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Type 'q()' to quit R. > x <- c(267413,267366,264777,258863,254844,254868,277267,285351,286602,283042,276687,277915,277128,277103,275037,270150,267140,264993,287259,291186,292300,288186,281477,282656,280190,280408,276836,275216,274352,271311,289802,290726,292300,278506,269826,265861,269034,264176,255198,253353,246057,235372,258556,260993,254663,250643,243422,247105,248541,245039,237080,237085,225554,226839,247934,248333,246969,245098,246263,255765,264319,268347,273046,273963,267430,271993,292710) > 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,] 0.1265969 -0.1532426 0.00824827 -0.1695694 0.08877702 0.04587977 [2,] 0.1251986 -0.1523621 0.00000000 -0.1684257 0.08763303 0.04552258 [3,] 0.1302793 -0.1641791 0.00000000 -0.1755989 0.09433162 0.00000000 [4,] 0.1524495 -0.1703230 0.00000000 -0.1829998 0.11147162 0.00000000 [5,] 0.1645336 -0.1708558 0.00000000 -0.1940793 0.10741206 0.00000000 [6,] 0.1504191 -0.1872645 0.00000000 -0.1898292 0.00000000 0.00000000 [7,] 0.0000000 -0.1831540 0.00000000 -0.2133539 0.00000000 0.00000000 [8,] 0.0000000 -0.2070243 0.00000000 -0.2114709 0.00000000 0.00000000 [9,] 0.0000000 0.0000000 0.00000000 -0.2019031 0.00000000 0.00000000 [10,] 0.0000000 0.0000000 0.00000000 0.0000000 0.00000000 0.00000000 [11,] 0.0000000 0.0000000 0.00000000 0.0000000 0.00000000 0.00000000 [12,] 0.0000000 0.0000000 0.00000000 0.0000000 0.00000000 0.00000000 [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,] 0.09593993 -0.2167061 -0.06653725 -0.2356973 0.1504453 [2,] 0.09369954 -0.2155563 -0.06615795 -0.2352845 0.1481671 [3,] 0.09772238 -0.2229249 -0.06881113 -0.2457082 0.1523931 [4,] 0.10911278 -0.2262861 0.00000000 -0.2482216 0.1661224 [5,] 0.00000000 -0.2278451 0.00000000 -0.2611720 0.1527825 [6,] 0.00000000 -0.2433753 0.00000000 -0.2584591 0.1660526 [7,] 0.00000000 -0.2486516 0.00000000 -0.2796660 0.1305283 [8,] 0.00000000 -0.2640041 0.00000000 -0.2819118 0.0000000 [9,] 0.00000000 -0.2870589 0.00000000 -0.2330626 0.0000000 [10,] 0.00000000 -0.2463887 0.00000000 -0.2642626 0.0000000 [11,] 0.00000000 0.0000000 0.00000000 -0.2357951 0.0000000 [12,] 0.00000000 0.0000000 0.00000000 0.0000000 0.0000000 [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,] 0.33167 0.22282 0.94711 0.16644 0.47869 0.72437 0.45979 0.09296 0.61255 [2,] 0.33082 0.22248 NA 0.16483 0.48018 0.72621 0.45434 0.09151 0.61426 [3,] 0.30878 0.17225 NA 0.14215 0.44234 NA 0.43284 0.07673 0.59981 [4,] 0.20750 0.15562 NA 0.12530 0.34900 NA 0.37645 0.07328 NA [5,] 0.17336 0.15914 NA 0.10478 0.37058 NA NA 0.07249 NA [6,] 0.21055 0.12043 NA 0.11445 NA NA NA 0.05475 NA [7,] NA 0.13228 NA 0.07523 NA NA NA 0.05188 NA [8,] NA 0.08781 NA 0.08096 NA NA NA 0.04012 NA [9,] NA NA NA 0.10570 NA NA NA 0.02630 NA [10,] NA NA NA NA NA NA NA 0.05489 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,] 0.07026 0.25832 [2,] 0.07023 0.24944 [3,] 0.05245 0.23463 [4,] 0.05044 0.18742 [5,] 0.03967 0.22390 [6,] 0.04269 0.18605 [7,] 0.02791 0.28842 [8,] 0.02843 NA [9,] 0.06591 NA [10,] 0.03774 NA [11,] 0.07502 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 0.1266 -0.1532 0.0082 -0.1696 0.0888 0.0459 0.0959 -0.2167 s.e. 0.1293 0.1243 0.1238 0.1209 0.1245 0.1294 0.1289 0.1268 ar9 ar10 ar11 -0.0665 -0.2357 0.1504 s.e. 0.1306 0.1277 0.1317 sigma^2 estimated as 48584230: log likelihood = -678.63, aic = 1381.26 [[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 0.1266 -0.1532 0.0082 -0.1696 0.0888 0.0459 0.0959 -0.2167 s.e. 0.1293 0.1243 0.1238 0.1209 0.1245 0.1294 0.1289 0.1268 ar9 ar10 ar11 -0.0665 -0.2357 0.1504 s.e. 0.1306 0.1277 0.1317 sigma^2 estimated as 48584230: log likelihood = -678.63, aic = 1381.26 [[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 ar9 0.1252 -0.1524 0 -0.1684 0.0876 0.0455 0.0937 -0.2156 -0.0662 s.e. 0.1276 0.1235 0 0.1197 0.1233 0.1294 0.1244 0.1255 0.1305 ar10 ar11 -0.2353 0.1482 s.e. 0.1275 0.1273 sigma^2 estimated as 48589464: log likelihood = -678.63, aic = 1379.26 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.1303 -0.1642 0 -0.1756 0.0943 0 0.0977 -0.2229 -0.0688 s.e. 0.1269 0.1188 0 0.1180 0.1219 0 0.1237 0.1237 0.1304 ar10 ar11 -0.2457 0.1524 s.e. 0.1240 0.1269 sigma^2 estimated as 48680631: log likelihood = -678.69, aic = 1377.38 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.1524 -0.1703 0 -0.1830 0.1115 0 0.1091 -0.2263 0 -0.2482 s.e. 0.1196 0.1184 0 0.1177 0.1181 0 0.1224 0.1240 0 0.1243 ar11 0.1661 s.e. 0.1245 sigma^2 estimated as 48962344: log likelihood = -678.83, aic = 1375.66 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.1645 -0.1709 0 -0.1941 0.1074 0 0 -0.2278 0 -0.2612 s.e. 0.1194 0.1198 0 0.1178 0.1190 0 0 0.1246 0 0.1241 ar11 0.1528 s.e. 0.1243 sigma^2 estimated as 49585131: log likelihood = -679.23, aic = 1374.45 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.1504 -0.1873 0 -0.1898 0 0 0 -0.2434 0 -0.2585 s.e. 0.1189 0.1189 0 0.1185 0 0 0 0.1242 0 0.1248 ar11 0.1661 s.e. 0.1241 sigma^2 estimated as 50232556: log likelihood = -679.63, aic = 1373.26 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0 -0.1832 0 -0.2134 0 0 0 -0.2487 0 -0.2797 0.1305 s.e. 0 0.1201 0 0.1179 0 0 0 0.1254 0 0.1242 0.1219 sigma^2 estimated as 51402996: log likelihood = -680.42, aic = 1372.85 [[3]][[9]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0 -0.2070 0 -0.2115 0 0 0 -0.2640 0 -0.2819 0 s.e. 0 0.1194 0 0.1192 0 0 0 0.1259 0 0.1256 0 sigma^2 estimated as 52433210: log likelihood = -680.99, aic = 1371.98 [[3]][[10]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0 0 0 -0.2019 0 0 0 -0.2871 0 -0.2331 0 s.e. 0 0 0 0.1230 0 0 0 0.1262 0 0.1245 0 sigma^2 estimated as 54893498: log likelihood = -682.46, aic = 1372.93 [[3]][[11]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0 0 0 0 0 0 0 -0.2464 0 -0.2643 0 s.e. 0 0 0 0 0 0 0 0.1260 0 0.1246 0 sigma^2 estimated as 57247629: log likelihood = -683.79, aic = 1373.57 [[3]][[12]] 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 0 0 0 0 0 0 0 -0.2358 0 s.e. 0 0 0 0 0 0 0 0 0 0.1303 0 sigma^2 estimated as 61131983: log likelihood = -685.58, aic = 1375.16 $aic [1] 1381.256 1379.260 1377.384 1375.661 1374.450 1373.259 1372.849 1371.978 [9] 1372.928 1373.571 1375.156 1376.282 There were 13 warnings (use warnings() to see them) > postscript(file="/var/www/html/rcomp/tmp/1j6mx1260541227.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 = 67 Frequency = 1 [1] 267.412858 -45.674731 -2515.997404 -5747.241657 -3905.675383 [6] 23.323267 21767.410529 7856.053695 1215.725281 -3459.617906 [11] -6175.741981 1216.917628 -1397.473637 -1419.492503 -3013.660698 [16] -4881.340916 2271.575511 -240.832027 22560.979730 3087.569275 [21] -384.478163 -3824.443559 -6894.570781 1173.105121 -2953.152775 [26] -934.330886 -4281.743394 -2126.252182 4386.214757 -2115.032455 [31] 18753.675794 -46.061237 -7.949645 -13515.997521 -9261.470834 [36] -3913.596658 2330.739733 -5239.988139 -9181.727008 -2562.053044 [41] -2935.911926 -10467.125284 23555.141562 -815.558266 -8376.701881 [46] -4954.927760 -6472.821997 2537.507173 -680.968835 -3937.042047 [51] -9679.361397 -2514.471152 -6064.325298 1859.632775 19602.416716 [56] -548.896493 -3066.676761 -1002.566471 1503.601832 8676.245393 [61] 6677.306420 4029.178976 1980.046153 1219.996765 -1558.901361 [66] 4657.082264 20395.375419 > postscript(file="/var/www/html/rcomp/tmp/288dr1260541227.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/3pfum1260541227.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/4i6eu1260541227.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/5875d1260541227.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/671sa1260541228.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/7kwr91260541228.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/8uvup1260541228.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/9ye0y1260541228.tab") > system("convert tmp/1j6mx1260541227.ps tmp/1j6mx1260541227.png") > system("convert tmp/288dr1260541227.ps tmp/288dr1260541227.png") > system("convert tmp/3pfum1260541227.ps tmp/3pfum1260541227.png") > system("convert tmp/4i6eu1260541227.ps tmp/4i6eu1260541227.png") > system("convert tmp/5875d1260541227.ps tmp/5875d1260541227.png") > system("convert tmp/671sa1260541228.ps tmp/671sa1260541228.png") > system("convert tmp/7kwr91260541228.ps tmp/7kwr91260541228.png") > > > proc.time() user system elapsed 2.820 1.087 5.825