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Type 'q()' to quit R. > x <- c(6.9,6.8,6.7,6.6,6.5,6.5,7.0,7.5,7.6,7.6,7.6,7.8,8.0,8.0,8.0,7.9,7.9,8.0,8.5,9.2,9.4,9.5,9.5,9.6,9.7,9.7,9.6,9.5,9.4,9.3,9.6,10.2,10.2,10.1,9.9,9.8,9.8,9.7,9.5,9.3,9.1,9.0,9.5,10.0,10.2,10.1,10.0,9.9,10.0,9.9,9.7,9.5,9.2,9.0,9.3,9.8,9.8,9.6,9.4,9.3,9.2,9.2,9.0,8.8,8.7,8.7,9.1,9.7,9.8,9.6,9.4,9.4,9.5,9.4,9.3,9.2,9.0,8.9,9.2,9.8,9.9,9.6,9.2,9.1,9.1,9.0,8.9,8.7,8.5,8.3,8.5,8.7,8.4,8.1,7.8,7.7,7.5,7.2,6.8,6.7,6.4,6.3,6.8,7.3,7.1,7.0,6.8,6.6,6.3,6.1,6.1,6.3,6.3,6.0,6.2,6.4,6.8,7.5,7.5,7.6,7.6,7.4,7.3,7.1,6.9,6.8,7.5,7.6,7.8,8.0,8.1,8.2,8.3,8.2,8.0,7.9,7.6,7.6,8.3,8.4,8.4,8.4,8.4,8.6,8.9,8.8,8.3,7.5,7.2,7.4,8.8,9.3,9.3,8.7,8.2,8.3,8.5,8.6,8.5,8.2,8.1,7.9,8.6,8.7,8.7,8.5,8.4,8.5,8.7,8.7,8.6,8.5,8.3,8.0,8.2,8.1,8.1,8.0,7.9,7.9) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '2.0' > 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] [,7] [1,] 0.3423482 0.02796488 -0.3571446 0.2092410 0.4222265 0.00959025 -0.8457206 [2,] 0.3425862 0.02821960 -0.3570957 0.2083888 0.4156042 0.00000000 -0.8364506 [3,] 0.3770280 0.00000000 -0.3463135 0.1734955 0.4164874 0.00000000 -0.8371441 [4,] 0.4875182 0.00000000 -0.3424814 0.0000000 0.4458995 0.00000000 -1.1468477 [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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.07203 0.82733 2e-05 0.29614 0.00995 0.93117 0 [2,] 0.07220 0.82575 2e-05 0.29807 0.00437 NA 0 [3,] 0.00049 NA 0e+00 0.14661 0.00428 NA 0 [4,] 0.00000 NA 0e+00 NA 0.00123 NA 0 [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 0.3423 0.028 -0.3571 0.2092 0.4222 0.0096 -0.8457 s.e. 0.1891 0.128 0.0813 0.1997 0.1620 0.1109 0.1607 sigma^2 estimated as 6.509: log likelihood = -397.04, aic = 810.07 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.3423 0.028 -0.3571 0.2092 0.4222 0.0096 -0.8457 s.e. 0.1891 0.128 0.0813 0.1997 0.1620 0.1109 0.1607 sigma^2 estimated as 6.509: log likelihood = -397.04, aic = 810.07 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.3426 0.0282 -0.3571 0.2084 0.4156 0 -0.8365 s.e. 0.1894 0.1280 0.0812 0.1997 0.1439 0 0.1190 sigma^2 estimated as 6.515: log likelihood = -397.04, aic = 808.08 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.377 0 -0.3463 0.1735 0.4165 0 -0.8371 s.e. 0.106 0 0.0661 0.1190 0.1439 0 0.1192 sigma^2 estimated as 6.516: log likelihood = -397.06, aic = 806.13 [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 810.0740 808.0817 806.1289 806.2275 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 4: In log(s2) : NaNs produced > postscript(file="/var/www/html/rcomp/tmp/1dsm21259932430.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 = 180 Frequency = 1 [1] 0.027487638 0.011231623 0.006211842 0.003457077 0.001572861 [6] 0.001308709 0.007404603 0.013285262 0.013232495 0.011905664 [11] 0.010820911 -0.012337334 -0.116394566 0.988330651 0.562657805 [16] -0.617754252 1.812309269 1.129755617 0.575453282 4.478783020 [21] -0.062244086 1.352999593 0.656700502 -0.493015799 0.116433969 [26] 0.620018513 -2.083259645 0.244155983 -1.220266042 -2.588343765 [31] -0.628039033 1.227262106 -4.493489164 -2.100237081 -1.963863722 [36] -3.190620803 -1.072107084 -1.997870215 -2.582822492 -1.341206786 [41] -1.656265454 -0.403847248 2.612262773 -3.481716514 3.931010703 [46] -1.761888165 0.848861665 -0.750751788 1.537941437 -1.013129626 [51] -1.152566845 0.140420318 -2.659388773 -1.215831480 -1.998364525 [56] 0.163228665 -3.874577259 -1.651945993 -0.926044410 -1.151116931 [61] -3.926938760 2.881001786 -1.951401452 -1.084347757 3.226503417 [66] 0.801790183 -0.295312492 2.089960204 0.943180293 -1.534869172 [71] 0.356198905 1.642862828 1.255248226 -2.841061526 2.579505006 [76] 1.171140184 -2.713422896 0.179112569 -0.722987700 0.878471974 [81] -0.777224420 -3.443410272 -2.480277392 0.346644773 -1.926012132 [86] -0.938027702 0.005652658 -1.972147360 0.448779715 -1.730732296 [91] -2.133910374 -6.109651247 -3.793962131 1.616448685 -1.935771441 [96] -2.749995711 -3.092723416 -0.811764520 -2.463266692 2.318537639 [101] -2.866055426 0.751038098 1.845807846 -0.503491419 -0.394722311 [106] 3.454927960 0.093716215 -2.337259130 -0.559311472 2.112388801 [111] 3.072594140 1.402873936 1.771213454 -2.448474331 -1.108210839 [116] -2.500613383 8.362724334 6.678628456 -4.486864250 5.262470018 [121] 4.167324311 -1.287720175 1.396365253 -2.684615907 -0.125387398 [126] 1.914966741 3.996424521 -7.082337145 3.060002455 -0.034058824 [131] 2.363791775 -0.406922750 0.938348915 1.073651807 -1.515896307 [136] 2.191960080 -2.339577595 2.244091126 2.255027586 -4.608150073 [141] 0.086782882 0.204076493 -0.030512386 1.836062076 2.559535819 [146] -1.977705614 -4.482200554 -6.456494237 4.621714535 1.116855233 [151] 8.190188437 -1.460910273 -1.002393177 -5.765298305 -0.663933457 [156] 2.534433494 -3.764072406 1.484105959 2.565362165 1.151384656 [161] 2.016569397 -3.954328879 -1.240665008 -3.270476247 0.895286919 [166] 1.215416147 0.605925009 -1.188824631 2.108601248 0.402147515 [171] 0.879070274 2.684786349 -2.638773465 -1.601540917 -5.286236143 [176] -1.922546303 1.022288793 -1.537857443 -1.406691672 -1.344445239 > postscript(file="/var/www/html/rcomp/tmp/2yncr1259932430.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/3xb4p1259932430.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/4huvr1259932430.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/59bow1259932430.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/6nx041259932430.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/7n6r91259932430.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/88pns1259932430.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/9atot1259932430.tab") > system("convert tmp/1dsm21259932430.ps tmp/1dsm21259932430.png") > system("convert tmp/2yncr1259932430.ps tmp/2yncr1259932430.png") > system("convert tmp/3xb4p1259932430.ps tmp/3xb4p1259932430.png") > system("convert tmp/4huvr1259932430.ps tmp/4huvr1259932430.png") > system("convert tmp/59bow1259932430.ps tmp/59bow1259932430.png") > system("convert tmp/6nx041259932430.ps tmp/6nx041259932430.png") > system("convert tmp/7n6r91259932430.ps tmp/7n6r91259932430.png") > > > proc.time() user system elapsed 10.496 1.739 32.558