R version 2.9.0 (2009-04-17) Copyright (C) 2009 The R Foundation for Statistical Computing ISBN 3-900051-07-0 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > x <- c(151.7,121.3,133.0,119.6,122.2,117.4,106.7,87.5,81.0,110.3,87.0,55.7,146.0,137.5,138.5,135.6,107.3,99.0,91.4,68.4,82.6,98.4,71.3,47.6,130.8,113.6,125.7,113.6,97.1,104.4,91.8,75.1,89.2,110.2,78.4,68.4,122.8,129.7,159.1,139.0,102.2,113.6,81.5,77.4,87.6,101.2,87.2,64.9,133.1,118.0,135.9,125.7,108.0,128.3,84.7,86.4,92.2,95.8,92.3,54.3) > par9 = '1' > par8 = '1' > par7 = '1' > par6 = '0' > par5 = '12' > par4 = '1' > par3 = '1' > 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 > par6 <- 11 > 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,] -1.0207263 -0.6866916 -0.3169133 -0.3976575 -0.08711121 0.2677672 [2,] -0.9904838 -0.6631792 -0.2902591 -0.3979846 -0.07803005 0.2648807 [3,] -0.8997597 -0.5699082 -0.2082054 -0.3374709 0.00000000 0.3067559 [4,] -0.8400509 -0.5150356 -0.1806486 -0.3394884 0.00000000 0.2860365 [5,] -0.8107468 -0.4738478 -0.1446408 -0.3168771 0.00000000 0.2519599 [6,] -0.7713154 -0.3799103 0.0000000 -0.2522564 0.00000000 0.2687056 [7,] -0.6926052 -0.3335172 0.0000000 -0.2761052 0.00000000 0.1610963 [8,] -0.6749666 -0.3509773 0.0000000 -0.2992939 0.00000000 0.2087206 [9,] -0.6755164 -0.2968599 0.0000000 -0.3170335 0.00000000 0.2056630 [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 [15,] NA NA NA NA NA NA [16,] NA NA NA NA NA NA [17,] NA NA NA NA NA NA [18,] NA NA NA NA NA NA [19,] NA NA NA NA NA NA [20,] NA NA NA NA NA NA [21,] NA NA NA NA NA NA [22,] NA NA NA NA NA NA [23,] NA NA NA NA NA NA [24,] NA NA NA NA NA NA [25,] NA NA NA NA NA NA [26,] NA NA NA NA NA NA [27,] NA NA NA NA NA NA [28,] NA NA NA NA NA NA [,7] [,8] [,9] [,10] [,11] [,12] [,13] [1,] 0.3397122 0.5014570 0.4493506 0.2027570 -0.2866730 0.18016166 -0.8025122 [2,] 0.3203160 0.4812226 0.4274475 0.2047799 -0.2756464 0.17035338 -0.8350859 [3,] 0.3191863 0.4683084 0.3905970 0.1546040 -0.3209045 0.07672896 -0.8398926 [4,] 0.2773301 0.4231469 0.3493229 0.1189601 -0.3434220 0.00000000 -0.8350755 [5,] 0.2252673 0.3503045 0.2435255 0.0000000 -0.4085449 0.00000000 -0.8394271 [6,] 0.2512949 0.3617437 0.2104401 0.0000000 -0.4111822 0.00000000 -0.8438687 [7,] 0.0000000 0.1789844 0.1344594 0.0000000 -0.4366983 0.00000000 -0.8051639 [8,] 0.0000000 0.1103430 0.0000000 0.0000000 -0.4218855 0.00000000 -0.8139782 [9,] 0.0000000 0.0000000 0.0000000 0.0000000 -0.3824042 0.00000000 -0.7811088 [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 [15,] NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA [17,] NA NA NA NA NA NA NA [18,] NA NA NA NA NA NA NA [19,] NA NA NA NA NA NA NA [20,] NA NA NA NA NA NA NA [21,] NA NA NA NA NA NA NA [22,] NA NA NA NA NA NA NA [23,] NA NA NA NA NA NA NA [24,] NA NA NA NA NA NA NA [25,] NA NA NA NA NA NA NA [26,] NA NA NA NA NA NA NA [27,] NA NA NA NA NA NA NA [28,] NA NA NA NA NA NA NA [,14] [1,] -0.1188166 [2,] 0.0000000 [3,] 0.0000000 [4,] 0.0000000 [5,] 0.0000000 [6,] 0.0000000 [7,] 0.0000000 [8,] 0.0000000 [9,] 0.0000000 [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [15,] NA [16,] NA [17,] NA [18,] NA [19,] NA [20,] NA [21,] NA [22,] NA [23,] NA [24,] NA [25,] NA [26,] NA [27,] NA [28,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 0.04729 0.19595 0.48159 0.20761 0.80233 0.35480 0.34140 0.20168 0.28002 [2,] 0.06114 0.21846 0.49774 0.19437 0.82214 0.34506 0.35179 0.20522 0.28783 [3,] 0.03525 0.16638 0.41615 0.01268 NA 0.12452 0.36333 0.22255 0.29585 [4,] 0.00000 0.00783 0.29644 0.01165 NA 0.04505 0.17564 0.06498 0.13966 [5,] 0.00000 0.00642 0.36741 0.01425 NA 0.04902 0.21538 0.05960 0.08548 [6,] 0.00000 0.00511 NA 0.01738 NA 0.03749 0.16760 0.05351 0.12502 [7,] 0.00000 0.01616 NA 0.01211 NA 0.13056 NA 0.21248 0.30010 [8,] 0.00000 0.01066 NA 0.00502 NA 0.02916 NA 0.39352 NA [9,] 0.00000 0.01824 NA 0.00457 NA 0.04622 NA NA NA [10,] NA NA NA NA NA NA NA NA NA [11,] NA NA NA NA NA NA NA NA NA [12,] NA NA NA NA NA NA NA NA NA [13,] NA NA NA NA NA NA NA NA NA [14,] NA NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA NA [16,] NA NA NA NA NA NA NA NA NA [17,] NA NA NA NA NA NA NA NA NA [18,] NA NA NA NA NA NA NA NA NA [19,] NA NA NA NA NA NA NA NA NA [20,] NA NA NA NA NA NA NA NA NA [21,] NA NA NA NA NA NA NA NA NA [22,] NA NA NA NA NA NA NA NA NA [23,] NA NA NA NA NA NA NA NA NA [24,] NA NA NA NA NA NA NA NA NA [25,] NA NA NA NA NA NA NA NA NA [26,] NA NA NA NA NA NA NA NA NA [27,] NA NA NA NA NA NA NA NA NA [28,] NA NA NA NA NA NA NA NA NA [,10] [,11] [,12] [,13] [,14] [1,] 0.59438 0.31376 0.72306 0.00037 0.85409 [2,] 0.59780 0.34173 0.75828 0.00000 NA [3,] 0.63686 0.14450 0.87794 0.00000 NA [4,] 0.57565 0.02495 NA 0.00000 NA [5,] NA 0.00007 NA 0.00000 NA [6,] NA 0.00005 NA 0.00000 NA [7,] NA 0.00004 NA 0.00000 NA [8,] NA 0.00003 NA 0.00000 NA [9,] NA 0.00004 NA 0.00000 NA [10,] NA NA NA NA NA [11,] NA NA NA NA NA [12,] NA NA NA NA NA [13,] NA NA NA NA NA [14,] NA NA NA NA NA [15,] NA NA NA NA NA [16,] NA NA NA NA NA [17,] NA NA NA NA NA [18,] NA NA NA NA NA [19,] NA NA NA NA NA [20,] NA NA NA NA NA [21,] NA NA NA NA NA [22,] NA NA NA NA NA [23,] NA NA NA NA NA [24,] NA NA NA NA NA [25,] NA NA NA NA NA [26,] NA NA NA NA NA [27,] NA NA NA NA NA [28,] 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 ar4 ar5 ar6 ar7 ar8 -1.0207 -0.6867 -0.3169 -0.3977 -0.0871 0.2678 0.3397 0.5015 s.e. 0.5005 0.5231 0.4466 0.3110 0.3459 0.2864 0.3533 0.3870 ar9 ar10 ar11 ma1 sar1 sma1 0.4494 0.2028 -0.2867 0.1802 -0.8025 -0.1188 s.e. 0.4110 0.3781 0.2814 0.5052 0.2084 0.6424 sigma^2 estimated as 59.6: log likelihood = -170.24, aic = 370.47 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 -1.0207 -0.6867 -0.3169 -0.3977 -0.0871 0.2678 0.3397 0.5015 s.e. 0.5005 0.5231 0.4466 0.3110 0.3459 0.2864 0.3533 0.3870 ar9 ar10 ar11 ma1 sar1 sma1 0.4494 0.2028 -0.2867 0.1802 -0.8025 -0.1188 s.e. 0.4110 0.3781 0.2814 0.5052 0.2084 0.6424 sigma^2 estimated as 59.6: log likelihood = -170.24, aic = 370.47 [[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 ar4 ar5 ar6 ar7 ar8 -0.9905 -0.6632 -0.2903 -0.3980 -0.0780 0.2649 0.3203 0.4812 s.e. 0.5160 0.5315 0.4247 0.3022 0.3451 0.2776 0.3405 0.3745 ar9 ar10 ar11 ma1 sar1 sma1 0.4274 0.2048 -0.2756 0.1704 -0.8351 0 s.e. 0.3975 0.3855 0.2869 0.5503 0.0884 0 sigma^2 estimated as 59.46: log likelihood = -170.25, aic = 368.51 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 -0.8998 -0.5699 -0.2082 -0.3375 0 0.3068 0.3192 0.4683 0.3906 s.e. 0.4150 0.4054 0.2538 0.1302 0 0.1961 0.3477 0.3788 0.3695 ar10 ar11 ma1 sar1 sma1 0.1546 -0.3209 0.0767 -0.8399 0 s.e. 0.3254 0.2163 0.4969 0.0850 0 sigma^2 estimated as 59.13: log likelihood = -170.28, aic = 366.56 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 -0.8401 -0.5150 -0.1806 -0.3395 0 0.286 0.2773 0.4231 0.3493 s.e. 0.1427 0.1855 0.1711 0.1294 0 0.139 0.2018 0.2240 0.2326 ar10 ar11 ma1 sar1 sma1 0.1190 -0.3434 0 -0.8351 0 s.e. 0.2111 0.1484 0 0.0823 0 sigma^2 estimated as 59.42: log likelihood = -170.29, aic = 364.58 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 -0.8107 -0.4738 -0.1446 -0.3169 0 0.2520 0.2253 0.3503 0.2435 s.e. 0.1305 0.1664 0.1590 0.1247 0 0.1248 0.1795 0.1816 0.1387 ar10 ar11 ma1 sar1 sma1 0 -0.4085 0 -0.8394 0 s.e. 0 0.0937 0 0.0795 0 sigma^2 estimated as 59.78: log likelihood = -170.45, aic = 362.9 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 -0.7713 -0.3799 0 -0.2523 0 0.2687 0.2513 0.3617 0.2104 0 s.e. 0.1222 0.1297 0 0.1025 0 0.1257 0.1795 0.1829 0.1349 0 ar11 ma1 sar1 sma1 -0.4112 0 -0.8439 0 s.e. 0.0929 0 0.0765 0 sigma^2 estimated as 59.87: log likelihood = -170.86, aic = 361.72 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 -0.6926 -0.3335 0 -0.2761 0 0.1611 0 0.1790 0.1345 0 s.e. 0.1133 0.1341 0 0.1061 0 0.1048 0 0.1418 0.1284 0 ar11 ma1 sar1 sma1 -0.4367 0 -0.8052 0 s.e. 0.0966 0 0.0903 0 sigma^2 estimated as 64.91: log likelihood = -171.75, aic = 361.49 [[3]][[9]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 -0.6750 -0.3510 0 -0.2993 0 0.2087 0 0.1103 0 0 s.e. 0.1117 0.1325 0 0.1021 0 0.0930 0 0.1282 0 0 ar11 ma1 sar1 sma1 -0.4219 0 -0.8140 0 s.e. 0.0928 0 0.0848 0 sigma^2 estimated as 65.56: log likelihood = -172.29, aic = 360.58 [[3]][[10]] NULL [[3]][[11]] NULL [[3]][[12]] NULL [[3]][[13]] NULL [[3]][[14]] NULL $aic [1] 370.4723 368.5065 366.5578 364.5809 362.8967 361.7190 361.4935 360.5754 [9] 359.2674 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 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 7: 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/1qadd1260464563.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 = 60 Frequency = 1 [1] 8.758397e-02 1.562105e-02 2.011467e-02 3.174826e-03 4.884528e-03 [6] -3.512630e-04 -1.026194e-02 -2.700896e-02 -3.014651e-02 7.460597e-04 [11] -2.158580e-02 -1.267762e-01 -3.739829e-01 1.001942e+01 4.119138e-01 [16] 6.240187e+00 -1.999899e+01 -5.269939e+00 -6.834816e+00 1.499434e+00 [21] 8.572097e+00 -3.787748e+00 -1.129815e+00 -3.258715e+00 4.373308e+00 [26] -3.509304e+00 6.452103e+00 -2.790859e-01 -1.207005e+01 4.009163e+00 [31] 4.044645e+00 9.018519e+00 7.843171e+00 1.013274e+01 -1.989959e+00 [36] 7.350654e+00 -1.303853e+01 -2.142202e+00 1.985480e+01 9.433031e+00 [41] -1.709776e+01 4.679815e+00 1.326723e+00 4.399179e+00 -7.612291e+00 [46] 3.462206e+00 1.110086e+01 -3.907588e+00 6.574740e+00 -4.685933e+00 [51] -1.331249e+00 -1.856516e+00 7.492797e+00 5.097517e+00 -9.486024e+00 [56] 1.834070e+00 -6.384396e+00 -6.386762e+00 2.015463e+00 -1.665978e+01 > postscript(file="/var/www/html/rcomp/tmp/2f2eq1260464563.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/3wz7d1260464563.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/4anzi1260464563.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/5njkx1260464563.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/60rs51260464563.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/75mx41260464563.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/8cxvh1260464563.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/9qykd1260464563.tab") > > system("convert tmp/1qadd1260464563.ps tmp/1qadd1260464563.png") > system("convert tmp/2f2eq1260464563.ps tmp/2f2eq1260464563.png") > system("convert tmp/3wz7d1260464563.ps tmp/3wz7d1260464563.png") > system("convert tmp/4anzi1260464563.ps tmp/4anzi1260464563.png") > system("convert tmp/5njkx1260464563.ps tmp/5njkx1260464563.png") > system("convert tmp/60rs51260464563.ps tmp/60rs51260464563.png") > system("convert tmp/75mx41260464563.ps tmp/75mx41260464563.png") > > > proc.time() user system elapsed 23.156 3.467 26.689