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(255 + ,280.2 + ,299.9 + ,339.2 + ,374.2 + ,393.5 + ,389.2 + ,381.7 + ,375.2 + ,369 + ,357.4 + ,352.1 + ,346.5 + ,342.9 + ,340.3 + ,328.3 + ,322.9 + ,314.3 + ,308.9 + ,294 + ,285.6 + ,281.2 + ,280.3 + ,278.8 + ,274.5 + ,270.4 + ,263.4 + ,259.9 + ,258 + ,262.7 + ,284.7 + ,311.3 + ,322.1 + ,327 + ,331.3 + ,333.3 + ,321.4 + ,327 + ,320 + ,314.7 + ,316.7 + ,314.4 + ,321.3 + ,318.2 + ,307.2 + ,301.3 + ,287.5 + ,277.7 + ,274.4 + ,258.8 + ,253.3 + ,251 + ,248.4 + ,249.5 + ,246.1 + ,244.5 + ,243.6 + ,244 + ,240.8 + ,249.8 + ,248 + ,259.4 + ,260.5 + ,260.8 + ,261.3 + ,259.5 + ,256.6 + ,257.9 + ,256.5 + ,254.2 + ,253.3 + ,253.8 + ,255.5 + ,257.1 + ,257.3 + ,253.2 + ,252.8 + ,252 + ,250.7 + ,252.2 + ,250 + ,251 + ,253.4 + ,251.2 + ,255.6 + ,261.1 + ,258.9 + ,259.9 + ,261.2 + ,264.7 + ,267.1 + ,266.4 + ,267.7 + ,268.6 + ,267.5 + ,268.5 + ,268.5 + ,270.5 + ,270.9 + ,270.1 + ,269.3 + ,269.8 + ,270.1 + ,264.9 + ,263.7 + ,264.8 + ,263.7 + ,255.9 + ,276.2 + ,360.1 + ,380.5 + ,373.7 + ,369.8 + ,366.6 + ,359.3 + ,345.8 + ,326.2 + ,324.5 + ,328.1 + ,327.5 + ,324.4 + ,316.5 + ,310.9 + ,301.5 + ,291.7 + ,290.4 + ,287.4 + ,277.7 + ,281.6 + ,288 + ,276 + ,272.9 + ,283 + ,283.3 + ,276.8 + ,284.5 + ,282.7 + ,281.2 + ,287.4 + ,283.1 + ,284 + ,285.5 + ,289.2 + ,292.5 + ,296.4 + ,305.2 + ,303.9 + ,311.5 + ,316.3 + ,316.7 + ,322.5 + ,317.1 + ,309.8 + ,303.8 + ,290.3 + ,293.7 + ,291.7 + ,296.5 + ,289.1 + ,288.5 + ,293.8 + ,297.7 + ,305.4 + ,302.7 + ,302.5 + ,303 + ,294.5 + ,294.1 + ,294.5 + ,297.1 + ,289.4 + ,292.4 + ,287.9 + ,286.6 + ,280.5 + ,272.4 + ,269.2 + ,270.6 + ,267.3 + ,262.5 + ,266.8 + ,268.8 + ,263.1 + ,261.2 + ,266 + ,262.5 + ,265.2 + ,261.3 + ,253.7 + ,249.2 + ,239.1 + ,236.4 + ,235.2 + ,245.2 + ,246.2 + ,247.7 + ,251.4 + ,253.3 + ,254.8 + ,250 + ,249.3 + ,241.5 + ,243.3 + ,248 + ,253 + ,252.9 + ,251.5 + ,251.6 + ,253.5 + ,259.8 + ,334.1 + ,448 + ,445.8 + ,445 + ,448.2 + ,438.2 + ,439.8 + ,423.4 + ,410.8 + ,408.4 + ,406.7 + ,405.9 + ,402.7 + ,405.1 + ,399.6 + ,386.5 + ,381.4 + ,375.2 + ,357.7 + ,359 + ,355 + ,352.7 + ,344.4 + ,343.8 + ,338 + ,339 + ,333.3 + ,334.4 + ,328.3 + ,330.7 + ,330 + ,331.6 + ,351.2 + ,389.4 + ,410.9 + ,442.8 + ,462.8 + ,466.9 + ,461.7 + ,439.2 + ,430.3 + ,416.1 + ,402.5 + ,397.3 + ,403.3 + ,395.9 + ,387.8 + ,378.6 + ,377.1 + ,370.4 + ,362 + ,350.3 + ,348.2 + ,344.6 + ,343.5 + ,342.8 + ,347.6 + ,346.6 + ,349.5 + ,342.1 + ,342 + ,342.8 + ,339.3 + ,348.2 + ,333.7 + ,334.7 + ,354 + ,367.7 + ,363.3 + ,358.4 + ,353.1 + ,343.1 + ,344.6 + ,344.4 + ,333.9 + ,331.7 + ,324.3 + ,321.2 + ,322.4 + ,321.7 + ,320.5 + ,312.8 + ,309.7 + ,315.6 + ,309.7 + ,304.6 + ,302.5 + ,301.5 + ,298.8 + ,291.3 + ,293.6 + ,294.6 + ,285.9 + ,297.6 + ,301.1 + ,293.8 + ,297.7 + ,292.9 + ,292.1 + ,287.2 + ,288.2 + ,283.8 + ,299.9 + ,292.4 + ,293.3 + ,300.8 + ,293.7 + ,293.1 + ,294.4 + ,292.1 + ,291.9 + ,282.5 + ,277.9 + ,287.5 + ,289.2 + ,285.6 + ,293.2 + ,290.8 + ,283.1 + ,275 + ,287.8 + ,287.8 + ,287.4 + ,284 + ,277.8 + ,277.6 + ,304.9 + ,294 + ,300.9 + ,324 + ,332.9 + ,341.6 + ,333.4 + ,348.2 + ,344.7 + ,344.7 + ,329.3 + ,323.5 + ,323.2 + ,317.4 + ,330.1 + ,329.2 + ,334.9 + ,315.8 + ,315.4 + ,319.6 + ,317.3 + ,313.8 + ,315.8) > 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,] 0.4713848 -0.10155061 0.1173685 -0.07264263 0.006265563 -0.09561733 [2,] 0.4713660 -0.10166447 0.1174443 -0.07275680 0.006492885 -0.09586169 [3,] 0.4712015 -0.10156371 0.1174412 -0.07281820 0.006262637 -0.09584576 [4,] 0.4707791 -0.10086568 0.1165607 -0.07019421 0.000000000 -0.09327245 [5,] 0.4720551 -0.10098296 0.1172511 -0.07129478 0.000000000 -0.09889331 [6,] 0.4718404 -0.10313161 0.1150479 -0.07074939 0.000000000 -0.09522787 [7,] 0.4715320 -0.10263355 0.1177432 -0.06715662 0.000000000 -0.09582499 [8,] 0.4666940 -0.09576057 0.0884483 0.00000000 0.000000000 -0.10113098 [9,] 0.4609273 -0.05592314 0.0000000 0.00000000 0.000000000 -0.09398952 [10,] 0.4365635 0.00000000 0.0000000 0.00000000 0.000000000 -0.09479968 [11,] 0.4419165 0.00000000 0.0000000 0.00000000 0.000000000 0.00000000 [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,] -0.01409109 0.001696411 0.03809185 -0.06154406 0.002891314 [2,] -0.01337262 0.000000000 0.03878929 -0.06177140 0.003068671 [3,] -0.01345926 0.000000000 0.03856961 -0.06041687 0.000000000 [4,] -0.01404608 0.000000000 0.03852373 -0.06041092 0.000000000 [5,] 0.00000000 0.000000000 0.03801648 -0.06155104 0.000000000 [6,] 0.00000000 0.000000000 0.00000000 -0.04596845 0.000000000 [7,] 0.00000000 0.000000000 0.00000000 0.00000000 0.000000000 [8,] 0.00000000 0.000000000 0.00000000 0.00000000 0.000000000 [9,] 0.00000000 0.000000000 0.00000000 0.00000000 0.000000000 [10,] 0.00000000 0.000000000 0.00000000 0.00000000 0.000000000 [11,] 0.00000000 0.000000000 0.00000000 0.00000000 0.000000000 [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 0.08328 0.04742 0.22140 0.91629 0.11076 0.81502 0.97748 0.52494 [2,] 0 0.08213 0.04705 0.21956 0.91248 0.10577 0.80592 NA 0.47409 [3,] 0 0.08228 0.04706 0.21911 0.91537 0.10581 0.80459 NA 0.47549 [4,] 0 0.08249 0.04650 0.19285 NA 0.08420 0.79524 NA 0.47596 [5,] 0 0.08214 0.04502 0.18470 NA 0.04567 NA NA 0.48156 [6,] 0 0.07568 0.04902 0.18850 NA 0.05310 NA NA NA [7,] 0 0.07748 0.04400 0.21126 NA 0.05193 NA NA NA [8,] 0 0.09851 0.09897 NA NA 0.04000 NA NA NA [9,] 0 0.28991 NA NA NA 0.05618 NA NA NA [10,] 0 NA NA NA NA 0.05444 NA NA NA [11,] 0 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,] 0.30253 0.95750 [2,] 0.29498 0.95456 [3,] 0.26236 NA [4,] 0.26241 NA [5,] 0.25200 NA [6,] 0.34797 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 0.4714 -0.1016 0.1174 -0.0726 0.0063 -0.0956 -0.0141 0.0017 s.e. 0.0530 0.0585 0.0590 0.0593 0.0596 0.0598 0.0602 0.0601 ar9 ar10 ar11 0.0381 -0.0615 0.0029 s.e. 0.0599 0.0596 0.0542 sigma^2 estimated as 109.6: log likelihood = -1348.85, aic = 2721.69 [[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 0.4714 -0.1016 0.1174 -0.0726 0.0063 -0.0956 -0.0141 0.0017 s.e. 0.0530 0.0585 0.0590 0.0593 0.0596 0.0598 0.0602 0.0601 ar9 ar10 ar11 0.0381 -0.0615 0.0029 s.e. 0.0599 0.0596 0.0542 sigma^2 estimated as 109.6: log likelihood = -1348.85, aic = 2721.69 [[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 0.4714 -0.1017 0.1174 -0.0728 0.0065 -0.0959 -0.0134 0 0.0388 s.e. 0.0530 0.0583 0.0589 0.0592 0.0590 0.0591 0.0544 0 0.0541 ar10 ar11 -0.0618 0.0031 s.e. 0.0589 0.0538 sigma^2 estimated as 109.6: log likelihood = -1348.85, aic = 2719.69 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.4712 -0.1016 0.1174 -0.0728 0.0063 -0.0958 -0.0135 0 0.0386 s.e. 0.0529 0.0583 0.0589 0.0591 0.0589 0.0591 0.0544 0 0.0540 ar10 ar11 -0.0604 0 s.e. 0.0538 0 sigma^2 estimated as 109.6: log likelihood = -1348.85, aic = 2717.69 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 0.4708 -0.1009 0.1166 -0.0702 0 -0.0933 -0.0140 0 0.0385 s.e. 0.0527 0.0579 0.0583 0.0538 0 0.0539 0.0541 0 0.0540 ar10 ar11 -0.0604 0 s.e. 0.0538 0 sigma^2 estimated as 109.6: log likelihood = -1348.85, aic = 2715.71 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.4721 -0.1010 0.1173 -0.0713 0 -0.0989 0 0 0.038 -0.0616 s.e. 0.0525 0.0579 0.0583 0.0536 0 0.0493 0 0 0.054 0.0536 ar11 0 s.e. 0 sigma^2 estimated as 109.6: log likelihood = -1348.89, aic = 2713.77 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 0.4718 -0.1031 0.1150 -0.0707 0 -0.0952 0 0 0 -0.0460 s.e. 0.0526 0.0579 0.0582 0.0537 0 0.0491 0 0 0 0.0489 ar11 0 s.e. 0 sigma^2 estimated as 109.7: log likelihood = -1349.13, aic = 2712.27 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.4715 -0.1026 0.1177 -0.0672 0 -0.0958 0 0 0 0 0 s.e. 0.0526 0.0580 0.0583 0.0536 0 0.0491 0 0 0 0 0 sigma^2 estimated as 110.0: log likelihood = -1349.58, aic = 2711.15 [[3]][[9]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.4667 -0.0958 0.0884 0 0 -0.1011 0 0 0 0 0 s.e. 0.0526 0.0578 0.0535 0 0 0.0491 0 0 0 0 0 sigma^2 estimated as 110.5: log likelihood = -1350.36, aic = 2710.72 [[3]][[10]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.4609 -0.0559 0 0 0 -0.0940 0 0 0 0 0 s.e. 0.0527 0.0528 0 0 0 0.0491 0 0 0 0 0 sigma^2 estimated as 111.4: log likelihood = -1351.72, aic = 2711.44 [[3]][[11]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ar5 ar6 ar7 ar8 ar9 ar10 ar11 0.4366 0 0 0 0 -0.0948 0 0 0 0 0 s.e. 0.0475 0 0 0 0 0.0491 0 0 0 0 0 sigma^2 estimated as 111.7: log likelihood = -1352.28, aic = 2710.56 $aic [1] 2721.690 2719.691 2717.694 2715.706 2713.773 2712.269 2711.151 2710.716 [9] 2711.441 2710.563 2712.265 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 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/1b0n11260456425.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 = 359 Frequency = 1 [1] 0.25499984 22.50447500 8.56666663 30.52244252 17.75740298 [6] 4.29599552 -11.69675641 -3.23382518 -1.35822034 0.36328992 [11] -5.57531774 1.59376998 -3.69385227 -1.86624221 -1.64456946 [16] -11.45269301 -1.26091474 -6.74499561 -2.17643243 -12.88383615 [21] -2.14168358 -1.87046308 0.50896096 -1.92237013 -4.15707308 [26] -3.63529234 -6.00640712 -0.86117436 -0.45734759 5.38727106 [31] 19.54051310 16.60692515 -1.47618585 -0.14668427 1.98071964 [36] 0.56833561 -10.68753397 13.31677669 -8.42091884 -1.77953733 [41] 4.72142497 -2.98352756 6.77597977 -5.58140968 -10.31025103 [46] -1.60024022 -11.03467621 -3.99346349 1.63243972 -14.45321958 [51] 0.26759353 -0.45821907 -2.90413962 1.30602814 -4.19305875 [56] -1.59455924 -0.72289670 0.57486785 -3.62110455 10.50128273 [61] -6.05139007 12.03413474 -3.96214318 -0.14229994 0.06567199 [66] -1.16508461 -2.28482519 3.64675039 -1.86325285 -1.66037125 [71] 0.15149580 0.72226769 1.20679920 0.98108170 -0.63122109 [76] -4.40535196 1.30459048 -0.57797478 -0.78958977 2.21921199 [81] -2.83588526 1.57176093 1.92551667 -3.32359205 5.23720003 [86] 3.72132029 -4.80965834 2.05523930 1.09095577 2.72390820 [91] 1.28914647 -1.22635407 1.39703513 0.42726718 -1.36966753 [96] 1.81201869 -0.20904423 1.93364022 -0.34988734 -0.88930567 [101] -0.55502888 0.94405045 0.08171827 -5.14136968 1.10804988 [106] 1.54803641 -1.65605955 -7.27238035 23.73363491 74.54480338 [111] -16.34143409 -15.60161498 -1.03564811 -2.23684000 -3.97856342 [116] -2.35939358 -11.77247979 6.21200603 3.97243913 -2.47498744 [121] -3.53009959 -7.82644895 -4.00922238 -7.11640407 -5.35502461 [126] 2.92144212 -2.72634651 -9.13922709 7.60378738 3.80628551 [131] -15.72304302 2.01552196 11.16894769 -5.02884786 -6.26125029 [136] 11.14438046 -6.29913482 -1.00806478 7.81232196 -6.97825356 [141] 2.16102497 1.83705042 2.87451538 1.54251567 3.04709859 [146] 6.68976387 -5.05643875 8.30973202 1.83287650 -1.38266567 [151] 5.99509337 -7.09783090 -5.06579689 -2.09260916 -10.42558076 [156] 9.33152661 -2.93447763 5.16120865 -10.18754228 2.06177154 [161] 4.28214240 1.90853256 5.80780314 -5.60650019 0.27720372 [166] 0.53043288 -8.21584343 3.68050818 1.30458292 2.16941548 [171] -8.85402494 6.40893850 -6.61548767 0.62661571 -5.49454763 [176] -5.19048371 -0.39379349 3.08140212 -4.33778741 -3.48258016 [181] 5.81722657 -0.64510029 -6.87648590 0.72113129 5.31663163 [186] -6.05054308 4.63561074 -4.88912199 -6.43776067 -1.36223708 [191] -7.68042596 1.37749209 0.23468048 10.15415740 -4.08611219 [196] 0.63683798 2.08767804 0.02875605 0.55676981 -4.50684839 [201] 1.49030430 -7.35220606 5.55595382 4.09430516 3.09035125 [206] -2.73785577 -1.42270343 -0.02824866 2.02698308 5.91608792 [211] 72.02364859 81.45385480 -52.05729789 0.16991958 3.72937016 [216] -10.79976510 13.00925084 -6.30081800 -5.64891852 3.02485988 [221] -0.34888872 -1.00583891 -2.69906974 2.24228833 -7.74222828 [226] -10.92642019 0.45782190 -4.04936609 -15.09666551 9.16737982 [231] -5.08893074 -1.79562196 -7.77938240 2.43571872 -7.19705632 [236] 3.65530766 -6.51576218 3.37037247 -7.36705715 5.00615731 [241] -2.29759045 2.00039410 18.36114029 29.74763579 4.24499770 [246] 22.74140480 6.00726578 -4.47958975 -5.13183647 -16.60852222 [251] 2.96087102 -7.29047540 -5.50480524 1.12594177 7.77717167 [256] -12.15237357 -5.71314753 -7.00999141 1.22710820 -6.53811314 [261] -4.90622672 -8.73438455 2.23991510 -3.55537378 0.32942894 [266] -0.85493805 4.30927711 -4.20466087 3.13748413 -9.00731289 [271] 3.02628997 0.77729657 -3.39421231 10.33317244 -18.11049574 [276] 6.62865257 18.85395657 5.35016492 -10.71271831 -2.13540362 [281] -4.53543439 -7.59141397 7.69526844 0.44391042 -10.82980590 [286] 1.91939792 -6.94199869 -0.81742718 2.69554625 -1.24283609 [291] -1.88980222 -7.38468314 -0.43997897 6.95946772 -8.36196481 [296] -2.59063535 0.01271404 -0.81317426 -2.55731555 -5.76196054 [301] 5.01490785 -0.48757433 -9.33564279 15.40330244 -1.86375164 [306] -9.53896972 7.30495254 -6.40779782 0.47074740 -3.44159298 [311] 3.47095984 -5.52860113 18.39059799 -14.98371020 4.09838622 [316] 6.64257445 -10.27942629 2.08248199 3.08821292 -3.57853010 [321] 0.88941567 -8.60168971 -1.16938118 11.55131212 -2.36776965 [326] -4.56019715 9.15266853 -6.60899930 -7.08832622 -3.82838441 [331] 16.49732350 -5.92929116 0.32047757 -3.45289385 -5.44564176 [336] 1.73881606 28.60074860 -22.81818252 11.62062187 19.76539320 [341] -1.77237399 4.79562525 -9.41007086 17.34650388 -9.30702145 [346] 3.71784472 -14.55628285 1.74783453 1.45471070 -4.26599570 [351] 14.90026920 -6.44435597 4.63299205 -22.13824988 7.90992222 [356] 3.82478724 -2.92961061 -2.58122375 4.06833029 > postscript(file="/var/www/html/rcomp/tmp/20kb31260456425.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/3pvks1260456425.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/4pgnv1260456425.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/58ggz1260456425.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/6twqw1260456425.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/7cug51260456425.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/8ajli1260456425.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/98itw1260456425.tab") > > system("convert tmp/1b0n11260456425.ps tmp/1b0n11260456425.png") > system("convert tmp/20kb31260456425.ps tmp/20kb31260456425.png") > system("convert tmp/3pvks1260456425.ps tmp/3pvks1260456425.png") > system("convert tmp/4pgnv1260456425.ps tmp/4pgnv1260456425.png") > system("convert tmp/58ggz1260456425.ps tmp/58ggz1260456425.png") > system("convert tmp/6twqw1260456425.ps tmp/6twqw1260456425.png") > system("convert tmp/7cug51260456425.ps tmp/7cug51260456425.png") > > > proc.time() user system elapsed 4.299 1.157 5.119