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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 = '0.5' > par1 = 'FALSE' > par9 <- '1' > par8 <- '2' > par7 <- '1' > par6 <- '3' > par5 <- '4' > par4 <- '0' > 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,] 0.1299473 -0.2456688 0.09268410 -0.15759948 -0.4798766 0.08897108 [2,] 0.0000000 -0.2493889 0.05758288 -0.02798885 -0.4844103 0.09219409 [3,] 0.0000000 -0.2495813 0.05557122 0.00000000 -0.5478942 0.08325639 [4,] 0.0000000 -0.2502174 0.00000000 0.00000000 -0.6082470 0.08302483 [5,] 0.0000000 -0.2643064 0.00000000 0.00000000 -0.8188486 0.00000000 [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,] 0.4523209 [2,] 0.4644572 [3,] 0.5297284 [4,] 0.6007481 [5,] 0.7651163 [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,] 0.93164 0.01524 0.82219 0.91724 0.58648 0.50736 0.61416 [2,] NA 0.00613 0.52757 0.76676 0.56973 0.44921 0.59018 [3,] NA 0.00609 0.53492 NA 0.42337 0.46148 0.44152 [4,] NA 0.00598 NA NA 0.22070 0.42893 0.22343 [5,] NA 0.00295 NA NA 0.00292 NA 0.01030 [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 0.1299 -0.2457 0.0927 -0.1576 -0.4799 0.0890 0.4523 s.e. 1.5118 0.0998 0.4115 1.5135 0.8799 0.1338 0.8949 sigma^2 estimated as 695.2: log likelihood = -605.28, aic = 1226.55 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.1299 -0.2457 0.0927 -0.1576 -0.4799 0.0890 0.4523 s.e. 1.5118 0.0998 0.4115 1.5135 0.8799 0.1338 0.8949 sigma^2 estimated as 695.2: log likelihood = -605.28, aic = 1226.55 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 -0.2494 0.0576 -0.0280 -0.4844 0.0922 0.4645 s.e. 0 0.0894 0.0909 0.0942 0.8499 0.1214 0.8601 sigma^2 estimated as 695.2: log likelihood = -605.28, aic = 1224.56 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 -0.2496 0.0556 0 -0.5479 0.0833 0.5297 s.e. 0 0.0894 0.0893 0 0.6821 0.1127 0.6861 sigma^2 estimated as 695.7: log likelihood = -605.32, aic = 1222.65 [[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 -0.2502 0 0 -0.6082 0.0830 0.6007 s.e. 0 0.0895 0 0 0.4942 0.1046 0.4910 sigma^2 estimated as 697.8: log likelihood = -605.52, aic = 1221.03 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 1226.551 1224.556 1222.646 1221.034 1219.616 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 > postscript(file="/var/wessaorg/rcomp/tmp/1g5qi1354620278.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] 0.42239887 -46.57553647 -29.10677722 -24.24916587 -26.49376940 [6] 29.28577816 -37.21744946 5.29695787 -23.95549380 -1.35883300 [11] 8.21970276 9.99108472 10.91812975 -35.55793769 15.14454195 [16] -12.12845757 2.00337810 -8.50123831 48.11946072 -13.13951075 [21] -36.72976727 -16.70502991 -8.47532585 -3.79500013 4.97690367 [26] 21.56999826 -13.52233953 5.96848022 -12.41876397 11.83777811 [31] -4.58702803 28.00385717 6.63953626 41.91810904 7.10823370 [36] -7.43709103 -0.84433964 44.79270560 -7.86995593 12.84296942 [41] 18.61255775 4.10503558 2.90767888 47.57059276 -46.32688306 [46] 1.26897257 9.30235598 -15.98384084 4.53565788 46.94392409 [51] 55.42199400 25.57195734 -38.25582980 -46.87069031 -50.81795266 [56] 7.86882870 -19.79558913 -26.20524117 -22.94246756 -4.66199104 [61] -10.45164037 -13.31222412 14.70474113 15.55491241 -10.08673030 [66] -14.85537100 -18.38063518 -7.99045442 5.44981801 -4.43227491 [71] 18.58922676 21.37126157 -6.70096707 -43.69001405 -6.33125489 [76] -9.89848305 -0.26218870 21.09578986 -6.57111466 -4.81918549 [81] -0.33817240 -0.01761662 5.73853745 17.84103145 33.83445412 [86] 3.36403159 31.16319127 -21.08093535 -1.47500852 57.92512248 [91] -1.94644964 -12.24430619 3.89370949 63.32157929 -31.66802711 [96] 16.50554683 33.89671480 76.12642260 -50.10421701 -8.86215444 [101] 60.91788674 -64.98911083 23.69417250 -39.74313110 -24.98343900 [106] -14.51990262 -26.78907385 45.51601264 -29.30054277 -15.67528719 [111] -25.31706865 -23.33451845 -12.85519278 4.08058661 10.59461846 [116] 10.39451526 -30.99297100 -0.56399109 -21.01987126 -1.63170341 [121] 1.51621259 -5.66177733 5.60937784 50.42925831 -3.62920275 [126] -67.43181067 0.08776294 -13.95730503 -5.23796585 17.87435096 > postscript(file="/var/wessaorg/rcomp/tmp/2ebzz1354620278.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/3wjlu1354620278.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/48ce91354620278.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/5ud7q1354620278.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/6vet01354620278.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/72ihg1354620278.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/8iyu21354620278.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/90re11354620278.tab") > > try(system("convert tmp/1g5qi1354620278.ps tmp/1g5qi1354620278.png",intern=TRUE)) character(0) > try(system("convert tmp/2ebzz1354620278.ps tmp/2ebzz1354620278.png",intern=TRUE)) character(0) > try(system("convert tmp/3wjlu1354620278.ps tmp/3wjlu1354620278.png",intern=TRUE)) character(0) > try(system("convert tmp/48ce91354620278.ps tmp/48ce91354620278.png",intern=TRUE)) character(0) > try(system("convert tmp/5ud7q1354620278.ps tmp/5ud7q1354620278.png",intern=TRUE)) character(0) > try(system("convert tmp/6vet01354620278.ps tmp/6vet01354620278.png",intern=TRUE)) character(0) > try(system("convert tmp/72ihg1354620278.ps tmp/72ihg1354620278.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 4.662 0.666 5.334