R version 2.8.0 (2008-10-20) Copyright (C) 2008 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(235.1 + ,280.7 + ,264.6 + ,240.7 + ,201.4 + ,240.8 + ,241.1 + ,223.8 + ,206.1 + ,174.7 + ,203.3 + ,220.5 + ,299.5 + ,347.4 + ,338.3 + ,327.7 + ,351.6 + ,396.6 + ,438.8 + ,395.6 + ,363.5 + ,378.8 + ,357 + ,369 + ,464.8 + ,479.1 + ,431.3 + ,366.5 + ,326.3 + ,355.1 + ,331.6 + ,261.3 + ,249 + ,205.5 + ,235.6 + ,240.9 + ,264.9 + ,253.8 + ,232.3 + ,193.8 + ,177 + ,213.2 + ,207.2 + ,180.6 + ,188.6 + ,175.4 + ,199 + ,179.6 + ,225.8 + ,234 + ,200.2 + ,183.6 + ,178.2 + ,203.2 + ,208.5 + ,191.8 + ,172.8 + ,148 + ,159.4 + ,154.5 + ,213.2 + ,196.4 + ,182.8 + ,176.4 + ,153.6 + ,173.2 + ,171 + ,151.2 + ,161.9 + ,157.2 + ,201.7 + ,236.4 + ,356.1 + ,398.3 + ,403.7 + ,384.6 + ,365.8 + ,368.1 + ,367.9 + ,347 + ,343.3 + ,292.9 + ,311.5 + ,300.9 + ,366.9 + ,356.9 + ,329.7 + ,316.2 + ,269 + ,289.3 + ,266.2 + ,253.6 + ,233.8 + ,228.4 + ,253.6 + ,260.1 + ,306.6 + ,309.2 + ,309.5 + ,271 + ,279.9 + ,317.9 + ,298.4 + ,246.7 + ,227.3 + ,209.1 + ,259.9 + ,266 + ,320.6 + ,308.5 + ,282.2 + ,262.7 + ,263.5 + ,313.1 + ,284.3 + ,252.6 + ,250.3 + ,246.5 + ,312.7 + ,333.2 + ,446.4 + ,511.6 + ,515.5 + ,506.4 + ,483.2 + ,522.3 + ,509.8 + ,460.7 + ,405.8 + ,375 + ,378.5 + ,406.8 + ,467.8 + ,469.8 + ,429.8 + ,355.8 + ,332.7 + ,378 + ,360.5 + ,334.7 + ,319.5 + ,323.1 + ,363.6 + ,352.1 + ,411.9 + ,388.6 + ,416.4 + ,360.7 + ,338 + ,417.2 + ,388.4 + ,371.1 + ,331.5 + ,353.7 + ,396.7 + ,447 + ,533.5 + ,565.4 + ,542.3 + ,488.7 + ,467.1 + ,531.3 + ,496.1 + ,444 + ,403.4 + ,386.3 + ,394.1 + ,404.1 + ,462.1 + ,448.1 + ,432.3 + ,386.3 + ,395.2 + ,421.9 + ,382.9 + ,384.2 + ,345.5 + ,323.4 + ,372.6 + ,376 + ,462.7 + ,487 + ,444.2 + ,399.3 + ,394.9 + ,455.4 + ,414 + ,375.5 + ,347 + ,339.4 + ,385.8 + ,378.8 + ,451.8 + ,446.1 + ,422.5 + ,383.1 + ,352.8 + ,445.3 + ,367.5 + ,355.1 + ,326.2 + ,319.8 + ,331.8 + ,340.9 + ,394.1 + ,417.2 + ,369.9 + ,349.2 + ,321.4 + ,405.7 + ,342.9 + ,316.5 + ,284.2 + ,270.9 + ,288.8 + ,278.8 + ,324.4 + ,310.9 + ,299 + ,273 + ,279.3 + ,359.2 + ,305 + ,282.1 + ,250.3 + ,246.5 + ,257.9 + ,266.5 + ,315.9 + ,318.4 + ,295.4 + ,266.4 + ,245.8 + ,362.8 + ,324.9 + ,294.2 + ,289.5 + ,295.2 + ,290.3 + ,272 + ,307.4 + ,328.7 + ,292.9 + ,249.1 + ,230.4 + ,361.5 + ,321.7 + ,277.2 + ,260.7 + ,251 + ,257.6 + ,241.8 + ,287.5 + ,292.3 + ,274.7 + ,254.2 + ,230 + ,339 + ,318.2 + ,287 + ,295.8 + ,284 + ,271 + ,262.7 + ,340.6 + ,379.4 + ,373.3 + ,355.2 + ,338.4 + ,466.9 + ,451 + ,422 + ,429.2 + ,425.9 + ,460.7 + ,463.6 + ,541.4 + ,544.2 + ,517.5 + ,469.4 + ,439.4 + ,549 + ,533 + ,506.1 + ,484 + ,457 + ,481.5 + ,469.5 + ,544.7 + ,541.2 + ,521.5 + ,469.7 + ,434.4 + ,542.6 + ,517.3 + ,485.7 + ,465.8 + ,447 + ,426.6 + ,411.6 + ,467.5 + ,484.5 + ,451.2 + ,417.4 + ,379.9 + ,484.7 + ,455 + ,420.8 + ,416.5 + ,376.3 + ,405.6 + ,405.8 + ,500.8 + ,514 + ,475.5 + ,430.1 + ,414.4 + ,538 + ,526 + ,488.5 + ,520.2 + ,504.4 + ,568.5 + ,610.6 + ,818 + ,830.9 + ,835.9 + ,782 + ,762.3 + ,856.9 + ,820.9 + ,769.6 + ,752.2 + ,724.4 + ,723.1 + ,719.5 + ,817.4 + ,803.3 + ,752.5 + ,689 + ,630.4 + ,765.5 + ,757.7 + ,732.2 + ,702.6 + ,683.3 + ,709.5 + ,702.2 + ,784.8 + ,810.9 + ,755.6 + ,656.8 + ,615.1 + ,745.3 + ,694.1 + ,675.7 + ,643.7 + ,622.1 + ,634.6 + ,588 + ,689.7 + ,673.9 + ,647.9 + ,568.8 + ,545.7 + ,632.6 + ,643.8 + ,593.1 + ,579.7 + ,546 + ,562.9 + ,572.5) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '0.5' > par1 = 'FALSE' > #'GNU S' R Code compiled by R2WASP v. 1.0.44 () > #Author: Prof. Dr. P. Wessa > #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > if (par1 == 'TRUE') par1 <- TRUE > if (par1 == 'FALSE') par1 <- FALSE > par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter > par3 <- as.numeric(par3) #degree of non-seasonal differencing > par4 <- as.numeric(par4) #degree of seasonal differencing > par5 <- as.numeric(par5) #seasonal period > par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial > par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial > par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial > par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial > armaGR <- function(arima.out, names, n){ + try1 <- arima.out$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,] 0.4606888 0.1800558 -0.006175732 -0.3736983 -0.09674542 -0.06207538 [2,] 0.4866396 0.1754143 0.000000000 -0.3973188 -0.10049067 -0.06163942 [3,] 0.4706225 0.1835941 0.000000000 -0.3841762 -0.04618892 0.00000000 [4,] 0.4617445 0.1881821 0.000000000 -0.3766777 0.00000000 0.00000000 [5,] NA NA NA NA NA NA [6,] NA NA NA NA NA NA [7,] NA NA NA NA NA NA [8,] NA NA NA NA NA NA [9,] NA NA NA NA NA NA [10,] NA NA NA NA NA NA [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 [,7] [1,] -0.6433188 [2,] -0.6417182 [3,] -0.6957637 [4,] -0.7209464 [5,] NA [6,] NA [7,] NA [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.00000 0.00000 0.14697 0.00000 0.00005 0.00428 0 [2,] 0.00537 0.01032 NA 0.02229 0.37319 0.49414 0 [3,] 0.00741 0.00623 NA 0.02926 0.55333 NA 0 [4,] 0.00778 0.00444 NA 0.03073 NA NA 0 [5,] NA NA NA NA NA NA NA [6,] NA NA NA NA NA NA NA [7,] NA NA NA NA NA NA NA [8,] NA NA NA NA NA NA NA [9,] NA NA NA NA NA NA NA [10,] NA NA NA NA NA NA NA [11,] NA NA NA NA NA NA NA [12,] NA NA NA NA NA NA NA [13,] NA NA NA NA NA NA NA [14,] NA NA NA NA NA NA NA [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4607 0.1801 -0.0062 -0.3737 -0.0967 -0.0621 -0.6433 s.e. 0.0031 0.0060 0.0042 0.0110 0.0235 0.0216 0.0073 sigma^2 estimated as 0.2823: log likelihood = -286.32, aic = 588.64 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4607 0.1801 -0.0062 -0.3737 -0.0967 -0.0621 -0.6433 s.e. 0.0031 0.0060 0.0042 0.0110 0.0235 0.0216 0.0073 sigma^2 estimated as 0.2823: log likelihood = -286.32, aic = 588.64 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4866 0.1754 0 -0.3973 -0.1005 -0.0616 -0.6417 s.e. 0.1738 0.0680 0 0.1731 0.1127 0.0901 0.1035 sigma^2 estimated as 0.2821: log likelihood = -286.61, aic = 587.22 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4706 0.1836 0 -0.3842 -0.0462 0 -0.6958 s.e. 0.1748 0.0667 0 0.1755 0.0778 0 0.0604 sigma^2 estimated as 0.2824: log likelihood = -286.83, aic = 585.67 [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 588.6409 587.2173 585.6698 584.0182 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 > postscript(file="/var/www/html/rcomp/tmp/1do3v1229363422.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 = 372 Frequency = 1 [1] 8.852492e-03 5.059755e-03 2.900304e-03 1.498531e-03 -1.256345e-06 [6] 1.218861e-03 1.052758e-03 3.875780e-04 -2.267326e-04 -1.287179e-03 [11] -1.752664e-04 -8.140182e-03 -4.471353e-02 -6.819174e-02 1.979183e-01 [16] 3.649775e-01 1.513380e+00 -3.594623e-01 4.572747e-01 -5.762008e-01 [21] -3.580616e-01 1.260009e+00 -1.342662e+00 -3.532895e-01 1.613960e-01 [26] -8.578228e-01 -5.551445e-01 -7.270273e-01 -3.690415e-01 -5.789650e-02 [31] -7.690363e-01 -8.537030e-01 7.008553e-01 -6.760584e-01 8.685783e-01 [36] -1.088458e-01 -1.578872e+00 -1.122689e+00 3.969248e-01 1.485014e-02 [41] 1.402596e-01 3.698653e-01 -2.797762e-01 3.045461e-01 8.924663e-01 [46] 8.937963e-02 1.352690e-01 -1.207368e+00 -2.303729e-01 -8.779680e-02 [51] -3.332327e-01 6.019666e-01 4.897904e-01 -3.004006e-01 1.013509e-01 [56] 5.963469e-01 -4.768391e-01 -4.183795e-01 -1.171129e-01 -1.471807e-01 [61] 5.322941e-01 -9.765284e-01 3.311799e-01 8.696196e-01 -4.525787e-01 [66] -4.213896e-01 -6.835989e-02 3.261028e-01 8.489494e-01 4.558275e-01 [71] 8.234728e-01 9.337355e-01 1.233438e+00 4.083388e-01 2.879750e-01 [76] -2.112042e-01 -2.674694e-01 -1.116455e+00 -3.172452e-02 5.479725e-01 [81] 1.208025e-01 -8.683217e-01 -3.111554e-01 -3.806887e-01 -3.056182e-01 [86] -4.099405e-01 -9.677472e-03 4.954267e-01 -7.043473e-01 -6.346972e-02 [91] -5.138985e-01 5.895118e-01 -3.494231e-01 6.413053e-01 3.436570e-02 [96] -5.275238e-02 -8.801382e-01 -1.106176e-01 7.250179e-01 -5.052103e-01 [101] 1.015062e+00 4.321653e-01 -6.059670e-01 -9.996577e-01 -2.738982e-01 [106] 2.760391e-01 1.003369e+00 1.299757e-02 -5.602275e-01 -5.497042e-01 [111] -2.647103e-01 2.796292e-01 6.786391e-01 6.610690e-01 -7.044508e-01 [116] -2.012606e-01 3.501255e-01 5.120740e-01 8.552067e-01 1.940690e-01 [121] 7.239285e-01 1.181310e+00 1.428806e-01 1.792502e-02 -4.979572e-01 [126] -2.997199e-01 1.357883e-01 -1.703313e-01 -1.033098e+00 -1.738189e-01 [131] -9.630236e-01 6.983804e-01 -3.670887e-01 -2.638482e-01 -4.188187e-01 [136] -1.120676e+00 5.005917e-02 6.089042e-01 1.346905e-01 3.300413e-01 [141] 1.278385e-01 5.813190e-01 -3.870831e-02 -8.700974e-01 -4.621137e-01 [146] -7.754562e-01 1.395870e+00 -3.732718e-01 -2.680803e-01 1.086562e+00 [151] -3.258802e-01 3.066183e-01 -5.527743e-01 9.287356e-01 9.305519e-02 [156] 7.937251e-01 -6.552009e-02 3.193893e-01 -4.662252e-01 -2.628730e-01 [161] 1.013779e-02 1.667096e-01 -2.794887e-01 -4.164377e-01 -2.211555e-01 [166] -1.802576e-01 -6.985120e-01 -3.096567e-02 -2.180269e-01 -3.735841e-01 [171] 5.295177e-02 1.627272e-01 8.277813e-01 -7.243261e-01 -4.700048e-01 [176] 1.045705e+00 -1.928635e-01 -5.711804e-01 5.312913e-01 -3.057183e-01 [181] 3.380500e-01 4.762421e-01 -8.138987e-01 -3.475256e-02 3.072755e-01 [186] 2.982654e-01 -3.562057e-01 -3.566042e-01 1.619145e-01 1.576222e-01 [191] 3.019288e-01 -5.619821e-01 -5.434435e-02 -2.426253e-01 -2.911870e-02 [196] 2.284708e-01 -5.106754e-01 1.123876e+00 -1.125988e+00 2.903238e-01 [201] 1.901863e-01 8.531189e-02 -7.080390e-01 8.212184e-02 -3.070607e-01 [206] 5.250535e-01 -6.016088e-01 5.381820e-01 -3.065121e-01 6.545421e-01 [211] -5.373751e-01 -1.869452e-01 -4.152848e-02 -8.826293e-02 -2.564922e-01 [216] -4.130590e-01 -2.701514e-01 -4.356153e-01 5.466497e-01 3.238847e-01 [221] 6.619319e-01 4.027741e-01 -4.281801e-01 -1.853639e-01 -1.462758e-01 [226] 1.779014e-01 -3.705034e-01 2.052387e-01 -8.941073e-02 -2.078028e-02 [231] -2.401475e-03 2.723730e-02 -3.040121e-01 1.507871e+00 2.590494e-01 [236] -5.463582e-01 5.814691e-01 3.302136e-01 -9.833670e-01 -7.362629e-01 [241] -3.387440e-01 7.605377e-01 -2.617095e-01 -4.760480e-01 -1.103675e-01 [246] 1.690810e+00 1.498716e-01 -8.944445e-01 8.547081e-02 -1.179159e-01 [251] -2.158929e-01 -3.940919e-01 9.242654e-02 5.859189e-02 2.532459e-01 [256] 3.705680e-01 -3.865241e-01 4.471109e-01 6.231542e-01 -1.842035e-01 [261] 7.054780e-01 -3.170311e-01 -9.274755e-01 -3.937568e-02 1.002748e+00 [266] 8.126191e-01 2.982929e-01 1.525505e-01 -1.506397e-01 1.107417e-01 [271] 5.527864e-01 4.349497e-02 3.632095e-01 -2.279736e-02 5.041384e-01 [276] 1.387790e-01 -8.530383e-02 -4.964851e-01 -8.522515e-02 -2.478309e-01 [281] -1.208491e-01 -4.517701e-01 6.121508e-01 3.506254e-01 -3.592907e-01 [286] -4.844366e-01 3.394311e-01 -4.179439e-02 -1.000691e-02 -4.039126e-01 [291] 1.518314e-01 -2.297009e-01 -2.173615e-01 -3.321888e-01 2.644183e-01 [296] 1.768695e-01 -1.766778e-01 -1.356704e-01 -8.279441e-01 -7.227308e-02 [301] -1.176075e-01 3.146353e-01 -1.543861e-01 1.630028e-01 -2.447392e-01 [306] -2.044815e-01 3.602610e-02 4.171809e-03 2.661119e-01 -6.510032e-01 [311] 5.899946e-01 3.123230e-01 5.526697e-01 -9.622907e-02 -4.670839e-01 [316] -1.981968e-01 3.955755e-01 1.757352e-01 3.156535e-01 -1.611493e-01 [321] 8.820408e-01 6.378449e-02 8.586335e-01 8.227611e-01 1.772668e+00 [326] -5.669823e-01 1.532608e-01 -2.792334e-01 4.957177e-02 -1.169733e+00 [331] -8.210915e-02 6.810248e-02 -2.015448e-01 7.643720e-02 -5.263600e-01 [336] -8.041370e-02 -3.906034e-01 -3.662013e-01 -2.363252e-01 -1.977452e-02 [341] -4.005929e-01 2.733056e-01 5.716710e-01 3.551069e-01 -5.980535e-01 [346] 6.801245e-02 8.251581e-02 -2.366889e-01 -7.209930e-01 4.458558e-01 [351] -2.781843e-01 -8.136956e-01 3.822276e-02 2.879819e-01 -3.974011e-01 [356] 4.534420e-01 -3.369936e-01 3.500176e-02 -1.290740e-01 -9.294872e-01 [361] 1.125387e-01 -2.662144e-01 3.189196e-01 -2.358237e-01 3.120562e-01 [366] -6.073489e-01 8.214397e-01 -3.307695e-01 -3.684718e-02 -2.234115e-01 [371] -1.623927e-02 4.943288e-01 > postscript(file="/var/www/html/rcomp/tmp/2ca421229363422.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/3cl0u1229363422.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/4p2qi1229363422.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/5zudg1229363422.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/6pk2u1229363422.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/7nbfw1229363422.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/8q2y81229363422.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/9x1tf1229363422.tab") > > system("convert tmp/1do3v1229363422.ps tmp/1do3v1229363422.png") > system("convert tmp/2ca421229363422.ps tmp/2ca421229363422.png") > system("convert tmp/3cl0u1229363422.ps tmp/3cl0u1229363422.png") > system("convert tmp/4p2qi1229363422.ps tmp/4p2qi1229363422.png") > system("convert tmp/5zudg1229363422.ps tmp/5zudg1229363422.png") > system("convert tmp/6pk2u1229363422.ps tmp/6pk2u1229363422.png") > system("convert tmp/7nbfw1229363422.ps tmp/7nbfw1229363422.png") > > > proc.time() user system elapsed 22.046 1.823 24.306