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Type 'q()' to quit R. > x <- c(19,23,22,23,25,25,23,22,21,16,21,21,26,23,22,22,22,12,20,18,23,25,28,28,29,31,33,32,33,35,33,36,30,34,34,35,33,28,27,23,23,24,24,20,16,6,2,12,19,21,22,20,21,20,19,17,17,17,16,12,11,7,2,9,11,10,7,9,15,5,14,14,17,19,17,16,14,20,16,18,18,14,13,14,14,17,18,15,9,9,9,10,6,12,11,15,19,18,15,16,14,18,18,18,18,22,21,12,19,21,19,22,22,21,19,18,18,19,12,16) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '0' > par3 = '1' > par2 = '1.3' > 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,] 0.4688013 0.14118781 -0.12346779 -0.6677979 0.2099902 -0.02789969 [2,] 0.4638959 0.13854867 -0.12409330 -0.6624189 0.3132985 0.00000000 [3,] 0.4686815 0.14225048 -0.10907085 -0.6769638 0.0000000 0.00000000 [4,] 0.0000000 0.05121128 -0.09552992 -0.2096108 0.0000000 0.00000000 [5,] 0.0000000 0.00000000 -0.09441801 -0.2008250 0.0000000 0.00000000 [6,] 0.0000000 0.00000000 0.00000000 -0.1986912 0.0000000 0.00000000 [7,] 0.0000000 0.00000000 0.00000000 -0.1710899 0.0000000 0.00000000 [8,] 0.0000000 0.00000000 0.00000000 0.0000000 0.0000000 0.00000000 [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,] -0.3918737 [2,] -0.4972177 [3,] -0.1887497 [4,] -0.1903523 [5,] -0.1928149 [6,] -0.1886506 [7,] 0.0000000 [8,] 0.0000000 [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.42129 0.37467 0.25316 0.25720 0.80574 0.88801 0.64748 [2,] 0.38946 0.35777 0.24103 0.22301 0.48932 NA 0.24571 [3,] 0.39847 0.36657 0.29356 0.22455 NA NA 0.06718 [4,] NA 0.58789 0.30335 0.02699 NA NA 0.06243 [5,] NA NA 0.30865 0.02473 NA NA 0.05827 [6,] NA NA NA 0.03074 NA NA 0.05679 [7,] NA NA NA 0.05768 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 0.4688 0.1412 -0.1235 -0.6678 0.2100 -0.0279 -0.3919 s.e. 0.5808 0.1584 0.1075 0.5864 0.8519 0.1977 0.8547 sigma^2 estimated as 103.9: log likelihood = -445.49, aic = 906.97 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4688 0.1412 -0.1235 -0.6678 0.2100 -0.0279 -0.3919 s.e. 0.5808 0.1584 0.1075 0.5864 0.8519 0.1977 0.8547 sigma^2 estimated as 103.9: log likelihood = -445.49, aic = 906.97 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4639 0.1385 -0.1241 -0.6624 0.3133 0 -0.4972 s.e. 0.5370 0.1500 0.1053 0.5406 0.4517 0 0.4261 sigma^2 estimated as 103.9: log likelihood = -445.49, aic = 904.99 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.4687 0.1423 -0.1091 -0.6770 0 0 -0.1887 s.e. 0.5530 0.1569 0.1034 0.5544 0 0 0.1021 sigma^2 estimated as 104.4: log likelihood = -445.7, aic = 903.4 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.0512 -0.0955 -0.2096 0 0 -0.1904 s.e. 0 0.0942 0.0924 0.0936 0 0 0.1012 sigma^2 estimated as 105.1: log likelihood = -446.1, aic = 902.21 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 -0.0944 -0.2008 0 0 -0.1928 s.e. 0 0 0.0923 0.0883 0 0 0.1008 sigma^2 estimated as 105.4: log likelihood = -446.25, aic = 900.5 [[3]][[7]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0 -0.1987 0 0 -0.1887 s.e. 0 0 0 0.0909 0 0 0.0981 sigma^2 estimated as 106.4: log likelihood = -446.77, aic = 899.54 [[3]][[8]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0 0 -0.1711 0 0 0 s.e. 0 0 0 0.0893 0 0 0 sigma^2 estimated as 110: log likelihood = -448.55, aic = 901.1 $aic [1] 906.9742 904.9894 903.4026 902.2083 900.5043 899.5450 901.1036 902.7585 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 7: In max(i) : no non-missing arguments to max; returning -Inf 8: In max(i) : no non-missing arguments to max; returning -Inf 9: In max(try.data.frame[, 4], na.rm = TRUE) : no non-missing arguments to max; returning -Inf > postscript(file="/var/www/html/rcomp/tmp/1y4ud1228931525.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 = 120 Frequency = 1 [1] 0.04595994 12.77221413 -1.15381064 3.11083005 7.27766318 [6] 1.24513443 -6.53241060 -4.42582086 -4.02058602 -16.27572514 [11] 12.80323068 2.19050384 17.12656257 -7.25004320 -4.54860057 [16] -0.77821975 -0.13314556 -30.34311243 18.64848927 -3.09807938 [21] 15.54723142 9.40541530 12.03209724 2.05857068 3.90353972 [26] 7.88047266 8.70192863 -2.20525459 3.31677010 8.05612179 [31] -6.11033451 10.23646621 -20.50852813 11.19700576 1.91569493 [36] 4.08860504 -6.78913668 -19.27916736 -6.81195665 -14.82033788 [41] -2.53561057 2.91781843 0.49920935 -13.05485968 -14.60432871 [46] -28.98638178 -12.76760836 20.64253274 24.20246284 10.52702425 [51] 5.06444048 -5.61396755 2.25657636 -2.83099218 -3.65351115 [56] -6.81247834 -1.16554644 -0.19941326 -3.04833501 -11.99065599 [61] -4.75621074 -10.84871058 -11.94334604 12.89296334 7.39171834 [66] -1.36722883 -7.63701250 3.54249273 17.00759714 -22.78703912 [71] 18.89865094 3.23336887 9.42519477 7.79995529 -4.85290557 [76] -3.84450069 -6.51553586 17.11381162 -9.44277285 4.46656224 [81] 0.76418382 -11.80916193 -4.85849557 2.00682721 0.34334793 [86] 8.93074129 4.59586807 -8.25401548 -17.81369124 -3.04774319 [91] -0.52143817 2.46477193 -9.26030634 13.43426647 -0.40626253 [96] 11.14614273 14.06680622 -0.71280231 -9.16227553 1.39062357 [101] -5.61985882 10.97840484 1.87829452 0.32135728 0.05498099 [106] 12.77849711 -1.07710046 -27.24124097 16.01003134 9.12538171 [111] -4.82496565 8.82409618 1.50971400 -3.00507578 -6.90036478 [116] -4.30007413 -0.73569938 2.99362045 -20.15855504 8.02019079 > postscript(file="/var/www/html/rcomp/tmp/2xcaf1228931525.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/3mw9g1228931525.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/4apxt1228931525.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/5tk3l1228931525.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/6etin1228931525.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/74d0p1228931525.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/87j1o1228931525.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/9pkiu1228931525.tab") > > system("convert tmp/1y4ud1228931525.ps tmp/1y4ud1228931525.png") > system("convert tmp/2xcaf1228931525.ps tmp/2xcaf1228931525.png") > system("convert tmp/3mw9g1228931525.ps tmp/3mw9g1228931525.png") > system("convert tmp/4apxt1228931525.ps tmp/4apxt1228931525.png") > system("convert tmp/5tk3l1228931525.ps tmp/5tk3l1228931525.png") > system("convert tmp/6etin1228931525.ps tmp/6etin1228931525.png") > system("convert tmp/74d0p1228931525.ps tmp/74d0p1228931525.png") > > > proc.time() user system elapsed 6.292 1.667 7.285