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Type 'q()' to quit R. > x <- c(9.5,9.6,9.5,9.1,8.9,9,10.1,10.3,10.2,9.6,9.2,9.3,9.4,9.4,9.2,9,9,9,9.8,10,9.8,9.3,9,9,9.1,9.1,9.1,9.2,8.8,8.3,8.4,8.1,7.7,7.9,7.9,8,7.9,7.6,7.1,6.8,6.5,6.9,8.2,8.7,8.3,7.9,7.5,7.8,8.3,8.4,8.2,7.7,7.2,7.3,8.1,8.5) > par9 = '0' > par8 = '0' > par7 = '0' > par6 = '3' > 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,] 0.7738333 -0.2480846 -0.2984204 0.03625126 0.04531360 0.18176833 [2,] 0.7711580 -0.2501527 -0.2917736 0.02837594 0.04946360 0.17814859 [3,] 0.7660718 -0.2490576 -0.2782506 0.00000000 0.06510264 0.18008867 [4,] 0.7639960 -0.2744177 -0.2488139 0.00000000 0.00000000 0.22632855 [5,] 0.7847312 -0.2997934 -0.2303480 0.00000000 0.00000000 0.20604323 [6,] 0.7911647 -0.3189140 -0.2542142 0.00000000 0.00000000 0.19745196 [7,] 0.7617946 -0.3023964 -0.2337726 0.00000000 0.00000000 0.06934742 [8,] 0.7568108 -0.3281616 -0.2545223 0.00000000 0.00000000 0.00000000 [9,] 0.7831123 -0.2858662 -0.3121542 0.00000000 0.00000000 0.00000000 [10,] 0.6084733 0.0000000 -0.4901472 0.00000000 0.00000000 0.00000000 [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,] -0.2268075 0.1305517 0.10000393 -0.03364868 -0.1482897 [2,] -0.2168243 0.1297606 0.07880634 0.00000000 -0.1653067 [3,] -0.2262079 0.1324306 0.07883822 0.00000000 -0.1658724 [4,] -0.2331763 0.1186301 0.08007763 0.00000000 -0.1605424 [5,] -0.2575337 0.1948871 0.00000000 0.00000000 -0.1403098 [6,] -0.2170814 0.2352872 0.00000000 0.00000000 0.0000000 [7,] 0.0000000 0.1115265 0.00000000 0.00000000 0.0000000 [8,] 0.0000000 0.1114246 0.00000000 0.00000000 0.0000000 [9,] 0.0000000 0.0000000 0.00000000 0.00000000 0.0000000 [10,] 0.0000000 0.0000000 0.00000000 0.00000000 0.0000000 [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,] 1e-05 0.21836 0.14368 0.85931 0.82860 0.37820 0.30115 0.55830 0.63904 [2,] 1e-05 0.21363 0.14438 0.88656 0.81116 0.38466 0.30307 0.56080 0.63773 [3,] 0e+00 0.21596 0.11321 NA 0.71090 0.37777 0.25839 0.55083 0.63731 [4,] 0e+00 0.14907 0.11183 NA NA 0.16136 0.24224 0.58732 0.63137 [5,] 0e+00 0.10199 0.12871 NA NA 0.18658 0.18286 0.19564 NA [6,] 0e+00 0.09188 0.09913 NA NA 0.21202 0.26682 0.12176 NA [7,] 0e+00 0.11794 0.13421 NA NA 0.52888 NA 0.28783 NA [8,] 0e+00 0.08390 0.09803 NA NA NA NA 0.28822 NA [9,] 0e+00 0.12758 0.03524 NA NA NA NA NA NA [10,] 0e+00 NA 0.00000 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 [,10] [,11] [1,] 0.87253 0.34913 [2,] NA 0.16052 [3,] NA 0.15898 [4,] NA 0.17021 [5,] NA 0.19948 [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 ar9 0.7738 -0.2481 -0.2984 0.0363 0.0453 0.1818 -0.2268 0.1306 0.1000 s.e. 0.1502 0.1987 0.2004 0.2033 0.2081 0.2042 0.2168 0.2213 0.2117 ar10 ar11 -0.0336 -0.1483 s.e. 0.2085 0.1567 sigma^2 estimated as 0.05046: log likelihood = 1.88, aic = 20.23 [[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 0.7738 -0.2481 -0.2984 0.0363 0.0453 0.1818 -0.2268 0.1306 0.1000 s.e. 0.1502 0.1987 0.2004 0.2033 0.2081 0.2042 0.2168 0.2213 0.2117 ar10 ar11 -0.0336 -0.1483 s.e. 0.2085 0.1567 sigma^2 estimated as 0.05046: log likelihood = 1.88, aic = 20.23 [[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 0.7712 -0.2502 -0.2918 0.0284 0.0495 0.1781 -0.2168 0.1298 0.0788 s.e. 0.1494 0.1983 0.1964 0.1978 0.2058 0.2029 0.2081 0.2214 0.1662 ar10 ar11 0 -0.1653 s.e. 0 0.1159 sigma^2 estimated as 0.0505: log likelihood = 1.87, aic = 18.26 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.7661 -0.2491 -0.2783 0 0.0651 0.1801 -0.2262 0.1324 0.0788 s.e. 0.1452 0.1985 0.1723 0 0.1746 0.2022 0.1977 0.2204 0.1661 ar10 ar11 0 -0.1659 s.e. 0 0.1159 sigma^2 estimated as 0.05053: log likelihood = 1.86, aic = 16.28 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.7640 -0.2744 -0.2488 0 0 0.2263 -0.2332 0.1186 0.0801 0 s.e. 0.1454 0.1871 0.1535 0 0 0.1591 0.1969 0.2171 0.1658 0 ar11 -0.1605 s.e. 0.1153 sigma^2 estimated as 0.05073: log likelihood = 1.79, aic = 14.42 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.7847 -0.2998 -0.2303 0 0 0.2060 -0.2575 0.1949 0 0 s.e. 0.1392 0.1798 0.1490 0 0 0.1538 0.1905 0.1485 0 0 ar11 -0.1403 s.e. 0.1078 sigma^2 estimated as 0.05109: log likelihood = 1.68, aic = 12.65 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.7912 -0.3189 -0.2542 0 0 0.1975 -0.2171 0.2353 0 0 s.e. 0.1424 0.1855 0.1512 0 0 0.1561 0.1933 0.1494 0 0 ar11 0 s.e. 0 sigma^2 estimated as 0.05349: log likelihood = 0.86, aic = 12.28 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.7618 -0.3024 -0.2338 0 0 0.0693 0 0.1115 0 0 0 s.e. 0.1433 0.1901 0.1536 0 0 0.1094 0 0.1038 0 0 0 sigma^2 estimated as 0.05544: log likelihood = 0.25, aic = 11.5 [[3]][[9]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.7568 -0.3282 -0.2545 0 0 0 0 0.1114 0 0 0 s.e. 0.1439 0.1861 0.1510 0 0 0 0 0.1038 0 0 0 sigma^2 estimated as 0.05598: log likelihood = 0.05, aic = 9.9 [[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 0.7831 -0.2859 -0.3122 0 0 0 0 0 0 0 0 s.e. 0.1440 0.1846 0.1444 0 0 0 0 0 0 0 0 sigma^2 estimated as 0.05775: log likelihood = -0.51, aic = 9.02 [[3]][[11]] NULL $aic [1] 20.232732 18.258788 16.279421 14.418456 12.649705 12.278489 11.500969 [8] 9.899740 9.024415 9.368838 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 log(s2) : NaNs produced 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 10: 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/1olf41260534046.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 = 56 Frequency = 1 [1] 5.484825e-03 2.530340e-03 1.571973e-03 8.189101e-04 4.723622e-04 [6] 4.850221e-04 1.439391e-03 1.446569e-03 1.190709e-03 5.006037e-04 [11] 7.278363e-05 -5.270156e-03 -3.362224e-02 -6.100609e-02 -3.027177e-02 [16] 2.259502e-01 -1.642450e-02 -2.306646e-01 -1.020847e-01 2.687779e-01 [21] -2.169753e-01 8.466498e-02 -6.897851e-03 -1.809400e-01 1.381133e-01 [26] 2.628799e-03 1.687846e-01 1.433775e-01 -5.777605e-01 -3.856438e-02 [31] -3.291441e-01 -2.196161e-01 -1.646273e-01 4.951814e-01 -4.614289e-01 [36] 2.741803e-03 2.595655e-02 -2.114466e-02 -2.910241e-01 -1.566345e-01 [41] 1.766656e-01 5.512652e-01 3.989239e-01 1.487602e-01 -2.511669e-03 [46] 3.277962e-03 3.195907e-01 3.417252e-01 1.417386e-01 -1.375558e-01 [51] 2.207056e-01 -1.332947e-01 1.672440e-01 -1.069045e-01 -3.846704e-01 [56] 1.433655e-01 > postscript(file="/var/www/html/rcomp/tmp/248lu1260534046.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/3khw81260534046.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/4dxuo1260534046.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/5fkkr1260534046.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/6cpwl1260534046.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/7km3c1260534046.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/85l6k1260534046.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/9oest1260534046.tab") > > system("convert tmp/1olf41260534046.ps tmp/1olf41260534046.png") > system("convert tmp/248lu1260534046.ps tmp/248lu1260534046.png") > system("convert tmp/3khw81260534046.ps tmp/3khw81260534046.png") > system("convert tmp/4dxuo1260534046.ps tmp/4dxuo1260534046.png") > system("convert tmp/5fkkr1260534046.ps tmp/5fkkr1260534046.png") > system("convert tmp/6cpwl1260534046.ps tmp/6cpwl1260534046.png") > system("convert tmp/7km3c1260534046.ps tmp/7km3c1260534046.png") > > > proc.time() user system elapsed 5.049 1.034 8.036