R version 3.0.2 (2013-09-25) -- "Frisbee Sailing" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) 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 = '0' > par6 = '0' > par5 = '12' > par4 = '1' > par3 = '2' > par2 = '1' > par1 = 'FALSE' > par9 <- '1' > par8 <- '2' > par7 <- '0' > par6 <- '0' > par5 <- '12' > par4 <- '1' > par3 <- '2' > par2 <- '1' > par1 <- 'FALSE' > #'GNU S' R Code compiled by R2WASP v. 1.2.327 () > #Author: root > #To cite this work: Wessa P., (2013), ARIMA Backward Selection (v1.0.5) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_arimabackwardselection.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > # > library(lattice) > if (par1 == 'TRUE') par1 <- TRUE > if (par1 == 'FALSE') par1 <- FALSE > par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter > par3 <- as.numeric(par3) #degree of non-seasonal differencing > par4 <- as.numeric(par4) #degree of seasonal differencing > par5 <- as.numeric(par5) #seasonal period > par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial > par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial > par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial > par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial > armaGR <- function(arima.out, names, n){ + try1 <- arima.out$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] [1,] -0.3452235 -0.26417453 -0.4035694 [2,] 0.0000000 -0.06817282 -0.6626342 [3,] 0.0000000 0.00000000 -0.6858295 [4,] NA NA NA [5,] NA NA NA [6,] NA NA NA [[2]] [,1] [,2] [,3] [1,] 0.00093 0.00093 6e-05 [2,] NA 0.31409 0e+00 [3,] NA NA 0e+00 [4,] NA NA NA [5,] NA NA NA [6,] NA NA NA [[3]] [[3]][[1]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: sar1 sar2 sma1 -0.3452 -0.2642 -0.4036 s.e. 0.1034 0.0792 0.0998 sigma^2 estimated as 783.6: log likelihood = -1708.11, aic = 3424.21 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: sar1 sar2 sma1 -0.3452 -0.2642 -0.4036 s.e. 0.1034 0.0792 0.0998 sigma^2 estimated as 783.6: log likelihood = -1708.11, aic = 3424.21 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: sar1 sar2 sma1 0 -0.0682 -0.6626 s.e. 0 0.0676 0.0431 sigma^2 estimated as 809.5: log likelihood = -1713.4, aic = 3432.8 $aic [1] 3424.211 3432.796 3431.788 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 > postscript(file="/var/wessaorg/rcomp/tmp/1q6gn1384799904.ps",horizontal=F,onefile=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] 0.07088531 -0.03173271 -0.08499708 -0.10022788 -0.11070503 [6] -0.05154355 -0.04724340 -0.05731700 -0.06266097 -0.07637719 [11] -0.14514317 -0.07677985 -0.17421454 0.55723170 3.90920325 [16] 5.24023780 41.50138041 -47.90150702 30.19182956 -56.38396846 [21] 9.56588442 50.81977389 -80.75005914 37.59297767 18.30476960 [26] -41.90978978 -2.83920661 -11.78270459 10.59728616 21.34720408 [31] -31.23580884 8.75469256 47.72475424 -48.23085447 63.56511303 [36] -36.09600463 -51.24931898 22.80768384 48.68680885 -6.54393107 [41] 6.74526897 -6.68326003 -6.27607425 26.06177855 6.29324044 [46] -14.77488534 -4.49216422 -35.96039230 16.58646118 7.33822352 [51] -0.70385327 28.55492243 -6.75529551 -23.30363413 14.89552594 [56] 17.70889916 -29.26563602 0.55516899 4.00986852 -0.35967598 [61] 4.14638822 -29.22975460 47.98309813 8.61582237 -32.02595260 [66] -4.29924419 8.27713286 17.65877849 12.00329539 -8.50559116 [71] 13.03595173 4.99474059 27.14858397 -21.18094892 -10.51752378 [76] -23.63238999 -5.10060182 -25.60764225 26.21984521 8.41625849 [81] -7.88800930 -35.76223602 28.27476429 -14.25307542 9.32454838 [86] -14.97184187 15.71234703 21.87407403 -39.20913567 30.25898988 [91] -23.68678309 37.02142257 -27.34706087 36.76753336 -18.80460298 [96] 1.52085064 -28.94582045 22.04343621 22.56202333 -40.16309017 [101] 56.25861994 -19.81407775 -28.44857824 -18.39799656 20.48236734 [106] 8.99810508 27.28979079 -26.30306198 -11.22631048 -8.10330475 [111] 4.37832020 21.59912621 7.84113431 9.73805326 -42.52096257 [116] 19.23767804 9.00034706 7.42330846 16.45295750 -17.70120125 [121] 34.26222988 15.51812746 -43.17956140 -9.06896534 -23.67426660 [126] 17.33192094 -2.33082316 -23.86391948 -26.54201515 29.61522199 [131] -22.18011868 56.99524425 -36.71861206 -2.27348766 -10.12040161 [136] -23.89917460 47.46465433 18.92586647 -14.16856510 14.48530392 [141] -1.38363612 14.13756343 -12.02725281 -39.10311427 17.28380396 [146] -24.33051397 83.18170579 -66.68436931 11.20505520 46.95988983 [151] -52.76020051 27.10014553 -36.21583426 54.11188855 -26.50262976 [156] 38.19550116 -27.73750023 11.62832383 -49.66582836 7.38177169 [161] 10.85586459 15.43239479 -27.12339738 -8.51348465 10.92053332 [166] -2.80555735 -13.28387459 14.97339372 -3.94798005 -11.33768973 [171] 26.63678349 1.81681438 28.87301519 -55.49006617 12.64579776 [176] 52.90835604 -46.50646235 -5.82759193 36.50001104 -34.03542981 [181] 30.29098260 4.03023050 -54.88267064 32.91698788 4.66399942 [186] 9.23291846 -27.23416365 -4.27733974 21.48754522 -2.17256351 [191] 7.16556733 -30.50059427 17.57618206 -14.81562522 16.45970609 [196] 8.13233359 -26.74830387 59.38224624 -84.14877892 63.56513522 [201] -15.77252978 -0.31000484 -27.68862024 27.01698184 -21.84692405 [206] 39.43710813 -46.04491513 49.70439989 -34.90600622 31.85960874 [211] -35.02766367 10.57073527 3.55722979 -3.41227553 -6.72680105 [216] -7.61574808 -3.20147780 -3.97885823 44.84317204 -8.69814283 [221] 14.12946636 -13.44153325 -14.87353845 6.16532850 -2.44943805 [226] 6.84798595 -22.88435618 23.49627085 -19.36880955 12.87667062 [231] -0.96445896 5.22684130 -15.64173285 54.36372979 -29.07410240 [236] -21.99165369 33.99954880 -13.30089526 -40.09133987 3.26510911 [241] 0.84955413 39.35550680 -27.33063991 -1.31115021 9.02126281 [246] 45.59860789 -34.37924490 -26.82010088 24.32474219 -11.80007143 [251] -0.75665494 -5.12528944 7.35398568 0.11001015 14.74230127 [256] 4.78338687 -24.45153328 17.97825467 16.90114365 -25.11487924 [261] 30.49763573 -36.41897143 -19.76024386 22.98117610 30.45243102 [266] 4.10896484 -14.88550860 -6.06671009 -10.06393500 24.84471383 [271] -4.49149001 -20.15323321 16.54512063 -14.27787643 28.04003615 [276] -21.98544305 9.41056882 -35.00429088 7.90195026 -13.07232794 [281] 8.16791575 9.63128727 18.62568803 -11.54280577 -19.61856406 [286] -5.72894381 30.78726141 -17.32082218 20.21963289 -26.77232772 [291] 17.00221378 -19.98254367 4.15320408 11.10691103 3.44669403 [296] -3.23272401 -6.35898743 2.89235158 -30.02011714 27.92750640 [301] -3.67213336 19.61233694 -21.78389094 17.71805944 -16.30264177 [306] 5.77123362 2.56554616 -0.19213317 11.84569124 -34.70166125 [311] 52.12116058 -16.30787517 22.30524463 -30.15643497 -14.92805184 [316] 4.61114233 22.48823613 1.09009054 0.06145328 -20.81371904 [321] 47.61975211 -34.18546931 41.31728496 -0.84971416 84.16900015 [326] -129.96940587 31.58346981 -46.79023855 18.02438260 -24.35947615 [331] 4.97254822 -3.46917020 -2.50478019 11.92514596 -21.17466821 [336] 16.78498818 -6.39741479 -6.54678219 -7.96715589 14.75888346 [341] -15.07945625 63.05406048 -9.08000977 -6.13041985 -36.98056121 [346] 27.81120442 5.67793877 -19.59366642 -11.03296199 43.47880199 [351] -46.99334028 -24.56524611 42.51461482 18.27745411 -44.17586058 [356] 47.13313711 -36.41108363 21.05786565 -11.27803223 -37.24042823 [361] 46.73975675 -26.56520307 38.09723021 -22.72818713 25.07417263 [366] -44.37581284 75.58903999 -63.63158794 24.18220506 -15.33516153 [371] 10.32207409 24.99622815 > postscript(file="/var/wessaorg/rcomp/tmp/2pj5y1384799904.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/3o4pq1384799904.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/4k2271384799904.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/5ai1j1384799904.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/6apy61384799904.ps",horizontal=F,onefile=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/wessaorg/rcomp/tmp/7nx5g1384799904.ps",horizontal=F,onefile=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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/8mvhw1384799904.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/wessaorg/rcomp/tmp/9yeqy1384799904.tab") > > try(system("convert tmp/1q6gn1384799904.ps tmp/1q6gn1384799904.png",intern=TRUE)) character(0) > try(system("convert tmp/2pj5y1384799904.ps tmp/2pj5y1384799904.png",intern=TRUE)) character(0) > try(system("convert tmp/3o4pq1384799904.ps tmp/3o4pq1384799904.png",intern=TRUE)) character(0) > try(system("convert tmp/4k2271384799904.ps tmp/4k2271384799904.png",intern=TRUE)) character(0) > try(system("convert tmp/5ai1j1384799904.ps tmp/5ai1j1384799904.png",intern=TRUE)) character(0) > try(system("convert tmp/6apy61384799904.ps tmp/6apy61384799904.png",intern=TRUE)) character(0) > try(system("convert tmp/7nx5g1384799904.ps tmp/7nx5g1384799904.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.096 1.117 8.232