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Type 'q()' to quit R. > x <- c(18992,0,21552,1868501,7185612,10348382,6942386,4306121,2833176,1515513,1242981,699343,89497,128,10585,1070323,7167741,13193530,7885720,6785683,3106846,1706331,1286534,499079,24637,16,27309,873433,8435418,11290088,6840395,3803252,4388988,2680940,1174135,328388,22943,5657,28156,770831,8378147,13274946,7297840,2848227,2892179,1762224,1009375,188388,3393,0,13807,2619905,13297704,6240087,5108460,4553381,3148546,2433387,1748108,723454,58525,792,42585,1634386,10360570,6798599,4847748,4971202,343863,2200366,1549422,90144,63288,338,44863,1678135,9293357,9361258,6766402,4331272,3518962,2425786,1701795,552452,16104,0,90198,1731332,7954135,11561342,6834733,4255652,4243070,3415216,1841237,655456) > par9 = '1' > par8 = '1' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '0' > par2 = '1' > par1 = 'FALSE' > 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,] -0.86305539 -0.1841889 -0.09140536 0.8281014 0.3143953 -0.9999506 [2,] -0.39232145 -0.1602602 0.00000000 0.3577653 0.3075144 -0.9999126 [3,] -0.04378304 -0.1496473 0.00000000 0.0000000 0.3127206 -0.9998396 [4,] 0.00000000 -0.1479215 0.00000000 0.0000000 0.3147732 -1.0000231 [5,] 0.00000000 0.0000000 0.00000000 0.0000000 0.3190529 -0.9999247 [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,] 0.08910 0.20088 0.55512 0.09595 0.01242 0 [2,] 0.55075 0.13950 NA 0.59181 0.01147 0 [3,] 0.68466 0.16395 NA NA 0.00986 0 [4,] NA 0.16885 NA NA 0.00931 0 [5,] NA NA NA NA 0.00822 0 [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 sma1 -0.8631 -0.1842 -0.0914 0.8281 0.3144 -1.0000 s.e. 0.5021 0.1429 0.1543 0.4921 0.1232 0.1483 sigma^2 estimated as 1.276e+12: log likelihood = -1299.2, aic = 2612.41 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 -0.8631 -0.1842 -0.0914 0.8281 0.3144 -1.0000 s.e. 0.5021 0.1429 0.1543 0.4921 0.1232 0.1483 sigma^2 estimated as 1.276e+12: log likelihood = -1299.2, aic = 2612.41 [[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 sma1 -0.3923 -0.1603 0 0.3578 0.3075 -0.9999 s.e. 0.6551 0.1075 0 0.6648 0.1192 0.1500 sigma^2 estimated as 1.276e+12: log likelihood = -1299.24, aic = 2610.48 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 -0.0438 -0.1496 0 0 0.3127 -0.9998 s.e. 0.1075 0.1066 0 0 0.1186 0.1471 sigma^2 estimated as 1.281e+12: log likelihood = -1299.34, aic = 2608.68 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sma1 0 -0.1479 0 0 0.3148 -1.0000 s.e. 0 0.1067 0 0 0.1185 0.1463 sigma^2 estimated as 1.284e+12: log likelihood = -1299.42, aic = 2606.84 [[3]][[6]] NULL $aic [1] 2612.409 2610.476 2608.678 2606.844 2606.743 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 arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE > postscript(file="/var/wessaorg/rcomp/tmp/1wq351324552666.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 = 96 Frequency = 1 [1] 1.899199e+01 0.000000e+00 2.155199e+01 1.868500e+03 7.185606e+03 [6] 1.034837e+04 6.942382e+03 4.306120e+03 2.833175e+03 1.515512e+03 [11] 1.242980e+03 6.993427e+02 5.653595e+04 1.027111e+02 -4.377749e+02 [16] -6.471348e+05 -1.579303e+04 2.211079e+06 7.626385e+05 2.351619e+06 [21] 3.354470e+05 4.520896e+05 6.530026e+04 -1.397237e+05 -3.057247e+04 [26] -2.265294e+04 5.993135e+03 -4.059345e+05 1.090405e+06 -8.614884e+05 [31] -4.621908e+05 -1.959280e+06 1.095492e+06 6.252611e+05 8.980150e+04 [36] -7.399958e+04 -2.260717e+04 -2.454175e+04 3.145833e+03 -3.482115e+05 [41] 4.320965e+05 1.637420e+06 2.857135e+05 -1.182597e+06 -7.485459e+05 [46] -6.143420e+05 -2.970243e+05 -2.957420e+05 -5.203171e+04 -3.690384e+04 [51] -1.332823e+04 1.430731e+06 4.850754e+06 -5.396528e+06 -1.228817e+06 [56] -1.666655e+05 -2.605264e+05 6.460506e+05 5.774551e+05 4.169547e+05 [61] 1.185549e+05 4.908709e+04 2.794104e+04 -2.431423e+05 -5.426728e+04 [66] -2.199975e+06 -1.253529e+06 1.288559e+05 -2.829742e+06 1.175360e+05 [71] -3.068665e+05 -4.509184e+05 3.080743e+04 -6.766811e+04 1.607741e+04 [76] 1.205522e+05 -1.812210e+05 3.780029e+05 7.666494e+05 -2.796675e+05 [81] 1.623244e+06 2.742164e+05 4.965227e+05 2.700547e+05 1.057167e+04 [86] 3.192950e+04 4.941676e+04 1.433009e+05 -1.098758e+06 1.651401e+06 [91] 3.071809e+04 6.644308e+04 1.095607e+06 1.124501e+06 4.852191e+05 [96] 3.257831e+05 > postscript(file="/var/wessaorg/rcomp/tmp/2oy021324552666.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/wessaorg/rcomp/tmp/30epc1324552666.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/wessaorg/rcomp/tmp/4hruc1324552666.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/wessaorg/rcomp/tmp/5a8yq1324552666.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/wessaorg/rcomp/tmp/6ic161324552666.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/wessaorg/rcomp/tmp/7a1re1324552666.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/8kt4g1324552666.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/wessaorg/rcomp/tmp/9k8l81324552666.tab") > > try(system("convert tmp/1wq351324552666.ps tmp/1wq351324552666.png",intern=TRUE)) character(0) > try(system("convert tmp/2oy021324552666.ps tmp/2oy021324552666.png",intern=TRUE)) character(0) > try(system("convert tmp/30epc1324552666.ps tmp/30epc1324552666.png",intern=TRUE)) character(0) > try(system("convert tmp/4hruc1324552666.ps tmp/4hruc1324552666.png",intern=TRUE)) character(0) > try(system("convert tmp/5a8yq1324552666.ps tmp/5a8yq1324552666.png",intern=TRUE)) character(0) > try(system("convert tmp/6ic161324552666.ps tmp/6ic161324552666.png",intern=TRUE)) character(0) > try(system("convert tmp/7a1re1324552666.ps tmp/7a1re1324552666.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.822 0.531 5.868