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Type 'q()' to quit R. > x <- c(286602,283042,276687,277915,277128,277103,275037,270150,267140,264993,287259,291186,292300,288186,281477,282656,280190,280408,276836,275216,274352,271311,289802,290726,292300,278506,269826,265861,269034,264176,255198,253353,246057,235372,258556,260993,254663,250643,243422,247105,248541,245039,237080,237085,225554,226839,247934,248333,246969,245098,246263,255765,264319,268347,273046,273963,267430,271993,292710,295881) > 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,] 0.10377077 -0.1514808 0.03804237 -0.1125426 0.1237061 0.08212022 [2,] 0.09700376 -0.1486123 0.00000000 -0.1084074 0.1186159 0.08090958 [3,] 0.11086099 -0.1536420 0.00000000 -0.1146321 0.1285066 0.08325800 [4,] 0.12518815 -0.1736241 0.00000000 -0.1294182 0.1387682 0.00000000 [5,] 0.14732325 -0.1708052 0.00000000 -0.1398925 0.1328279 0.00000000 [6,] 0.12082823 -0.1891273 0.00000000 -0.1423858 0.1560175 0.00000000 [7,] 0.00000000 -0.1834359 0.00000000 -0.1522697 0.1445939 0.00000000 [8,] 0.00000000 -0.1994378 0.00000000 -0.1451964 0.0000000 0.00000000 [9,] 0.00000000 -0.1907158 0.00000000 0.0000000 0.0000000 0.00000000 [10,] 0.00000000 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 [11,] 0.00000000 0.0000000 0.00000000 0.0000000 0.0000000 0.00000000 [12,] 0.00000000 0.0000000 0.00000000 0.0000000 0.0000000 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,] 0.1182348 -0.1899746 -0.05144078 -0.2690627 0.1288065 [2,] 0.1090239 -0.1850771 -0.04904590 -0.2666076 0.1189484 [3,] 0.1164773 -0.1880069 0.00000000 -0.2687691 0.1289554 [4,] 0.1231077 -0.2011121 0.00000000 -0.2870229 0.1413346 [5,] 0.0000000 -0.2014585 0.00000000 -0.2994986 0.1294152 [6,] 0.0000000 -0.2109475 0.00000000 -0.3040965 0.0000000 [7,] 0.0000000 -0.2036380 0.00000000 -0.3230739 0.0000000 [8,] 0.0000000 -0.2188100 0.00000000 -0.3104490 0.0000000 [9,] 0.0000000 -0.2001001 0.00000000 -0.3387448 0.0000000 [10,] 0.0000000 -0.2187614 0.00000000 -0.2950599 0.0000000 [11,] 0.0000000 0.0000000 0.00000000 -0.2668572 0.0000000 [12,] 0.0000000 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,] 0.42484 0.24881 0.76958 0.38192 0.34333 0.52451 0.35480 0.13222 0.69035 [2,] 0.44874 0.25690 NA 0.39743 0.35977 0.53096 0.37943 0.13896 0.70363 [3,] 0.36701 0.23909 NA 0.36817 0.31250 0.51954 0.34262 0.13280 NA [4,] 0.30127 0.17098 NA 0.30319 0.27329 NA 0.31479 0.10337 NA [5,] 0.22076 0.18427 NA 0.26943 0.29894 NA NA 0.10489 NA [6,] 0.30678 0.14065 NA 0.26412 0.22057 NA NA 0.09175 NA [7,] NA 0.15497 NA 0.23441 0.25858 NA NA 0.10552 NA [8,] NA 0.12516 NA 0.26220 NA NA NA 0.08419 NA [9,] NA 0.14136 NA NA NA NA NA 0.11087 NA [10,] NA NA NA NA NA NA NA 0.08097 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,] 0.05330 0.36688 [2,] 0.05496 0.39171 [3,] 0.05292 0.34529 [4,] 0.03522 0.29771 [5,] 0.02896 0.34182 [6,] 0.02786 NA [7,] 0.01915 NA [8,] 0.02544 NA [9,] 0.01390 NA [10,] 0.02878 NA [11,] 0.05691 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 ar9 0.1038 -0.1515 0.0380 -0.1125 0.1237 0.0821 0.1182 -0.190 -0.0514 s.e. 0.1289 0.1298 0.1291 0.1275 0.1293 0.1281 0.1265 0.124 0.1283 ar10 ar11 -0.2691 0.1288 s.e. 0.1358 0.1414 sigma^2 estimated as 46639382: log likelihood = -605.52, aic = 1235.04 [[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 ar9 0.1038 -0.1515 0.0380 -0.1125 0.1237 0.0821 0.1182 -0.190 -0.0514 s.e. 0.1289 0.1298 0.1291 0.1275 0.1293 0.1281 0.1265 0.124 0.1283 ar10 ar11 -0.2691 0.1288 s.e. 0.1358 0.1414 sigma^2 estimated as 46639382: log likelihood = -605.52, aic = 1235.04 [[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 0.097 -0.1486 0 -0.1084 0.1186 0.0809 0.1090 -0.1851 -0.0490 s.e. 0.127 0.1296 0 0.1270 0.1283 0.1282 0.1229 0.1230 0.1282 ar10 ar11 -0.2666 0.1189 s.e. 0.1356 0.1376 sigma^2 estimated as 46715379: log likelihood = -605.56, aic = 1233.13 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.1109 -0.1536 0 -0.1146 0.1285 0.0833 0.1165 -0.188 0 s.e. 0.1218 0.1290 0 0.1262 0.1259 0.1284 0.1216 0.123 0 ar10 ar11 -0.2688 0.1290 s.e. 0.1356 0.1353 sigma^2 estimated as 46861241: log likelihood = -605.64, aic = 1231.27 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.1252 -0.1736 0 -0.1294 0.1388 0 0.1231 -0.2011 0 -0.2870 s.e. 0.1199 0.1250 0 0.1244 0.1253 0 0.1213 0.1213 0 0.1327 ar11 0.1413 s.e. 0.1343 sigma^2 estimated as 47199019: log likelihood = -605.85, aic = 1229.69 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.1473 -0.1708 0 -0.1399 0.1328 0 0 -0.2015 0 -0.2995 s.e. 0.1189 0.1269 0 0.1253 0.1266 0 0 0.1221 0 0.1333 ar11 0.1294 s.e. 0.1349 sigma^2 estimated as 48093952: log likelihood = -606.36, aic = 1228.71 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.1208 -0.1891 0 -0.1424 0.1560 0 0 -0.2109 0 -0.3041 s.e. 0.1171 0.1264 0 0.1262 0.1259 0 0 0.1228 0 0.1345 ar11 0 s.e. 0 sigma^2 estimated as 48938537: log likelihood = -606.81, aic = 1227.62 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0 -0.1834 0 -0.1523 0.1446 0 0 -0.2036 0 -0.3231 0 s.e. 0 0.1272 0 0.1266 0.1266 0 0 0.1237 0 0.1338 0 sigma^2 estimated as 49794045: log likelihood = -607.34, aic = 1226.68 [[3]][[9]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0 -0.1994 0 -0.1452 0 0 0 -0.2188 0 -0.3104 0 s.e. 0 0.1281 0 0.1282 0 0 0 0.1244 0 0.1351 0 sigma^2 estimated as 51005252: log likelihood = -607.98, aic = 1225.96 [[3]][[10]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0 -0.1907 0 0 0 0 0 -0.2001 0 -0.3387 0 s.e. 0 0.1278 0 0 0 0 0 0.1235 0 0.1334 0 sigma^2 estimated as 52101018: log likelihood = -608.62, aic = 1225.24 [[3]][[11]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0 0 0 0 0 0 0 -0.2188 0 -0.2951 0 s.e. 0 0 0 0 0 0 0 0.1231 0 0.1315 0 sigma^2 estimated as 54106084: log likelihood = -609.71, aic = 1225.41 [[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 -0.2669 0 s.e. 0 0 0 0 0 0 0 0 0 0.1374 0 sigma^2 estimated as 57544615: log likelihood = -611.19, aic = 1226.39 $aic [1] 1235.039 1233.126 1231.271 1229.690 1228.710 1227.617 1226.676 1225.964 [9] 1225.240 1225.413 1226.390 1227.935 There were 13 warnings (use warnings() to see them) > postscript(file="/var/www/html/rcomp/tmp/1q9ci1260550008.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] 286.6018 -3430.9004 -6124.5427 1183.4679 -758.4603 -24.0934 [7] -1991.0787 -4709.7782 -2900.8456 -2069.1413 21458.6275 2976.9882 [13] -581.8778 -3786.2993 -6919.0166 1172.3286 -3017.3271 -1086.1313 [19] -4375.2403 -2192.9425 5077.8433 -1993.0516 18788.2790 -173.8507 [25] -216.3452 -13479.3753 -9338.0700 -3906.8251 2219.7859 -5290.3087 [31] -9208.5647 -2656.5129 -2361.5428 -10438.4239 23604.0333 -1244.0288 [37] -8646.3208 -5078.0890 -6374.2620 2386.6075 -959.8443 -3994.3516 [43] -9905.9904 -2846.3696 -5344.1817 1935.3311 19405.7937 -673.7661 [49] -3290.9761 -888.1648 1548.2070 8567.4659 6430.0832 4029.3343 [55] 1621.8692 1259.9116 -903.6465 4669.4760 20353.0067 2671.7101 > postscript(file="/var/www/html/rcomp/tmp/22vln1260550008.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/316dh1260550008.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/4c4pn1260550008.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/5jl9g1260550008.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/6lbnx1260550008.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/70it61260550008.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/8nz241260550008.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/98q2t1260550009.tab") > > system("convert tmp/1q9ci1260550008.ps tmp/1q9ci1260550008.png") > system("convert tmp/22vln1260550008.ps tmp/22vln1260550008.png") > system("convert tmp/316dh1260550008.ps tmp/316dh1260550008.png") > system("convert tmp/4c4pn1260550008.ps tmp/4c4pn1260550008.png") > system("convert tmp/5jl9g1260550008.ps tmp/5jl9g1260550008.png") > system("convert tmp/6lbnx1260550008.ps tmp/6lbnx1260550008.png") > system("convert tmp/70it61260550008.ps tmp/70it61260550008.png") > > > proc.time() user system elapsed 2.680 1.036 3.117