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Type 'q()' to quit R. > x <- c(25.22,27.63,27.47,22.54,27.4,29.68,28.51,29.89,32.62,30.93,32.52,25.28,25.64,27.41,24.4,25.55,28.45,27.72,24.54,25.67,25.54,20.48,18.94,18.6,19.49,20.29,23.69,25.65,25.43,24.13,25.77,26.63,28.34,27.55,24.5,28.52,31.29,32.65,30.34,25.02,25.81,27.55,28.4,29.83,27.1,29.59,28.77,29.88,31.18,30.87,33.8,33.36,37.92,35.19,38.37,43.03,43.38,49.77,43.05,39.65,44.28,45.56,53.08,51.86,48.67,54.31,57.58,64.09,62.98,58.52,55.54,56.75,63.57,59.92,62.25,70.44,70.19,68.86,73.9,73.61,62.77,58.38,58.48,62.31,54.3,57.76,62.14,67.4,67.48,71.32,77.2,70.8,77.13,83.04,92.53,91.45,91.92,94.82,103.28,110.44,123.94,133.05,133.9,113.85,99.06,72.84,53.24,41.58,44.86,43.24,46.84,50.85,57.94,68.59,64.92,72.5,67.69,73.19,77.04,74.67) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > 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,] -0.7040527 0.31564909 0.1382048 0.9999942 -0.06688037 -0.10703240 [2,] -0.6963605 0.31826226 0.1348292 1.0001440 0.00000000 -0.07030102 [3,] -0.7110605 0.30035110 0.1301139 0.9999949 0.00000000 0.00000000 [4,] 0.4351356 0.03708451 0.0000000 -0.1991890 0.00000000 0.00000000 [5,] 0.5331856 0.00000000 0.0000000 -0.2918868 0.00000000 0.00000000 [6,] 0.6480512 0.00000000 0.0000000 -0.3005908 0.00000000 0.00000000 [7,] 0.3938081 0.00000000 0.0000000 0.0000000 0.00000000 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,] -0.7990616 [2,] -0.8844550 [3,] -1.0777102 [4,] -0.9781089 [5,] -1.0108934 [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,] 0.00000 0.00897 0.16708 0.00000 0.76486 0.56083 0.00651 [2,] 0.00000 0.00800 0.17474 0.00000 NA 0.61818 0.00006 [3,] 0.00000 0.00938 0.18688 0.00000 NA NA 0.00266 [4,] 0.45754 0.84273 NA 0.73118 NA NA 0.23176 [5,] 0.02845 NA NA 0.27682 NA NA 0.41186 [6,] 0.00083 NA NA 0.19270 NA NA NA [7,] 0.00002 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 -0.7041 0.3156 0.1382 1.0000 -0.0669 -0.1070 -0.7991 s.e. 0.1030 0.1187 0.0994 0.0654 0.2231 0.1835 0.2882 sigma^2 estimated as 0.1213: log likelihood = -48.55, aic = 113.11 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.7041 0.3156 0.1382 1.0000 -0.0669 -0.1070 -0.7991 s.e. 0.1030 0.1187 0.0994 0.0654 0.2231 0.1835 0.2882 sigma^2 estimated as 0.1213: log likelihood = -48.55, aic = 113.11 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.6964 0.3183 0.1348 1.0001 0 -0.0703 -0.8845 s.e. 0.0998 0.1179 0.0987 0.0717 0 0.1407 0.2121 sigma^2 estimated as 0.1175: log likelihood = -48.6, aic = 111.2 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.7111 0.3004 0.1301 1.0000 0 0 -1.0777 s.e. 0.0956 0.1137 0.0980 0.0733 0 0 0.3508 sigma^2 estimated as 0.09947: log likelihood = -48.72, aic = 109.44 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4351 0.0371 0 -0.1992 0 0 -0.9781 s.e. 0.5837 0.1865 0 0.5784 0 0 0.8136 sigma^2 estimated as 0.1171: log likelihood = -49.76, aic = 109.53 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5332 0 0 -0.2919 0 0 -1.0109 s.e. 0.2403 0 0 0.2671 0 0 1.2274 sigma^2 estimated as 0.1134: log likelihood = -49.78, aic = 107.57 [[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 0.6481 0 0 -0.3006 0 0 0 s.e. 0.1889 0 0 0.2294 0 0 0 sigma^2 estimated as 0.2226: log likelihood = -71.55, aic = 149.11 $aic [1] 113.1071 111.1985 109.4395 109.5280 107.5658 149.1094 148.4689 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 log(s2) : NaNs produced 3: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 4: In log(s2) : NaNs produced 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 7: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 8: 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/145qm1293300811.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 = 120 Frequency = 1 [1] 0.0028994246 0.0014782824 0.0009548792 0.0002764507 0.0006609193 [6] 0.0007462452 0.0005380712 0.0005904772 0.0007556527 0.0005372804 [11] 0.0006232027 -0.0031565451 -0.0182853915 -0.0568895590 -0.2535948072 [16] 0.7143104280 -0.3875408501 -0.2641814499 -0.0991911606 0.0866364987 [21] -0.2213462529 -0.2782553730 -0.1531050968 0.7933431601 -0.1070424061 [26] -0.1573097903 0.6645776377 -0.1447703332 -0.3977580904 0.0137373293 [31] 0.5195001684 -0.1806729925 0.1402813345 0.3816463265 -0.3047645049 [36] 0.4196828371 -0.0010885901 -0.0678380177 -0.6088442808 -0.5180285978 [41] 0.4003243273 0.3544924343 -0.1710681857 0.0514145054 -0.4350068575 [46] 0.4494169231 0.1582450879 -0.3852238586 -0.0647340052 -0.0796239068 [51] 0.5355826339 0.3288039091 0.0991661085 -0.5613014262 0.2686597586 [56] 0.1957423544 0.1904798770 0.1086874537 -0.5369528997 -0.2576730451 [61] 0.4001795625 0.0881745304 0.2246748359 -0.1589287193 -0.6249113040 [66] 0.8245446721 -0.1968794724 0.0211885361 -0.1236480438 -0.7293840484 [71] 0.5659844910 0.3233354781 -0.0441723706 -0.3943988285 -0.2928613889 [76] 0.7497845445 0.0549354223 -0.5925358146 0.2080526951 -0.4234702153 [81] -0.4330507973 0.2544802079 0.2777079118 0.1170672536 -1.0368300364 [86] 0.7768966464 0.0670316183 -0.2427073720 0.0609631531 0.3157585118 [91] -0.0632263036 -0.4020137426 1.1343133641 0.2890624718 0.1901492868 [96] -0.5696703008 0.5743165525 -0.2644597970 0.1152790807 -0.0380095651 [101] 0.5948513159 -0.0508848172 -0.4308210329 -0.4615877172 -0.8809376166 [106] -1.3100128956 -1.0053352729 0.0364330013 0.7491815570 -0.1927024865 [111] -0.0384400300 0.0306735783 -0.0951464552 0.3321702535 -0.3353462964 [116] 1.4274619516 -0.0216685444 1.4608895343 0.7678369376 -0.0032161018 > postscript(file="/var/www/html/rcomp/tmp/245qm1293300811.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/html/rcomp/tmp/345qm1293300811.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/html/rcomp/tmp/4ee771293300811.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/html/rcomp/tmp/5ee771293300811.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/html/rcomp/tmp/6ee771293300811.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/html/rcomp/tmp/7ee771293300811.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/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/8lgqt1293300812.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/96z6g1293300812.tab") > > try(system("convert tmp/145qm1293300811.ps tmp/145qm1293300811.png",intern=TRUE)) character(0) > try(system("convert tmp/245qm1293300811.ps tmp/245qm1293300811.png",intern=TRUE)) character(0) > try(system("convert tmp/345qm1293300811.ps tmp/345qm1293300811.png",intern=TRUE)) character(0) > try(system("convert tmp/4ee771293300811.ps tmp/4ee771293300811.png",intern=TRUE)) character(0) > try(system("convert tmp/5ee771293300811.ps tmp/5ee771293300811.png",intern=TRUE)) character(0) > try(system("convert tmp/6ee771293300811.ps tmp/6ee771293300811.png",intern=TRUE)) character(0) > try(system("convert tmp/7ee771293300811.ps tmp/7ee771293300811.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 14.598 2.301 32.316