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Type 'q()' to quit R. > x <- c(2648.9,2669.6,3042.3,2604.2,2732.1,2621.7,2483.7,2479.3,2684.6,2834.7,2566.1,2251.2,2350,2299.8,2542.8,2530.2,2508.1,2616.8,2534.1,2181.8,2578.9,2841.9,2529.9,2103.2,2326.2,2452.6,2782.1,2727.3,2648.2,2760.7,2613,2225.4,2713.9,2923.3,2707,2473.9,2521,2531.8,3068.8,2826.9,2674.2,2966.6,2798.8,2629.6,3124.6,3115.7,3083,2863.9,2728.7,2789.4,3225.7,3148.2,2836.5,3153.5,2656.9,2834.7,3172.5,2998.8,3103.1,2735.6,2818.1,2874.4,3438.5,2949.1,3306.8,3530,3003.8,3206.4,3514.6,3522.6,3525.5,2996.2,3231.1,3030,3541.7,3113.2,3390.8,3424.2,3079.8,3123.4,3317.1,3579.9,3317.9,2668.1,3609.2,3535.2,3644.7,3925.7,3663.2,3905.3,3990,3695.8) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > par2 = '-0.1' > 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,] -0.8473530 -0.4707148 0.04849662 0.2782206 0.3293324 -0.1486581 [2,] -0.9425038 -0.5381944 0.00000000 0.3688916 0.3265322 -0.1463409 [3,] -0.9191550 -0.5523418 0.00000000 0.3143530 0.3190829 0.0000000 [4,] -0.6705457 -0.4387875 0.00000000 0.0000000 0.3210049 0.0000000 [5,] -0.7193929 -0.4957038 0.00000000 0.0000000 0.0000000 0.0000000 [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,] -0.9954695 [2,] -0.9990501 [3,] -1.0001368 [4,] -0.9996700 [5,] -0.6808280 [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,] 8e-05 0.00018 0.64128 0.24512 0.00219 0.17646 0.10541 [2,] 1e-05 0.00001 NA 0.13358 0.03649 0.37157 0.09587 [3,] 1e-05 0.00000 NA 0.16572 0.04576 NA 0.00081 [4,] 0e+00 0.00006 NA NA 0.05394 NA 0.00155 [5,] 0e+00 0.00000 NA NA NA NA 0.00102 [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 -0.8474 -0.4707 0.0485 0.2782 0.3293 -0.1487 -0.9955 s.e. 0.2050 0.1198 0.1037 0.2377 0.1042 0.1091 0.6082 sigma^2 estimated as 5.163e-06: log likelihood = 358.02, aic = -700.04 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.8474 -0.4707 0.0485 0.2782 0.3293 -0.1487 -0.9955 s.e. 0.2050 0.1198 0.1037 0.2377 0.1042 0.1091 0.6082 sigma^2 estimated as 5.163e-06: log likelihood = 358.02, aic = -700.04 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.9425 -0.5382 0 0.3689 0.3265 -0.1463 -0.9991 s.e. 0.2035 0.1111 0 0.2436 0.1537 0.1629 0.5933 sigma^2 estimated as 5.147e-06: log likelihood = 358.01, aic = -702.01 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.9192 -0.5523 0 0.3144 0.3191 0 -1.0001 s.e. 0.1898 0.1086 0 0.2249 0.1574 0 0.2881 sigma^2 estimated as 5.417e-06: log likelihood = 357.62, aic = -703.25 [[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 -0.6705 -0.4388 0 0 0.3210 0 -0.9997 s.e. 0.1033 0.1041 0 0 0.1643 0 0.3058 sigma^2 estimated as 5.535e-06: log likelihood = 356.85, aic = -703.71 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -700.0399 -702.0111 -703.2492 -703.7067 -702.5686 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 > postscript(file="/var/www/html/freestat/rcomp/tmp/1vum61228589136.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 = 92 Frequency = 1 [1] 2.625020e-04 1.171204e-04 7.168817e-05 5.963018e-05 4.559799e-05 [6] 3.966315e-05 3.626280e-05 3.178710e-05 2.481304e-05 1.998184e-05 [11] 2.247847e-05 -2.275944e-04 -1.574231e-03 8.691542e-04 1.402685e-03 [16] -4.350667e-03 -1.147011e-03 -4.104666e-03 -1.996906e-03 3.639353e-03 [21] 8.849408e-05 -1.457216e-03 -1.837369e-03 1.899147e-03 -8.857912e-04 [26] -3.298743e-03 -3.321875e-03 -2.941101e-03 3.545076e-04 -6.424068e-04 [31] 5.565074e-04 2.340310e-03 -5.096204e-04 -2.405937e-04 -2.173187e-03 [36] -3.784460e-03 -1.976523e-04 1.267721e-03 -1.093008e-03 -2.177152e-04 [41] 1.584596e-03 -1.477989e-03 -1.093996e-03 -3.609801e-03 -1.719878e-03 [46] 1.848411e-03 -1.252377e-03 -2.604751e-03 9.271347e-04 9.748009e-04 [51] 1.715180e-03 -2.069619e-03 2.030714e-03 -7.551934e-04 4.944956e-03 [56] -3.445357e-03 9.106400e-05 2.356909e-03 -2.729341e-04 1.441028e-04 [61] -2.193921e-03 -5.866172e-04 -2.165355e-03 3.158820e-03 -4.789667e-03 [66] -2.725917e-03 -9.265113e-04 -1.762061e-03 4.481527e-04 -3.247486e-04 [71] -3.063997e-04 8.166505e-04 -1.305963e-03 2.760241e-03 1.543002e-03 [76] 2.448773e-03 -1.202296e-03 9.623286e-04 4.731265e-05 -5.782685e-05 [81] 1.889995e-03 -9.538132e-04 1.394098e-03 3.461703e-03 -7.584828e-03 [86] -5.657416e-03 4.456742e-04 -3.964647e-03 1.710379e-03 -1.645089e-03 [91] -3.892978e-03 -1.374559e-03 > postscript(file="/var/www/html/freestat/rcomp/tmp/2pixy1228589136.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/freestat/rcomp/tmp/3loxj1228589136.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/freestat/rcomp/tmp/4xg3j1228589136.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/freestat/rcomp/tmp/517em1228589136.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/freestat/rcomp/tmp/6svl61228589136.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/freestat/rcomp/tmp/7ms7s1228589136.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/freestat/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/www/html/freestat/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/freestat/rcomp/tmp/8mw5s1228589136.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/freestat/rcomp/tmp/9qjs71228589136.tab") > > system("convert tmp/1vum61228589136.ps tmp/1vum61228589136.png") > system("convert tmp/2pixy1228589136.ps tmp/2pixy1228589136.png") > system("convert tmp/3loxj1228589136.ps tmp/3loxj1228589136.png") > system("convert tmp/4xg3j1228589136.ps tmp/4xg3j1228589136.png") > system("convert tmp/517em1228589136.ps tmp/517em1228589136.png") > system("convert tmp/6svl61228589136.ps tmp/6svl61228589136.png") > system("convert tmp/7ms7s1228589136.ps tmp/7ms7s1228589136.png") > > > proc.time() user system elapsed 12.475 2.187 13.462