSelect.out[[1]] + k <- min(which(is.na(coeff[,1])))-1 + coeff <- coeff[1:k,] + pval <- arimaSelect.out[[2]][1:k,] + aic <- arimaSelect.out$aic[1:k] + coeff[coeff==0] <- NA + n <- ncol(coeff) + if(missing(choix)) choix <- k + layout(matrix(c(1,1,1,2, + 3,3,3,2, + 3,3,3,4, + 5,6,7,7),nr=4), + widths=c(10,35,45,15), + heights=c(30,30,15,15)) + couleurs <- rainbow(75)[1:50]#(50) + ticks <- pretty(coeff) + par(mar=c(1,1,3,1)) + plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA) + points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA) + title('aic',line=2) + par(mar=c(3,0,0,0)) + plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1)) + rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)), + xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)), + ytop = rep(1,50), + ybottom= rep(0,50),col=couleurs,border=NA) + axis(1,ticks) + rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0) + text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2) + par(mar=c(1,1,3,1)) + image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks)) + for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) { + if(pval[j,i]<.01) symb = 'green' + else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange' + else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red' + else symb = 'black' + polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5), + c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5), + col=symb) + if(j==choix) { + rect(xleft=i-.5, + xright=i+.5, + ybottom=k-j+1.5, + ytop=k-j+.5, + lwd=4) + text(i, + k-j+1, + round(coeff[j,i],2), + cex=1.2, + font=2) + } + else{ + rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5) + text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1) + } + } + axis(3,1:n,noms) + par(mar=c(0.5,0,0,0.5)) + plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8)) + cols <- c('green','orange','red','black') + niv <- c('0','0.01','0.05','0.1') + for(i in 0:3){ + polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i), + c(.4 ,.7 , .4 , .4), + col=cols[i+1]) + text(2*i,0.5,niv[i+1],cex=1.5) + } + text(8,.5,1,cex=1.5) + text(4,0,'p-value',cex=2) + box() + residus <- arimaSelect.out[[3]][[choix]]$res + par(mar=c(1,2,4,1)) + acf(residus,main='') + title('acf',line=.5) + par(mar=c(1,2,4,1)) + pacf(residus,main='') + title('pacf',line=.5) + par(mar=c(2,2,4,1)) + qqnorm(residus,main='') + title('qq-norm',line=.5) + qqline(residus) + residus + } > if (par2 == 0) x <- log(x) > if (par2 != 0) x <- x^par2 > (selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5))) [[1]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] -0.5615829 -0.2867828 -0.09233356 -0.999766 0.08831262 0.1646984 [2,] -0.5631533 -0.2895171 -0.08984023 -1.000169 0.00000000 0.1210718 [3,] -0.5409526 -0.2417395 0.00000000 -1.000161 0.00000000 0.1234906 [4,] -0.5435366 -0.2246375 0.00000000 -1.000163 0.00000000 0.0000000 [5,] NA NA NA NA NA NA [6,] NA NA NA NA NA NA [7,] NA NA NA NA NA NA [8,] NA NA NA NA NA NA [9,] NA NA NA NA NA NA [10,] NA NA NA NA NA NA [11,] NA NA NA NA NA NA [12,] NA NA NA NA NA NA [13,] NA NA NA NA NA NA [14,] NA NA NA NA NA NA [,7] [1,] -0.6965699 [2,] -0.6230003 [3,] -0.6246897 [4,] -0.5814306 [5,] NA [6,] NA [7,] NA [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0 0.00123 0.23406 0 0.63655 0.20613 4e-05 [2,] 0 0.00109 0.24601 0 NA 0.17683 0e+00 [3,] 0 0.00201 NA 0 NA 0.16995 0e+00 [4,] 0 0.00396 NA 0 NA NA 0e+00 [5,] NA NA NA NA NA NA NA [6,] NA NA NA NA NA NA NA [7,] NA NA NA NA NA NA NA [8,] NA NA NA NA NA NA NA [9,] NA NA NA NA NA NA NA [10,] NA NA NA NA NA NA NA [11,] NA NA NA NA NA NA NA [12,] NA NA NA NA NA NA NA [13,] NA NA NA NA NA NA NA [14,] NA NA NA NA NA NA NA [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5616 -0.2868 -0.0923 -0.9998 0.0883 0.1647 -0.6966 s.e. 0.0764 0.0873 0.0773 0.0190 0.1866 0.1298 0.1646 sigma^2 estimated as 113.6: log likelihood = -660.6, aic = 1337.21 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5616 -0.2868 -0.0923 -0.9998 0.0883 0.1647 -0.6966 s.e. 0.0764 0.0873 0.0773 0.0190 0.1866 0.1298 0.1646 sigma^2 estimated as 113.6: log likelihood = -660.6, aic = 1337.21 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5632 -0.2895 -0.0898 -1.0002 0 0.1211 -0.6230 s.e. 0.0763 0.0872 0.0772 0.0186 0 0.0893 0.0781 sigma^2 estimated as 113.7: log likelihood = -660.71, aic = 1335.41 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5410 -0.2417 0 -1.0002 0 0.1235 -0.6247 s.e. 0.0742 0.0771 0 0.0182 0 0.0896 0.0774 sigma^2 estimated as 114.7: log likelihood = -661.38, aic = 1334.76 [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 1337.206 1335.414 1334.761 1334.710 Warning messages: 1: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 2: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 3: In log(s2) : NaNs produced 4: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE > postscript(file="/var/www/rcomp/tmp/1j5ed1323110902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > resid <- arimaSelectplot(selection) > dev.off() null device 1 > resid Time Series: Start = 1 End = 186 Frequency = 1 [1] 0.15868375 -0.13208073 -0.14524132 -0.16523305 -0.15145256 [6] -0.16918319 -0.13329986 -0.08610415 -0.08004102 -0.06094110 [11] -0.28090618 -0.27297086 -0.73782012 1.30449222 8.37733252 [16] -5.07717355 4.54703440 -3.36292258 10.79577826 1.52662986 [21] -0.50654386 5.21758748 -10.22035594 -0.93969979 -6.03918829 [26] -13.76711395 5.28048320 3.49388269 2.72191510 2.99262684 [31] 23.60642870 1.63303691 -2.47821626 7.29051911 4.04998070 [36] 2.90938435 -3.53818590 -12.08903261 -8.41456320 9.17321156 [41] 0.82740281 1.46203909 -15.16350970 6.10975152 -6.68669002 [46] -4.12471968 -1.76218252 -0.01533940 3.33600114 10.42494297 [51] 5.18299384 -6.30375582 2.15372486 -4.19397601 -15.69161622 [56] 0.48308234 1.65593114 -8.60512361 22.07508502 -5.25156298 [61] -9.99398650 1.05101572 22.30479565 -3.21892994 -3.78102147 [66] -9.41993882 -3.30647121 1.65695098 8.36925587 -0.83892972 [71] 7.65444015 -10.55369422 -10.79185039 -1.07044528 1.55575785 [76] 2.87783285 -9.63958189 -5.39835511 -13.37572161 -10.14231784 [81] -0.30677252 14.73163099 21.43083878 6.17145722 2.78725357 [86] -5.19393950 -4.48401459 -1.15633916 -19.18070500 9.56601937 [91] 31.80888767 -18.88525750 4.02153989 4.81844060 -2.97593769 [96] -5.85840715 -3.95152235 6.73968787 0.41983182 8.44007692 [101] -20.73762867 -1.48010572 3.67475963 -13.27451308 -1.93263111 [106] 59.44985573 14.54529935 9.53352476 -7.96307633 -7.00558286 [111] -10.82173388 10.93551622 -3.40974124 8.44262868 -2.71510089 [116] -19.57614879 2.48370747 -30.73761184 -0.24593005 -12.60361840 [121] 13.06554184 -0.39488240 7.96273472 -7.95866955 6.50834531 [126] -1.19934149 7.23643699 -8.41020702 -6.47052212 -19.36819737 [131] -5.99844681 -3.56808976 1.83708195 -6.49105412 4.27569929 [136] 0.17658172 0.93168388 -2.83678080 13.02808725 -1.97300291 [141] 13.54495510 -1.51427128 5.22460350 -10.49573744 -5.57196200 [146] 9.14272803 1.68296809 1.72107033 -5.25511411 4.19263405 [151] -7.38752212 -0.19634344 13.16942512 4.20812533 -17.99318220 [156] 5.77045893 4.21529374 -6.57236109 3.53853147 0.02097246 [161] 15.47966865 -9.43214900 -10.75576120 -12.66389393 4.92936824 [166] 0.17614733 11.43961621 10.09993867 -3.79506137 3.97344713 [171] 12.94193785 19.20110351 -27.57760555 3.18639779 5.15805711 [176] 5.03607890 -1.56800498 13.56315516 -5.30347979 13.32734401 [181] -12.73574540 15.13611859 7.60784052 -22.98677421 -7.53103532 [186] 0.01722723 > postscript(file="/var/www/rcomp/tmp/216dw1323110902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > acf(resid,length(resid)/2, main='Residual Autocorrelation Function') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/3mmvq1323110902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/4yojw1323110902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > cpgram(resid, main='Residual Cumulative Periodogram') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/5l2n21323110902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > hist(resid, main='Residual Histogram', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/6fzmm1323110902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals') > dev.off() null device 1 > postscript(file="/var/www/rcomp/tmp/72vf11323110902.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) > qqnorm(resid, main='Residual Normal Q-Q Plot') > qqline(resid) > dev.off() null device 1 > ncols <- length(selection[[1]][1,]) > nrows <- length(selection[[2]][,1])-1 > > #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,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Iteration', header=TRUE) > for (i in 1:ncols) { + a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE) + } > a<-table.row.end(a) > for (j in 1:nrows) { + a<-table.row.start(a) + mydum <- 'Estimates (' + mydum <- paste(mydum,j) + mydum <- paste(mydum,')') + a<-table.element(a,mydum, header=TRUE) + for (i in 1:ncols) { + a<-table.element(a,round(selection[[1]][j,i],4)) + } + a<-table.row.end(a) + a<-table.row.start(a) + a<-table.element(a,'(p-val)', header=TRUE) + for (i in 1:ncols) { + mydum <- '(' + mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='') + mydum <- paste(mydum,')') + a<-table.element(a,mydum) + } + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/8m9w61323110902.tab") > a<-table.start() > a<-table.row.start(a) > a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE) > a<-table.row.end(a) > a<-table.row.start(a) > a<-table.element(a,'Value', 1,TRUE) > a<-table.row.end(a) > for (i in (par4*par5+par3):length(resid)) { + a<-table.row.start(a) + a<-table.element(a,resid[i]) + a<-table.row.end(a) + } > a<-table.end(a) > table.save(a,file="/var/www/rcomp/tmp/9eajg1323110902.tab") > > try(system("convert tmp/1j5ed1323110902.ps tmp/1j5ed1323110902.png",intern=TRUE)) character(0) > try(system("convert tmp/216dw1323110902.ps tmp/216dw1323110902.png",intern=TRUE)) character(0) > try(system("convert tmp/3mmvq1323110902.ps tmp/3mmvq1323110902.png",intern=TRUE)) character(0) > try(system("convert tmp/4yojw1323110902.ps tmp/4yojw1323110902.png",intern=TRUE)) character(0) > try(system("convert tmp/5l2n21323110902.ps tmp/5l2n21323110902.png",intern=TRUE)) character(0) > try(system("convert tmp/6fzmm1323110902.ps tmp/6fzmm1323110902.png",intern=TRUE)) character(0) > try(system("convert tmp/72vf11323110902.ps tmp/72vf11323110902.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 8.680 0.360 9.049