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Type 'q()' to quit R. > x <- c(332,369,384,373,378,426,423,397,422,409,430,412,396,470,491,504,484,474,508,492,452,457,457,471,451,476,493,514,522,490,484,506,501,462,465,454,464,427,482,460,473,465,422,415,413,420,363,376,380,384,346,410,389,407,393,346,348,353,364,305,307,312,312,286,344,324,336,327,302,299,311,315,264,278,278,287,279,300,324,354,354,360,363,385,412,370,389,395,417,404,377,456,478,468,437,432,441,449,386,396,394) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > 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,] -0.6545710 -0.2546617 -0.1811960 0.3576095 0.9731370 -0.1551138 [2,] -0.6316041 -0.2677432 -0.1965042 0.3375310 -0.1507514 0.0000000 [3,] -0.6295530 -0.2718762 -0.1975508 0.3365515 0.0000000 0.0000000 [4,] -0.5966542 -0.2539263 -0.1863992 0.3439475 0.0000000 0.0000000 [5,] -0.2563240 -0.1663931 -0.1128960 0.0000000 0.0000000 0.0000000 [6,] -0.2405860 -0.1390985 0.0000000 0.0000000 0.0000000 0.0000000 [7,] -0.2097656 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 [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,] -0.99997227 [2,] 0.22232881 [3,] 0.07047283 [4,] 0.00000000 [5,] 0.00000000 [6,] 0.00000000 [7,] 0.00000000 [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.04880 0.10285 0.09185 0.25887 0.00000 0.29701 0.00009 [2,] 0.04705 0.06337 0.06172 0.26453 0.81162 NA 0.71800 [3,] 0.04953 0.05974 0.06265 0.26892 NA NA 0.64729 [4,] 0.07074 0.06045 0.07529 0.28535 NA NA NA [5,] 0.01207 0.10542 0.27159 NA NA NA NA [6,] 0.01778 0.16481 NA NA NA NA NA [7,] 0.03510 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 -0.6546 -0.2547 -0.1812 0.3576 0.9731 -0.1551 -1.0000 s.e. 0.3279 0.1546 0.1064 0.3148 0.1502 0.1479 0.2436 sigma^2 estimated as 589.4: log likelihood = -465.06, aic = 946.12 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.6546 -0.2547 -0.1812 0.3576 0.9731 -0.1551 -1.0000 s.e. 0.3279 0.1546 0.1064 0.3148 0.1502 0.1479 0.2436 sigma^2 estimated as 589.4: log likelihood = -465.06, aic = 946.12 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.6316 -0.2677 -0.1965 0.3375 -0.1508 0 0.2223 s.e. 0.3139 0.1425 0.1039 0.3007 0.6307 0 0.6138 sigma^2 estimated as 653.6: log likelihood = -466.14, aic = 946.29 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.6296 -0.2719 -0.1976 0.3366 0 0 0.0705 s.e. 0.3165 0.1427 0.1049 0.3026 0 0 0.1535 sigma^2 estimated as 654: log likelihood = -466.17, aic = 944.34 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.5967 -0.2539 -0.1864 0.3439 0 0 0 s.e. 0.3265 0.1337 0.1037 0.3201 0 0 0 sigma^2 estimated as 655.9: log likelihood = -466.27, aic = 942.54 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.2563 -0.1664 -0.1129 0 0 0 0 s.e. 0.1002 0.1018 0.1021 0 0 0 0 sigma^2 estimated as 662.1: log likelihood = -466.73, aic = 941.45 [[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 -0.2406 -0.1391 0 0 0 0 0 s.e. 0.0998 0.0994 0 0 0 0 0 sigma^2 estimated as 670.4: log likelihood = -467.33, aic = 940.66 $aic [1] 946.1214 946.2851 944.3408 942.5415 941.4502 940.6642 940.6021 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/www/rcomp/tmp/17dp41293370198.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 = 101 Frequency = 1 [1] 0.3319998 35.8138208 22.5929304 -2.2445649 4.4400310 47.6728467 [7] 9.2436225 -20.0450302 18.3274674 -10.6019099 21.3498439 -14.7559736 [13] -17.4094803 67.6468504 36.5777912 28.3455957 -13.9513130 -13.0034404 [19] 28.8121696 -9.2110595 -39.1200278 -6.8490177 -4.3610097 14.6954925 [25] -16.6317954 22.1356581 20.2326811 28.5674252 15.4169814 -27.1542432 [31] -12.5859654 16.1053318 -0.5416980 -37.1427633 -7.0783482 -15.7030833 [37] 7.7708490 -36.1242230 47.4893014 -13.9144121 15.3575244 -7.9325484 [43] -43.1164079 -18.4579878 -9.6653377 5.5451384 -55.5940947 0.2602850 [49] -0.8009958 6.7706246 -36.4812618 55.4141244 -10.8882362 21.8499970 [55] -12.5905197 -47.8644316 -11.2549230 -1.0564573 12.4811272 -55.6580610 [61] -10.6644930 -2.7256393 1.4811272 -25.3045075 51.7447629 -9.6625705 [67] 15.2559920 -8.8949374 -25.4960924 -10.2665375 7.8007794 6.4697370 [73] -48.3684739 2.2865058 -3.7258188 10.9473790 -5.8347256 20.3271981 [79] 27.9395189 38.6951335 10.5559452 10.1729549 4.4435163 23.5563491 [85] 32.7101884 -32.4440099 12.6510456 4.7289979 26.0863877 -6.8725161 [91] -27.0674516 70.6958964 37.2506379 6.2816743 -30.3456935 -13.8491523 [97] 3.4850164 9.4697819 -59.8234252 -4.0441327 -8.3573449 > postscript(file="/var/www/rcomp/tmp/2z4671293370198.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/www/rcomp/tmp/3z4671293370198.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/www/rcomp/tmp/4z4671293370198.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/www/rcomp/tmp/5z4671293370198.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/www/rcomp/tmp/6svoa1293370198.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/www/rcomp/tmp/7svoa1293370198.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/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/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/rcomp/tmp/8onl01293370198.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/rcomp/tmp/9he341293370198.tab") > > try(system("convert tmp/17dp41293370198.ps tmp/17dp41293370198.png",intern=TRUE)) character(0) > try(system("convert tmp/2z4671293370198.ps tmp/2z4671293370198.png",intern=TRUE)) character(0) > try(system("convert tmp/3z4671293370198.ps tmp/3z4671293370198.png",intern=TRUE)) character(0) > try(system("convert tmp/4z4671293370198.ps tmp/4z4671293370198.png",intern=TRUE)) character(0) > try(system("convert tmp/5z4671293370198.ps tmp/5z4671293370198.png",intern=TRUE)) character(0) > try(system("convert tmp/6svoa1293370198.ps tmp/6svoa1293370198.png",intern=TRUE)) character(0) > try(system("convert tmp/7svoa1293370198.ps tmp/7svoa1293370198.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.500 5.340 12.041