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Type 'q()' to quit R. > x <- c(8715.1 + ,8919.9 + ,10085.8 + ,9511.7 + ,8991.3 + ,10311.2 + ,8895.4 + ,7449.8 + ,10084.0 + ,9859.4 + ,9100.1 + ,8920.8 + ,8502.7 + ,8599.6 + ,10394.4 + ,9290.4 + ,8742.2 + ,10217.3 + ,8639.0 + ,8139.6 + ,10779.1 + ,10427.7 + ,10349.1 + ,10036.4 + ,9492.1 + ,10638.8 + ,12054.5 + ,10324.7 + ,11817.3 + ,11008.9 + ,9996.6 + ,9419.5 + ,11958.8 + ,12594.6 + ,11890.6 + ,10871.7 + ,11835.7 + ,11542.2 + ,13093.7 + ,11180.2 + ,12035.7 + ,12112.0 + ,10875.2 + ,9897.3 + ,11672.1 + ,12385.7 + ,11405.6 + ,9830.9 + ,11025.1 + ,10853.8 + ,12252.6 + ,11839.4 + ,11669.1 + ,11601.4 + ,11178.4 + ,9516.4 + ,12102.8 + ,12989.0 + ,11610.2 + ,10205.5 + ,11356.2 + ,11307.1 + ,12648.6 + ,11947.2 + ,11714.1 + ,12192.5 + ,11268.8 + ,9097.4 + ,12639.8 + ,13040.1 + ,11687.3 + ,11191.7 + ,11391.9 + ,11793.1 + ,13933.2 + ,12778.1 + ,11810.3 + ,13698.4 + ,11956.6 + ,10723.8 + ,13938.9 + ,13979.8 + ,13807.4 + ,12973.9 + ,12509.8 + ,12934.1 + ,14908.3 + ,13772.1 + ,13012.6 + ,14049.9 + ,11816.5 + ,11593.2 + ,14466.2 + ,13615.9 + ,14733.9 + ,13880.7 + ,13527.5 + ,13584.0 + ,16170.2 + ,13260.6 + ,14741.9 + ,15486.5 + ,13154.5 + ,12621.2 + ,15031.6 + ,15452.4 + ,15428.0 + ,13105.9 + ,14716.8 + ,14180.0 + ,16202.2 + ,14392.4 + ,15140.6 + ,15960.1 + ,14351.3 + ,13230.2 + ,15202.1 + ,17056.0 + ,16077.7 + ,13348.2 + ,16402.4 + ,16559.1 + ,16579.0 + ,17561.2 + ,16129.6 + ,18484.3 + ,16402.6 + ,14032.3 + ,17109.1 + ,17157.2 + ,13879.8 + ,12362.4) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '0.0' > 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,] -0.5339483 -0.1558731 0.3029371 -0.03754438 0.3604869 -0.2872399 [2,] -0.5689873 -0.1806587 0.2879120 0.00000000 0.3610291 -0.2895573 [3,] -0.4738367 0.0000000 0.3859186 0.00000000 0.3631768 -0.3253847 [4,] NA NA NA NA NA NA [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,] -0.9998545 [2,] -0.9998574 [3,] -1.0000918 [4,] NA [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,] 0.09978 0.52548 0.06303 0.90999 0.00153 0.01292 0.00019 [2,] 0.00000 0.12043 0.00341 NA 0.00149 0.01090 0.00019 [3,] 0.00000 NA 0.00000 NA 0.00090 0.00227 0.00001 [4,] NA NA NA NA NA NA NA [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 -0.5339 -0.1559 0.3029 -0.0375 0.3605 -0.2872 -0.9999 s.e. 0.3220 0.2448 0.1615 0.3314 0.1112 0.1139 0.2600 sigma^2 estimated as 0.001806: log likelihood = 191.84, aic = -367.68 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5339 -0.1559 0.3029 -0.0375 0.3605 -0.2872 -0.9999 s.e. 0.3220 0.2448 0.1615 0.3314 0.1112 0.1139 0.2600 sigma^2 estimated as 0.001806: log likelihood = 191.84, aic = -367.68 [[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 -0.5690 -0.1807 0.2879 0 0.3610 -0.2896 -0.9999 s.e. 0.0941 0.1155 0.0964 0 0.1112 0.1120 0.2597 sigma^2 estimated as 0.001805: log likelihood = 191.83, aic = -369.66 [[3]][[4]] NULL [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -367.6763 -369.6632 -369.2208 Warning messages: 1: In log(s2) : NaNs produced 2: 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/1gzkl1260971249.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 = 132 Frequency = 1 [1] 0.0052381882 0.0023605816 0.0016491857 0.0011775411 0.0008884598 [6] 0.0008652389 0.0006034463 0.0003611104 0.0006072378 0.0005249270 [11] 0.0004005260 -0.0048004211 -0.0320280598 -0.0070784802 0.0411646450 [16] -0.0091070459 -0.0154314785 -0.0095899191 0.0046470912 0.0841207769 [21] 0.0282145126 0.0035351265 0.0202771900 0.0288801779 0.0013468433 [26] 0.0566048829 0.0025723010 -0.0460929082 0.0976068662 -0.0824573805 [31] -0.0062412450 -0.0310789537 0.0349851130 0.0321657665 -0.0069536656 [36] -0.0367026921 0.0572754492 -0.0411094682 -0.0187654430 -0.0699886737 [41] 0.0006144706 0.0088630973 0.0200816037 0.0158418005 -0.0801576894 [46] -0.0199577419 -0.0162420972 -0.0418435393 0.0087418660 0.0251132235 [51] 0.0137426547 0.0563422256 0.0222477236 -0.0674786944 0.0159726888 [56] -0.0023240692 0.0340272139 0.0267924677 0.0017258829 -0.0458413225 [61] 0.0248405961 0.0142111287 -0.0148745711 -0.0311327443 -0.0019842205 [66] 0.0069476532 0.0056713793 -0.0638229031 0.0125941081 0.0164765719 [71] 0.0024439395 0.0049192734 -0.0072786622 0.0139333139 0.0302333664 [76] 0.0439007305 -0.0707851570 0.0311428839 0.0133126862 0.0686757523 [81] -0.0320137665 -0.0131360243 0.0364840043 0.0279824822 -0.0343614695 [86] -0.0422416964 -0.0164150655 0.0273297677 -0.0203537917 -0.0273720101 [91] -0.0627887062 0.0500640709 0.0213625185 -0.0531474402 0.0419382669 [96] 0.0728202374 0.0197344785 -0.0597064358 0.0215311780 -0.0722424698 [101] 0.0561543160 0.0400843732 0.0259656689 -0.0076428596 -0.0491863520 [106] 0.0170320969 0.0090773117 -0.0537839002 0.0312732970 -0.0124694002 [111] -0.0038570502 -0.0164576914 0.0137765711 0.0083191635 0.0070276653 [116] 0.0386268486 -0.0669046251 0.0212746119 0.0225536233 -0.0199364729 [121] 0.0655228551 0.0648170750 -0.0746244758 0.0317114394 -0.0250118389 [126] 0.0899792421 -0.0255719405 -0.0137618111 -0.0662586042 -0.0646754129 [131] -0.1630559011 -0.0925675613 > postscript(file="/var/www/html/rcomp/tmp/2nkn51260971249.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/39ps61260971249.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/4ozfr1260971249.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/5dgur1260971249.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/68ujt1260971249.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/7e1qi1260971249.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/8ucrk1260971249.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/94yc61260971249.tab") > > try(system("convert tmp/1gzkl1260971249.ps tmp/1gzkl1260971249.png",intern=TRUE)) character(0) > try(system("convert tmp/2nkn51260971249.ps tmp/2nkn51260971249.png",intern=TRUE)) character(0) > try(system("convert tmp/39ps61260971249.ps tmp/39ps61260971249.png",intern=TRUE)) character(0) > try(system("convert tmp/4ozfr1260971249.ps tmp/4ozfr1260971249.png",intern=TRUE)) character(0) > try(system("convert tmp/5dgur1260971249.ps tmp/5dgur1260971249.png",intern=TRUE)) character(0) > try(system("convert tmp/68ujt1260971249.ps tmp/68ujt1260971249.png",intern=TRUE)) character(0) > try(system("convert tmp/7e1qi1260971249.ps tmp/7e1qi1260971249.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.923 1.733 15.250