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Type 'q()' to quit R. > x <- c(8.9,8.8,8.3,7.5,7.2,7.4,8.8,9.3,9.3,8.7,8.2,8.3,8.5,8.6,8.5,8.2,8.1,7.9,8.6,8.7,8.7,8.5,8.4,8.5,8.7,8.7,8.6,8.5,8.3,8,8.2,8.1,8.1,8,7.9,7.9,8,8,7.9,8,7.7,7.2,7.5,7.3,7,7,7,7.2,7.3,7.1,6.8,6.4,6.1,6.5,7.7,7.9,7.5,6.9,6.6,6.9) > par9 = '0' > par8 = '0' > par7 = '0' > par6 = '3' > par5 = '12' > par4 = '0' > 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,] 0.2087858 -0.2216316 -0.3608252 -0.2603865 -0.06626988 0.1008956 [2,] 0.2086702 -0.2222267 -0.3620197 -0.2606042 -0.06512134 0.1014910 [3,] 0.2100019 -0.2205604 -0.3621287 -0.2624321 -0.06491758 0.1086764 [4,] 0.2072969 -0.2204114 -0.3563393 -0.2563464 -0.05942721 0.1042362 [5,] 0.2132942 -0.2217872 -0.3641533 -0.2585530 -0.04858324 0.1197602 [6,] 0.2249885 -0.2098444 -0.3554677 -0.2636378 0.00000000 0.1108730 [7,] 0.2187362 -0.2244360 -0.3585617 -0.2405616 0.00000000 0.1293211 [8,] 0.2204616 -0.2628414 -0.3990366 -0.2636295 0.00000000 0.0000000 [9,] 0.0000000 -0.1913583 -0.4548131 -0.3729650 0.00000000 0.0000000 [10,] 0.0000000 0.0000000 -0.5510974 -0.2878823 0.00000000 0.0000000 [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 [,7] [,8] [,9] [,10] [,11] [1,] -0.03337878 -0.08599018 -0.05243846 -0.02757982 0.01735496 [2,] -0.03794261 -0.09033210 -0.05551615 -0.02481860 0.00000000 [3,] -0.03214975 -0.08596918 -0.05958733 0.00000000 0.00000000 [4,] 0.00000000 -0.09069124 -0.05338589 0.00000000 0.00000000 [5,] 0.00000000 -0.09767543 0.00000000 0.00000000 0.00000000 [6,] 0.00000000 -0.08252221 0.00000000 0.00000000 0.00000000 [7,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [8,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [9,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [10,] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 [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 [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [1,] 0.12055 0.10661 0.01074 0.07627 0.65608 0.57450 0.85245 0.62466 0.75322 [2,] 0.12073 0.10507 0.01017 0.07592 0.66070 0.57202 0.82761 0.59707 0.73565 [3,] 0.11750 0.10657 0.01006 0.07286 0.66120 0.53110 0.85035 0.61000 0.71389 [4,] 0.12037 0.10667 0.00935 0.07231 0.68224 0.54354 NA 0.58604 0.73678 [5,] 0.10744 0.10489 0.00706 0.07027 0.73215 0.47314 NA 0.55682 NA [6,] 0.07949 0.11259 0.00745 0.06373 NA 0.50212 NA 0.60702 NA [7,] 0.08720 0.08262 0.00703 0.07289 NA 0.42469 NA NA NA [8,] 0.08614 0.03085 0.00152 0.04726 NA NA NA NA NA [9,] NA 0.09879 0.00029 0.00216 NA NA NA NA NA [10,] NA NA 0.00000 0.00927 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 [,10] [,11] [1,] 0.86650 0.9153 [2,] 0.87816 NA [3,] NA NA [4,] NA NA [5,] NA NA [6,] NA NA [7,] NA NA [8,] NA NA [9,] NA NA [10,] NA NA [11,] NA NA [12,] NA NA [13,] NA NA [14,] NA NA [15,] NA NA [16,] NA NA [17,] NA NA [18,] NA NA [19,] NA NA [20,] NA NA [21,] NA NA [22,] 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 0.2088 -0.2216 -0.3608 -0.2604 -0.0663 0.1009 -0.0334 -0.0860 s.e. 0.1321 0.1348 0.1359 0.1437 0.1479 0.1785 0.1785 0.1746 ar9 ar10 ar11 -0.0524 -0.0276 0.0174 s.e. 0.1658 0.1632 0.1623 sigma^2 estimated as 0.06794: log likelihood = -5.58, aic = 35.16 [[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 0.2088 -0.2216 -0.3608 -0.2604 -0.0663 0.1009 -0.0334 -0.0860 s.e. 0.1321 0.1348 0.1359 0.1437 0.1479 0.1785 0.1785 0.1746 ar9 ar10 ar11 -0.0524 -0.0276 0.0174 s.e. 0.1658 0.1632 0.1623 sigma^2 estimated as 0.06794: log likelihood = -5.58, aic = 35.16 [[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 0.2087 -0.2222 -0.3620 -0.2606 -0.0651 0.1015 -0.0379 -0.0903 s.e. 0.1321 0.1346 0.1354 0.1437 0.1475 0.1784 0.1733 0.1698 ar9 ar10 ar11 -0.0555 -0.0248 0 s.e. 0.1635 0.1611 0 sigma^2 estimated as 0.06796: log likelihood = -5.58, aic = 33.17 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 0.2100 -0.2206 -0.3621 -0.2624 -0.0649 0.1087 -0.0321 -0.0860 s.e. 0.1318 0.1342 0.1354 0.1432 0.1472 0.1723 0.1695 0.1675 ar9 ar10 ar11 -0.0596 0 0 s.e. 0.1616 0 0 sigma^2 estimated as 0.06802: log likelihood = -5.6, aic = 31.19 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.2073 -0.2204 -0.3563 -0.2563 -0.0594 0.1042 0 -0.0907 -0.0534 s.e. 0.1312 0.1342 0.1319 0.1397 0.1443 0.1704 0 0.1655 0.1580 ar10 ar11 0 0 s.e. 0 0 sigma^2 estimated as 0.06803: log likelihood = -5.61, aic = 29.23 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.2133 -0.2218 -0.3642 -0.2586 -0.0486 0.1198 0 -0.0977 0 s.e. 0.1302 0.1344 0.1298 0.1399 0.1412 0.1657 0 0.1652 0 ar10 ar11 0 0 s.e. 0 0 sigma^2 estimated as 0.0683: log likelihood = -5.67, aic = 27.34 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.2250 -0.2098 -0.3555 -0.2636 0 0.1109 0 -0.0825 0 0 s.e. 0.1258 0.1301 0.1277 0.1392 0 0.1641 0 0.1595 0 0 ar11 0 s.e. 0 sigma^2 estimated as 0.06848: log likelihood = -5.73, aic = 25.46 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.2187 -0.2244 -0.3586 -0.2406 0 0.1293 0 0 0 0 0 s.e. 0.1256 0.1269 0.1280 0.1315 0 0.1608 0 0 0 0 0 sigma^2 estimated as 0.06882: log likelihood = -5.86, aic = 23.73 [[3]][[9]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.2205 -0.2628 -0.3990 -0.2636 0 0 0 0 0 0 0 s.e. 0.1262 0.1186 0.1196 0.1299 0 0 0 0 0 0 0 sigma^2 estimated as 0.06994: log likelihood = -6.18, aic = 22.35 [[3]][[10]] 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 ar11 0 -0.1914 -0.4548 -0.373 0 0 0 0 0 0 0 s.e. 0 0.1140 0.1175 0.116 0 0 0 0 0 0 0 sigma^2 estimated as 0.07353: log likelihood = -7.67, aic = 23.35 [[3]][[11]] NULL $aic [1] 35.15776 33.16920 31.19290 29.22894 27.34202 25.46024 23.72702 22.35192 [9] 23.34539 24.11032 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 8: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 9: 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/1w72k1260283453.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] 0.0088999912 -0.0711733959 -0.3499362350 -0.5089419163 -0.2457715735 [6] -0.2177896797 0.7922595673 0.1034557660 0.2469747577 0.2070104450 [11] 0.2495574915 0.1716674984 -0.1685669964 -0.3320496848 -0.2027295158 [16] -0.1526050583 0.0009384697 -0.2655923031 0.5071237515 -0.0956424598 [21] 0.0056917068 0.0629119850 0.2065567855 0.0990248348 0.0899015553 [26] -0.1009384697 -0.0535435280 0.0282591106 -0.1445428372 -0.3646171379 [31] 0.0789505341 -0.2856666038 -0.1727652523 -0.1400627082 -0.0708883129 [36] -0.0564323280 0.0353828621 -0.0827778038 -0.1181606659 0.1454813068 [41] -0.2818393341 -0.5263454758 0.2507773167 -0.3948265789 -0.5818885319 [46] -0.0883102264 -0.0364806160 -0.0110369144 -0.0118894909 -0.1617283379 [51] -0.1899015553 -0.3181973614 -0.4110736099 0.1124197613 0.8487777886 [56] -0.0090865841 -0.1003342907 0.1332333320 0.1619772529 0.0778527802 > postscript(file="/var/www/html/rcomp/tmp/2d5jk1260283453.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/3kg851260283453.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/4ep2v1260283453.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/5hpa31260283453.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/6pwpb1260283453.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/7k6c71260283453.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/8umus1260283453.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/998gd1260283453.tab") > > system("convert tmp/1w72k1260283453.ps tmp/1w72k1260283453.png") > system("convert tmp/2d5jk1260283453.ps tmp/2d5jk1260283453.png") > system("convert tmp/3kg851260283453.ps tmp/3kg851260283453.png") > system("convert tmp/4ep2v1260283453.ps tmp/4ep2v1260283453.png") > system("convert tmp/5hpa31260283453.ps tmp/5hpa31260283453.png") > system("convert tmp/6pwpb1260283453.ps tmp/6pwpb1260283453.png") > system("convert tmp/7k6c71260283453.ps tmp/7k6c71260283453.png") > > > proc.time() user system elapsed 3.657 1.055 4.448