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Type 'q()' to quit R. > x <- c(32.68,31.54,32.43,26.54,25.85,27.6,25.71,25.38,28.57,27.64,25.36,25.9,26.29,21.74,19.2,19.32,19.82,20.36,24.31,25.97,25.61,24.67,25.59,26.09,28.37,27.34,24.46,27.46,30.23,32.33,29.87,24.87,25.48,27.28,28.24,29.58,26.95,29.08,28.76,29.59,30.7,30.52,32.67,33.19,37.13,35.54,37.75,41.84,42.94,49.14,44.61,40.22,44.23,45.85,53.38,53.26,51.8,55.3,57.81,63.96,63.77,59.15,56.12,57.42,63.52,61.71,63.01,68.18,72.03,69.75,74.41,74.33,64.24,60.03,59.44,62.5,55.04,58.34,61.92,67.65,67.68,70.3,75.26,71.44,76.36,81.71,92.6,90.6,92.23,94.09,102.79,109.65,124.05,132.69,135.81,116.07,101.42,75.73,55.48) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '2' > par2 = '1' > 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,] -0.6134972 -0.1552622 -0.1904312 0.3010739 0.8760690 -0.10600957 [2,] -0.3250031 -0.0668600 -0.1845411 0.0000000 0.8569360 -0.08951929 [3,] -0.3030223 0.0000000 -0.1673637 0.0000000 0.8700263 -0.09970287 [4,] -0.3208736 0.0000000 -0.1599676 0.0000000 -1.2535481 0.00000000 [5,] -0.3199051 0.0000000 -0.1609160 0.0000000 0.0000000 0.00000000 [6,] -0.3203541 0.0000000 -0.1606462 0.0000000 0.0000000 0.00000000 [7,] -0.3291021 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 [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,] -0.88406843 [2,] -0.87501899 [3,] -0.90683497 [4,] 1.27649672 [5,] 0.01662117 [6,] 0.00000000 [7,] 0.00000000 [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.12664 0.35385 0.07147 0.45643 0.02163 0.44634 0.04536 [2,] 0.00214 0.55495 0.07296 NA 0.03518 0.52213 0.05464 [3,] 0.00214 NA 0.09013 NA 0.01658 0.47382 0.05320 [4,] 0.00097 NA 0.10195 NA 0.13220 NA 0.10365 [5,] 0.00102 NA 0.10182 NA NA NA 0.90253 [6,] 0.00100 NA 0.10242 NA NA NA NA [7,] 0.00087 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 -0.6135 -0.1553 -0.1904 0.3011 0.8761 -0.1060 -0.8841 s.e. 0.3980 0.1666 0.1044 0.4025 0.3749 0.1386 0.4357 sigma^2 estimated as 23.59: log likelihood = -291.99, aic = 599.98 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.6135 -0.1553 -0.1904 0.3011 0.8761 -0.1060 -0.8841 s.e. 0.3980 0.1666 0.1044 0.4025 0.3749 0.1386 0.4357 sigma^2 estimated as 23.59: log likelihood = -291.99, aic = 599.98 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.3250 -0.0669 -0.1845 0 0.8569 -0.0895 -0.8750 s.e. 0.1029 0.1128 0.1017 0 0.4008 0.1393 0.4495 sigma^2 estimated as 23.77: log likelihood = -292.18, aic = 598.36 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.3030 0 -0.1674 0 0.8700 -0.0997 -0.9068 s.e. 0.0959 0 0.0977 0 0.3565 0.1386 0.4631 sigma^2 estimated as 23.58: log likelihood = -292.36, aic = 596.71 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.3209 0 -0.1600 0 -1.2535 0 1.2765 s.e. 0.0942 0 0.0969 0 0.8254 0 0.7768 sigma^2 estimated as 24.64: log likelihood = -292.68, aic = 595.37 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.3199 0 -0.1609 0 0 0 0.0166 s.e. 0.0944 0 0.0974 0 0 0 0.1354 sigma^2 estimated as 24.64: log likelihood = -293.15, aic = 594.3 [[3]][[7]] 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 -0.3204 0 -0.1606 0 0 0 0 s.e. 0.0943 0 0.0974 0 0 0 0 sigma^2 estimated as 24.64: log likelihood = -293.16, aic = 592.32 $aic [1] 599.9833 598.3627 596.7121 595.3696 594.3030 592.3180 592.9915 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 5: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 6: 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/12uxm1229285834.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 = 99 Frequency = 1 [1] 0.01461494 -0.04639382 1.87634877 -5.98794585 2.82602066 [6] 4.43195283 -3.94751704 1.22927123 4.41172896 -3.57710572 [11] -2.41925073 2.95299649 0.09153628 -5.20492542 0.88047310 [16] 3.27981474 0.43854979 0.48463332 3.85013294 -1.13654711 [21] -2.74718496 -0.67931181 1.30631494 -0.14864668 1.55227652 [26] -2.44096791 -2.97784333 5.57329515 1.12194310 -1.04087683 [31] -3.83003781 -4.03776314 4.68866776 2.25463981 -0.86681991 [36] 1.01212753 -3.65709653 3.35325160 -0.86406912 -0.27263270 [41] 1.41308288 -1.59388395 2.10148634 -0.83859411 2.69058933 [46] -4.06008356 1.76658880 3.64675530 -3.27610762 4.75259675 [51] -8.79417950 -3.77773110 9.26414497 -1.42275915 5.16684425 [56] -4.40727976 -4.17465290 5.48014435 -0.62998696 3.10758363 [61] -4.37710627 -6.62008445 0.75558352 3.82086632 5.47547061 [66] -6.11687311 1.27159723 5.63740269 -1.35094089 -6.05325781 [71] 5.59793023 -2.72879574 -12.51323920 3.78814017 4.74221910 [76] 3.20161366 -8.40610826 7.97141436 4.31336819 0.54970155 [81] -3.28268610 0.80896277 3.51510626 -8.94605460 6.34336488 [86] 3.60580652 4.26727898 -9.71119107 -0.43028602 2.28286496 [91] 4.84295246 0.93436734 6.98749714 -2.24571065 -7.66082833 [96] -23.41708239 -3.15861574 -10.29616462 -1.76908002 > postscript(file="/var/www/html/rcomp/tmp/2v5co1229285834.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/3w03q1229285834.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/4y4pb1229285834.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/5umwe1229285834.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/6remv1229285834.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/7w7fn1229285834.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/8ta7f1229285834.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/99xb61229285834.tab") > > system("convert tmp/12uxm1229285834.ps tmp/12uxm1229285834.png") > system("convert tmp/2v5co1229285834.ps tmp/2v5co1229285834.png") > system("convert tmp/3w03q1229285834.ps tmp/3w03q1229285834.png") > system("convert tmp/4y4pb1229285834.ps tmp/4y4pb1229285834.png") > system("convert tmp/5umwe1229285834.ps tmp/5umwe1229285834.png") > system("convert tmp/6remv1229285834.ps tmp/6remv1229285834.png") > system("convert tmp/7w7fn1229285834.ps tmp/7w7fn1229285834.png") > > > proc.time() user system elapsed 7.866 1.982 9.161