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Type 'q()' to quit R. > x <- c(25.22,27.63,27.47,22.54,27.4,29.68,28.51,29.89,32.62,30.93,32.52,25.28,25.64,27.41,24.4,25.55,28.45,27.72,24.54,25.67,25.54,20.48,18.94,18.6,19.49,20.29,23.69,25.65,25.43,24.13,25.77,26.63,28.34,27.55,24.5,28.52,31.29,32.65,30.34,25.02,25.81,27.55,28.4,29.83,27.1,29.59,28.77,29.88,31.18,30.87,33.8,33.36,37.92,35.19,38.37,43.03,43.38,49.77,43.05,39.65,44.28,45.56,53.08,51.86,48.67,54.31,57.58,64.09,62.98,58.52,55.54,56.75,63.57,59.92,62.25,70.44,70.19,68.86,73.9,73.61,62.77,58.38,58.48,62.31,54.3,57.76,62.14,67.4,67.48,71.32,77.2,70.8,77.13,83.04,92.53,91.45,91.92,94.82,103.28,110.44,123.94,133.05,133.9,113.85,99.06,72.84,53.24,41.58,44.86,43.24,46.84,50.85,57.94,68.59,64.92,72.5,67.69,73.19,77.04,74.67) > par9 = '0' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > 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,] -0.69599705 0.03508964 0.06414685 0.81938452 0.03278003 -0.08362357 [2,] -0.70057913 0.00000000 0.05164331 0.80742083 0.03079658 -0.07416091 [3,] -0.69739017 0.00000000 0.05623054 0.80279001 0.00000000 -0.07488893 [4,] 0.03307383 0.00000000 0.00000000 0.05841047 0.00000000 -0.09678305 [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,] 0.00141 0.7666 0.54396 0.00006 0.75448 0.49005 [2,] 0.00233 NA 0.59887 0.00013 0.76720 0.52711 [3,] 0.00178 NA 0.55731 0.00008 NA 0.52300 [4,] 0.00000 NA NA 0.00000 NA 0.00000 [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 ma1 sar1 sar2 -0.6960 0.0351 0.0641 0.8194 0.0328 -0.0836 s.e. 0.2126 0.1179 0.1054 0.1961 0.1046 0.1208 sigma^2 estimated as 5.98e-05: log likelihood = 409.6, aic = -805.19 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 -0.6960 0.0351 0.0641 0.8194 0.0328 -0.0836 s.e. 0.2126 0.1179 0.1054 0.1961 0.1046 0.1208 sigma^2 estimated as 5.98e-05: log likelihood = 409.6, aic = -805.19 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 -0.7006 0 0.0516 0.8074 0.0308 -0.0742 s.e. 0.2250 0 0.0979 0.2037 0.1038 0.1169 sigma^2 estimated as 5.987e-05: log likelihood = 409.55, aic = -807.1 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 -0.6974 0 0.0562 0.8028 0 -0.0749 s.e. 0.2180 0 0.0955 0.1968 0 0.1169 sigma^2 estimated as 5.991e-05: log likelihood = 409.51, aic = -809.02 [[3]][[5]] NULL [[3]][[6]] NULL $aic [1] -805.1933 -807.1040 -809.0160 -809.3480 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/1bnox1293301989.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 = 120 Frequency = 1 [1] 0.0001991257 -0.0087038176 0.0011990280 0.0190036885 -0.0201318180 [6] -0.0050642975 0.0014401956 -0.0018190452 -0.0089520649 0.0062294033 [11] -0.0059037359 0.0255361872 -0.0057884427 -0.0025433004 0.0076195382 [16] -0.0026783525 -0.0110151983 0.0034449078 0.0110992626 -0.0045188736 [21] 0.0008790858 0.0219770203 0.0075056332 0.0020427662 -0.0066926750 [26] -0.0038588555 -0.0171242445 -0.0039997544 -0.0016603068 0.0065430856 [31] -0.0079051647 -0.0015508907 -0.0080077340 0.0052615847 0.0092640540 [36] -0.0122917327 -0.0079656942 -0.0044694414 0.0087539413 0.0166402120 [41] -0.0044068970 -0.0057027801 -0.0026922248 -0.0038914560 0.0090976312 [46] -0.0074190899 0.0049377703 -0.0055371519 -0.0017717103 -0.0011695637 [51] -0.0077004561 0.0005242930 -0.0107615421 0.0082859680 -0.0097251559 [56] -0.0061451850 -0.0029357609 -0.0078351706 0.0114423950 0.0042041704 [61] -0.0082948765 -0.0027898193 -0.0101426470 0.0043821775 0.0029439191 [66] -0.0069358983 -0.0043868035 -0.0068067758 0.0026606063 0.0034254248 [71] 0.0041674587 -0.0025955020 -0.0069434363 0.0038912494 -0.0033949587 [76] -0.0064765875 -0.0008468607 0.0020475579 -0.0048148768 0.0001650359 [81] 0.0090844327 0.0035840552 0.0005573416 -0.0041127662 0.0089527666 [86] -0.0056657752 -0.0037762793 -0.0062323209 0.0020690420 -0.0050612750 [91] -0.0032676219 0.0037152656 -0.0045117325 -0.0032932385 -0.0057624364 [96] 0.0015559647 -0.0015012532 -0.0003790553 -0.0051271655 -0.0027734475 [101] -0.0056732814 -0.0019447174 -0.0009359330 0.0079579668 0.0063630688 [106] 0.0171843670 0.0175528353 0.0170716419 -0.0074040073 0.0037308012 [111] -0.0085891767 -0.0034792528 -0.0105748226 -0.0082163106 0.0023831832 [116] -0.0055976338 0.0044467920 -0.0061048986 -0.0015989271 0.0005489166 > postscript(file="/var/www/html/rcomp/tmp/2meo01293301989.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/html/rcomp/tmp/3meo01293301989.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/html/rcomp/tmp/4meo01293301989.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/html/rcomp/tmp/5meo01293301989.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/html/rcomp/tmp/6ennk1293301989.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/html/rcomp/tmp/7ennk1293301989.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/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/8sx2t1293301989.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/93oke1293301989.tab") > > try(system("convert tmp/1bnox1293301989.ps tmp/1bnox1293301989.png",intern=TRUE)) character(0) > try(system("convert tmp/2meo01293301989.ps tmp/2meo01293301989.png",intern=TRUE)) character(0) > try(system("convert tmp/3meo01293301989.ps tmp/3meo01293301989.png",intern=TRUE)) character(0) > try(system("convert tmp/4meo01293301989.ps tmp/4meo01293301989.png",intern=TRUE)) character(0) > try(system("convert tmp/5meo01293301989.ps tmp/5meo01293301989.png",intern=TRUE)) character(0) > try(system("convert tmp/6ennk1293301989.ps tmp/6ennk1293301989.png",intern=TRUE)) character(0) > try(system("convert tmp/7ennk1293301989.ps tmp/7ennk1293301989.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.689 1.259 10.472