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Type 'q()' to quit R. > x <- c(277 + ,260.6 + ,291.6 + ,275.4 + ,275.3 + ,231.7 + ,238.8 + ,274.2 + ,277.8 + ,299.1 + ,286.6 + ,232.3 + ,294.1 + ,267.5 + ,309.7 + ,280.7 + ,287.3 + ,235.7 + ,256.4 + ,289 + ,290.8 + ,321.9 + ,291.8 + ,241.4 + ,295.5 + ,258.2 + ,306.1 + ,281.5 + ,283.1 + ,237.4 + ,274.8 + ,299.3 + ,300.4 + ,340.9 + ,318.8 + ,265.7 + ,322.7 + ,281.6 + ,323.5 + ,312.6 + ,310.8 + ,262.8 + ,273.8 + ,320 + ,310.3 + ,342.2 + ,320.1 + ,265.6 + ,327 + ,300.7 + ,346.4 + ,317.3 + ,326.2 + ,270.7 + ,278.2 + ,324.6 + ,321.8 + ,343.5 + ,354 + ,278.2 + ,330.2 + ,307.3 + ,375.9 + ,335.3 + ,339.3 + ,280.3 + ,293.7 + ,341.2 + ,345.1 + ,368.7 + ,369.4 + ,288.4 + ,341 + ,319.1 + ,374.2 + ,344.5 + ,337.3 + ,281 + ,282.2 + ,321 + ,325.4 + ,366.3 + ,380.3 + ,300.7 + ,359.3 + ,327.6 + ,383.6 + ,352.4 + ,329.4 + ,294.5 + ,333.5 + ,334.3 + ,358 + ,396.1 + ,387 + ,307.2 + ,363.9 + ,344.7 + ,397.6 + ,376.8 + ,337.1 + ,299.3 + ,323.1 + ,329.1 + ,347 + ,462 + ,436.5 + ,360.4 + ,415.5 + ,382.1 + ,432.2 + ,424.3 + ,386.7 + ,354.5 + ,375.8 + ,368 + ,402.4 + ,426.5 + ,433.3 + ,338.5 + ,416.8 + ,381.1 + ,445.7 + ,412.4 + ,394 + ,348.2 + ,380.1 + ,373.7 + ,393.6 + ,434.2 + ,430.7 + ,344.5 + ,411.9 + ,370.5 + ,437.3 + ,411.3 + ,385.5 + ,341.3 + ,384.2 + ,373.2 + ,415.8 + ,448.6 + ,454.3 + ,350.3 + ,419.1 + ,398 + ,456.1 + ,430.1 + ,399.8 + ,362.7 + ,384.9 + ,385.3 + ,432.3 + ,468.9 + ,442.7 + ,370.2 + ,439.4 + ,393.9 + ,468.7 + ,438.8 + ,430.1 + ,366.3 + ,391 + ,380.9 + ,431.4 + ,465.4 + ,471.5 + ,387.5 + ,446.4 + ,421.5 + ,504.8 + ,492.1 + ,421.3 + ,396.7 + ,428 + ,421.9 + ,465.6 + ,525.8 + ,499.9 + ,435.3 + ,479.5 + ,473 + ,554.4 + ,489.6 + ,462.2 + ,420.3) > par9 = '1' > par8 = '2' > par7 = '0' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '0.0' > 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,] -0.5696910 -0.2768456 -0.07993690 0.1189565 0.1632306 -0.7218957 [2,] -0.5715587 -0.2814493 -0.07709626 0.0000000 0.1066140 -0.6248138 [3,] -0.5536327 -0.2404362 0.00000000 0.0000000 0.1083346 -0.6275730 [4,] -0.5519193 -0.2244336 0.00000000 0.0000000 0.0000000 -0.5877839 [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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0 0.00194 0.29767 0.50336 0.19073 1e-05 [2,] 0 0.00160 0.31458 NA 0.22876 0e+00 [3,] 0 0.00231 NA NA 0.22257 0e+00 [4,] 0 0.00424 NA NA NA 0e+00 [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 [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 sma1 -0.5697 -0.2768 -0.0799 0.1190 0.1632 -0.7219 s.e. 0.0766 0.0880 0.0765 0.1774 0.1243 0.1538 sigma^2 estimated as 0.001242: log likelihood = 330.52, aic = -647.05 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 sma1 -0.5697 -0.2768 -0.0799 0.1190 0.1632 -0.7219 s.e. 0.0766 0.0880 0.0765 0.1774 0.1243 0.1538 sigma^2 estimated as 0.001242: log likelihood = 330.52, aic = -647.05 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 sma1 -0.5716 -0.2814 -0.0771 0 0.1066 -0.6248 s.e. 0.0765 0.0878 0.0764 0 0.0883 0.0786 sigma^2 estimated as 0.001245: log likelihood = 330.32, aic = -648.63 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 sar1 sar2 sma1 -0.5536 -0.2404 0 0 0.1083 -0.6276 s.e. 0.0744 0.0778 0 0 0.0885 0.0779 sigma^2 estimated as 0.001252: log likelihood = 329.81, aic = -649.62 [[3]][[5]] NULL [[3]][[6]] NULL $aic [1] -647.0499 -648.6305 -649.6166 -650.0809 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 arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE > postscript(file="/var/www/html/rcomp/tmp/158qx1262970946.ps",horizontal=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] 0.0032470270 0.0014048405 0.0010146760 0.0007058853 0.0005627728 [6] 0.0003096536 0.0002932700 0.0003863026 0.0003555764 0.0003901949 [11] 0.0003138530 -0.0031044011 -0.0192508207 -0.0247432120 0.0155232046 [16] -0.0255996429 0.0075697864 -0.0188671818 0.0384027906 0.0042483430 [21] -0.0035330069 0.0170266562 -0.0364847821 -0.0028641841 -0.0258512256 [26] -0.0617953378 0.0052010435 0.0027566681 0.0006419113 0.0043695195 [31] 0.0901683785 0.0076643830 -0.0084575106 0.0246867697 0.0219729267 [36] 0.0280072637 -0.0103610923 -0.0380152753 -0.0328464039 0.0363491016 [41] 0.0077298697 0.0187465743 -0.0528156879 0.0181311835 -0.0251745594 [46] -0.0175597060 -0.0059078380 0.0061225335 0.0090977300 0.0409877838 [51] 0.0152585774 -0.0186495280 0.0096723069 -0.0031346670 -0.0561501947 [56] -0.0030597166 0.0029832930 -0.0331817277 0.0753779169 -0.0079803301 [61] -0.0381766466 0.0047321618 0.0709590345 -0.0055320691 -0.0109205559 [66] -0.0230803668 -0.0108013289 0.0018359772 0.0263518172 -0.0064686075 [71] 0.0267445295 -0.0256802964 -0.0393270757 -0.0016016751 0.0005724828 [76] 0.0103416136 -0.0329426248 -0.0152876695 -0.0507126885 -0.0398925639 [81] -0.0050537623 0.0465877636 0.0726735007 0.0297785708 0.0061446907 [86] -0.0158016697 -0.0192394287 -0.0037522744 -0.0668136110 0.0369692407 [91] 0.1148028341 -0.0685309175 0.0081761271 0.0107526695 -0.0090508712 [96] -0.0128031686 -0.0156934673 0.0241963585 -0.0037193489 0.0285606888 [101] -0.0693726069 -0.0002939061 0.0147482964 -0.0505365663 -0.0109179486 [106] 0.1818376312 0.0524784755 0.0523833873 -0.0260615010 -0.0181049401 [111] -0.0433556744 0.0362538365 -0.0049678009 0.0457418684 -0.0027806233 [116] -0.0687953513 0.0018918074 -0.1014079317 -0.0058045998 -0.0334384739 [121] 0.0385663362 0.0023102651 0.0183793878 -0.0228167440 0.0225542396 [126] 0.0065113465 0.0253600483 -0.0304437023 -0.0250676821 -0.0629628712 [131] -0.0214828292 -0.0080886589 0.0035669636 -0.0217281938 0.0091791602 [136] 0.0013030658 0.0026625444 -0.0062796433 0.0426169180 -0.0091988206 [141] 0.0400784725 -0.0099881200 0.0133044758 -0.0280218897 -0.0181393482 [146] 0.0302883434 0.0014664654 0.0046945171 -0.0146805862 0.0191791259 [151] -0.0229400169 -0.0037181321 0.0376903674 0.0079981998 -0.0559636635 [156] 0.0238318189 0.0111058795 -0.0206230393 0.0054475023 0.0007827395 [161] 0.0497093051 -0.0229923875 -0.0337158403 -0.0420787400 0.0132303333 [166] -0.0023732309 0.0322893930 0.0386502682 -0.0139601235 0.0133043501 [171] 0.0313825012 0.0570727560 -0.0786605068 0.0185672611 0.0177236993 [176] 0.0151088815 -0.0095529884 0.0309398896 -0.0170949009 0.0531860041 [181] -0.0389884209 0.0458134542 0.0111849501 -0.0643842610 -0.0178006168 [186] 0.0109961213 > postscript(file="/var/www/html/rcomp/tmp/2apvs1262970946.ps",horizontal=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/html/rcomp/tmp/3hw5x1262970946.ps",horizontal=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/html/rcomp/tmp/4aczl1262970946.ps",horizontal=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/html/rcomp/tmp/5bo9u1262970946.ps",horizontal=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/html/rcomp/tmp/6tdeq1262970946.ps",horizontal=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/html/rcomp/tmp/78u1e1262970946.ps",horizontal=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/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/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/html/rcomp/tmp/8v9mj1262970946.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/html/rcomp/tmp/9n7tz1262970946.tab") > > try(system("convert tmp/158qx1262970946.ps tmp/158qx1262970946.png",intern=TRUE)) character(0) > try(system("convert tmp/2apvs1262970946.ps tmp/2apvs1262970946.png",intern=TRUE)) character(0) > try(system("convert tmp/3hw5x1262970946.ps tmp/3hw5x1262970946.png",intern=TRUE)) character(0) > try(system("convert tmp/4aczl1262970946.ps tmp/4aczl1262970946.png",intern=TRUE)) character(0) > try(system("convert tmp/5bo9u1262970946.ps tmp/5bo9u1262970946.png",intern=TRUE)) character(0) > try(system("convert tmp/6tdeq1262970946.ps tmp/6tdeq1262970946.png",intern=TRUE)) character(0) > try(system("convert tmp/78u1e1262970946.ps tmp/78u1e1262970946.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.947 1.300 9.154