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Type 'q()' to quit R. > x <- c(627,696,825,677,656,785,412,352,839,729,696,641,695,638,762,635,721,854,418,367,824,687,601,676,740,691,683,594,729,731,386,331,707,715,657,653,642,643,718,654,632,731,392,344,792,852,649,629,685,617,715,715,629,916,531,357,917,828,708,858,775,785,1006,789,734,906,532,387,991,841) > 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$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/ > #Source of accompanying publication: Office for Research, Development, and Education > #Technical description: Write here your technical program description (don't use hard returns!) > library(lattice) > if (par1 == 'TRUE') par1 <- TRUE > if (par1 == 'FALSE') par1 <- FALSE > par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter > par3 <- as.numeric(par3) #degree of non-seasonal differencing > par4 <- as.numeric(par4) #degree of seasonal differencing > par5 <- as.numeric(par5) #seasonal period > par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial > par6 <- 4 > par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial > par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial > par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial > armaGR <- function(arima.out, names, n){ + try1 <- arima.out$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,] -0.1123346 -0.3474069 -0.08486297 -0.2833618 -0.5377424 0.1990915 [2,] 0.0000000 -0.2946549 -0.03418631 -0.2535518 -0.6248504 0.2192258 [3,] 0.0000000 -0.2894359 0.00000000 -0.2474643 -0.6336367 0.2066288 [4,] 0.0000000 -0.3035015 0.00000000 -0.2260669 -0.6342142 0.0000000 [5,] 0.0000000 -0.2126429 0.00000000 0.0000000 -0.7024603 0.0000000 [6,] 0.0000000 0.0000000 0.00000000 0.0000000 -1.3074259 0.0000000 [7,] 0.0000000 0.0000000 0.00000000 0.0000000 -1.3257291 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 [15,] NA NA NA NA NA NA [16,] NA NA NA NA NA NA [,7] [,8] [1,] -0.2254442 -0.9998795 [2,] -0.2251410 -1.0001964 [3,] -0.2274097 -1.0004262 [4,] -0.3054937 -0.5694239 [5,] -0.3256647 -0.5884564 [6,] -0.3070195 -0.5847386 [7,] 0.0000000 -0.6882157 [8,] NA NA [9,] NA NA [10,] NA NA [11,] NA NA [12,] NA NA [13,] NA NA [14,] NA NA [15,] NA NA [16,] NA NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 0.66219 0.07524 0.63596 0.06224 0.03844 0.27866 0.22267 0.16252 [2,] NA 0.04755 0.80457 0.06899 0.00000 0.21688 0.22383 0.13339 [3,] NA 0.04938 NA 0.07250 0.00000 0.22718 0.21800 0.13671 [4,] NA 0.04055 NA 0.10346 0.00000 NA 0.06280 0.01080 [5,] NA 0.14026 NA NA 0.00000 NA 0.05020 0.00898 [6,] NA NA NA NA 0.00000 NA 0.06359 0.00868 [7,] NA NA NA NA 0.00000 NA NA 0.00401 [8,] NA NA NA NA NA NA NA NA [9,] NA NA NA NA NA NA NA NA [10,] NA NA NA NA NA NA NA NA [11,] NA NA NA NA NA NA NA NA [12,] NA NA NA NA NA NA NA NA [13,] NA NA NA NA NA NA NA NA [14,] NA NA NA NA NA NA NA NA [15,] NA NA NA NA NA NA NA NA [16,] NA 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 ar4 ma1 sar1 sar2 sma1 -0.1123 -0.3474 -0.0849 -0.2834 -0.5377 0.1991 -0.2254 -0.9999 s.e. 0.2559 0.1919 0.1784 0.1492 0.2541 0.1821 0.1830 0.7073 sigma^2 estimated as 1.932e-06: log likelihood = 282.49, aic = -546.98 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ma1 sar1 sar2 sma1 -0.1123 -0.3474 -0.0849 -0.2834 -0.5377 0.1991 -0.2254 -0.9999 s.e. 0.2559 0.1919 0.1784 0.1492 0.2541 0.1821 0.1830 0.7073 sigma^2 estimated as 1.932e-06: log likelihood = 282.49, aic = -546.98 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ma1 sar1 sar2 sma1 0 -0.2947 -0.0342 -0.2536 -0.6249 0.2192 -0.2251 -1.0002 s.e. 0 0.1458 0.1376 0.1370 0.1153 0.1757 0.1832 0.6577 sigma^2 estimated as 1.950e-06: log likelihood = 282.38, aic = -548.77 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ma1 sar1 sar2 sma1 0 -0.2894 0 -0.2475 -0.6336 0.2066 -0.2274 -1.0004 s.e. 0 0.1444 0 0.1355 0.1094 0.1694 0.1828 0.6637 sigma^2 estimated as 1.942e-06: log likelihood = 282.35, aic = -550.71 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ma1 sar1 sar2 sma1 0 -0.3035 0 -0.2261 -0.6342 0 -0.3055 -0.5694 s.e. 0 0.1452 0 0.1369 0.1079 0 0.1613 0.2169 sigma^2 estimated as 2.451e-06: log likelihood = 282.12, aic = -552.24 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ar4 ma1 sar1 sar2 sma1 0 -0.2126 0 0 -0.7025 0 -0.3257 -0.5885 s.e. 0 0.1424 0 0 0.0989 0 0.1632 0.2184 sigma^2 estimated as 2.523e-06: log likelihood = 280.86, aic = -551.72 [[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 ar4 ma1 sar1 sar2 sma1 0 0 0 0 -1.3074 0 -0.3070 -0.5847 s.e. 0 0 0 0 0.1335 0 0.1627 0.2162 sigma^2 estimated as 1.547e-06: log likelihood = 279.82, aic = -551.65 [[3]][[8]] NULL $aic [1] -546.9836 -548.7664 -550.7052 -552.2417 -551.7206 -551.6454 -550.6598 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/html/rcomp/tmp/1ji991261245080.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 = 70 Frequency = 1 [1] 2.305713e-05 8.738068e-06 3.109416e-06 5.508033e-06 4.945943e-06 [6] 1.031967e-06 1.352128e-05 1.561303e-05 -3.888090e-06 -1.107721e-06 [11] -1.775265e-07 -2.111860e-05 -1.471002e-04 1.840929e-03 8.576538e-04 [16] 4.705318e-04 -1.533725e-03 -9.212234e-04 7.966414e-06 -4.602844e-04 [21] 5.362881e-04 9.117585e-04 1.807033e-03 -1.083107e-03 -1.310669e-03 [26] -4.838092e-04 1.928347e-03 9.705724e-04 -8.889809e-04 1.344240e-03 [31] 7.340551e-04 9.249973e-04 9.925801e-04 -1.405703e-03 -1.396471e-03 [36] -2.346075e-04 4.721868e-04 4.350857e-04 -3.321431e-04 -1.231897e-03 [41] 1.009229e-03 -1.889939e-04 -3.197137e-04 -7.173368e-04 -8.443156e-04 [46] -2.289369e-03 9.697169e-04 6.276622e-04 -6.755920e-04 1.022273e-03 [51] 6.762882e-04 -1.535465e-03 9.034257e-04 -2.197507e-03 -4.343718e-03 [56] 1.215227e-03 -3.798905e-04 2.491238e-04 -1.747965e-04 -2.694134e-03 [61] 3.759992e-04 -8.697215e-04 -2.253894e-03 -3.004311e-05 1.372646e-03 [66] 1.142851e-03 -9.181112e-04 1.864806e-04 -2.126680e-04 6.503388e-04 > postscript(file="/var/www/html/rcomp/tmp/239y41261245080.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/3xdn11261245080.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/4f4121261245080.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/5hw4x1261245080.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/62ivy1261245080.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/7fvrz1261245080.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/8o5nf1261245080.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/91ifb1261245080.tab") > > try(system("convert tmp/1ji991261245080.ps tmp/1ji991261245080.png",intern=TRUE)) character(0) > try(system("convert tmp/239y41261245080.ps tmp/239y41261245080.png",intern=TRUE)) character(0) > try(system("convert tmp/3xdn11261245080.ps tmp/3xdn11261245080.png",intern=TRUE)) character(0) > try(system("convert tmp/4f4121261245080.ps tmp/4f4121261245080.png",intern=TRUE)) character(0) > try(system("convert tmp/5hw4x1261245080.ps tmp/5hw4x1261245080.png",intern=TRUE)) character(0) > try(system("convert tmp/62ivy1261245080.ps tmp/62ivy1261245080.png",intern=TRUE)) character(0) > try(system("convert tmp/7fvrz1261245080.ps tmp/7fvrz1261245080.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 10.703 2.064 12.997