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Type 'q()' to quit R. > x <- c(178421,139871,118159,109763,97415,119190,97903,96953,87888,84637,90549,95680,99371,79984,86752,85733,84906,78356,108895,101768,73285,65724,67457,67203,69273,80807,75129,74991,68157,73858,71349,85634,91624,116014,120033,108651,105378,138939,132974,135277,152741,158417,157460,193997,154089,147570,162924,153629,155907,197675,250708,266652,209842,165826,137152,150581,145973,126532,115437,119526,110856,97243,103876,116370,109616,98365,90440,88899,92358,88394,98219,113546,107168,77540,74944,75641,75910,87384,84615,80420,80784,79933,82118,91420,112426,114528,131025,116460,111258,155318,155078,134794,139985,198778,172436,169585,203702,282392,220658,194472,269246,215340,218319,195724,174614,172085,152347,189615,173804,145683,133550,121156,112040,120767,127019,136295,113425,107815,100298,97048,98750,98235,101254,139589,134921,80355,80396,82183,79709,90781) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '4' > par4 = '0' > par3 = '1' > par2 = '1' > par1 = 'FALSE' > par9 <- '1' > par8 <- '2' > par7 <- '1' > par6 <- '3' > par5 <- '4' > par4 <- '0' > par3 <- '1' > par2 <- '1' > 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,] 0.66199421 -0.2386292 0.28434855 -0.7161378 -0.5609511 0.01761888 [2,] 0.66669372 -0.2414236 0.28747279 -0.7188545 -0.6516989 0.00000000 [3,] 0.68005696 -0.2570784 0.31093124 -0.7237502 -0.1377829 0.00000000 [4,] -0.15943089 -0.2653187 0.02686461 0.1134783 0.0000000 0.00000000 [5,] -0.26162846 -0.2715526 0.00000000 0.2172593 0.0000000 0.00000000 [6,] -0.06051681 -0.2629910 0.00000000 0.0000000 0.0000000 0.00000000 [7,] 0.00000000 -0.2594511 0.00000000 0.0000000 0.0000000 0.00000000 [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,] 0.4514047 [2,] 0.5393394 [3,] 0.0000000 [4,] 0.0000000 [5,] 0.0000000 [6,] 0.0000000 [7,] 0.0000000 [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.02066 0.03495 0.02375 0.01081 0.64881 0.93743 0.71268 [2,] 0.01315 0.02289 0.01196 0.00726 0.15010 NA 0.29955 [3,] 0.00218 0.01783 0.00703 0.00097 0.24664 NA NA [4,] 0.93928 0.09159 0.96218 0.95677 NA NA NA [5,] 0.42131 0.00188 NA 0.52239 NA NA NA [6,] 0.48255 0.00262 NA NA NA NA NA [7,] NA 0.00298 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 0.6620 -0.2386 0.2843 -0.7161 -0.5610 0.0176 0.4514 s.e. 0.2823 0.1119 0.1242 0.2767 1.2287 0.2240 1.2229 sigma^2 estimated as 444501468: log likelihood = -1467.54, aic = 2951.07 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.6620 -0.2386 0.2843 -0.7161 -0.5610 0.0176 0.4514 s.e. 0.2823 0.1119 0.1242 0.2767 1.2287 0.2240 1.2229 sigma^2 estimated as 444501468: log likelihood = -1467.54, aic = 2951.07 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.6667 -0.2414 0.2875 -0.7189 -0.6517 0 0.5393 s.e. 0.2650 0.1048 0.1127 0.2633 0.4500 0 0.5177 sigma^2 estimated as 444491137: log likelihood = -1467.54, aic = 2949.08 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.6801 -0.2571 0.3109 -0.7238 -0.1378 0 0 s.e. 0.2173 0.1071 0.1134 0.2141 0.1184 0 0 sigma^2 estimated as 447125979: log likelihood = -1467.89, aic = 2947.78 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.1594 -0.2653 0.0269 0.1135 0 0 0 s.e. 2.0887 0.1561 0.5655 2.0893 0 0 0 sigma^2 estimated as 451267825: log likelihood = -1468.45, aic = 2946.9 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2616 -0.2716 0 0.2173 0 0 0 s.e. 0.3243 0.0855 0 0.3387 0 0 0 sigma^2 estimated as 451255832: log likelihood = -1468.45, aic = 2944.89 [[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 ma1 sar1 sar2 sma1 -0.0605 -0.2630 0 0 0 0 0 s.e. 0.0859 0.0857 0 0 0 0 0 sigma^2 estimated as 452735693: log likelihood = -1468.65, aic = 2943.31 $aic [1] 2951.075 2949.075 2947.784 2946.897 2944.894 2943.307 2941.802 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 > postscript(file="/var/wessaorg/rcomp/tmp/1hps51355500149.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 = 130 Frequency = 1 [1] 178.4209 -37150.2452 -22729.7721 -19848.2434 -18566.1594 18819.6661 [7] -23216.6592 3488.4074 -14720.7801 -4049.4263 3331.2466 4633.7917 [13] 5556.3144 -17814.2257 6565.4604 -5708.0285 891.2564 -6868.0352 [19] 29925.1214 -7001.4681 -20882.8216 -11159.0370 -6215.3398 -2137.5992 [25] 2510.3921 11592.4701 -4435.6078 2551.7236 -8335.6141 5251.1354 [31] -3961.2741 15632.4749 6194.6382 28509.3219 7070.3210 -4724.4328 [37] -2904.8415 30369.5651 -4794.7649 10768.2577 16034.6290 7338.5338 [43] 3979.3680 37971.8222 -37948.5797 674.7968 4464.0467 -10080.2632 [49] 5753.4599 39461.3561 56159.7595 30137.9954 -41897.9191 -43260.8316 [55] -46278.2257 117.9298 -11336.3233 -16188.1555 -13483.3697 -1695.2417 [61] -11340.4318 -13062.3106 3529.0528 9315.3117 -4253.4838 -8373.9212 [67] -10382.1157 -4979.5073 1281.5400 -4159.9415 10494.7972 14879.0814 [73] -2866.5724 -25983.1134 -6066.3485 -7251.9985 -371.5444 11673.5837 [79] -2003.8856 -1345.0125 -618.0900 -1932.2191 2229.2289 9210.4239 [85] 22143.5627 5819.5582 22148.5950 -13013.8472 -1744.8650 39914.7279 [91] 1058.2915 -8711.1412 3900.3592 53772.6336 -21418.8491 11016.8952 [97] 27016.7580 80004.8647 -47999.4689 -9227.1840 56953.8214 -56267.5981 [103] 19381.6689 -36591.5125 -21693.9271 -9748.7911 -25442.7867 35408.4150 [109] -18746.5757 -19276.6832 -17992.9436 -20523.8199 -13056.9149 4915.8185 [115] 4382.7044 11949.4734 -20664.4264 -4554.5150 -13871.1031 -5180.2843 [121] -471.5829 -1266.7211 3435.4445 38382.2599 -1554.1184 -44766.7330 [127] -4488.8021 -12560.8849 -2355.0738 11392.2463 > postscript(file="/var/wessaorg/rcomp/tmp/2jsqj1355500149.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/3dsph1355500149.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/44wy81355500149.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/5ckf31355500149.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/6t6b31355500149.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/7ih6w1355500149.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/84pqa1355500149.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/9rpou1355500149.tab") > > try(system("convert tmp/1hps51355500149.ps tmp/1hps51355500149.png",intern=TRUE)) character(0) > try(system("convert tmp/2jsqj1355500149.ps tmp/2jsqj1355500149.png",intern=TRUE)) character(0) > try(system("convert tmp/3dsph1355500149.ps tmp/3dsph1355500149.png",intern=TRUE)) character(0) > try(system("convert tmp/44wy81355500149.ps tmp/44wy81355500149.png",intern=TRUE)) character(0) > try(system("convert tmp/5ckf31355500149.ps tmp/5ckf31355500149.png",intern=TRUE)) character(0) > try(system("convert tmp/6t6b31355500149.ps tmp/6t6b31355500149.png",intern=TRUE)) character(0) > try(system("convert tmp/7ih6w1355500149.ps tmp/7ih6w1355500149.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 12.886 2.118 15.112