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Type 'q()' to quit R. > x <- c(1.262,1.743,1.964,3.258,4.966,4.944,5.907,5.561,5.321,3.582,1.757,1.894,1.442,2.238,2.179,3.218,5.139,4.990,4.914,6.084,5.672,3.548,1.793,2.086,1.376,2.202,2.683,3.303,5.202,5.231,4.880,7.998,4.977,3.531,2.025,2.205,1.504,2.090,2.702,2.939,4.500,6.208,6.415,5.657,5.964,3.163,1.997,2.422,1.507,1.992,2.487,3.490,4.647,5.594,5.611,5.788,6.204,3.013,1.931,2.549,1.580,2.111,2.192,3.601,4.665,4.876,5.813,5.589,5.331,3.075,2.002,2.306,1.594,2.467,2.222,3.607,4.685,4.962,5.770,5.480,5.000,3.228,1.993,2.288,1.351,2.218,2.461,3.028,4.784,4.975,4.607,6.249,4.809,3.157,1.910,2.228,1.169,2.154,2.249,2.687,4.359,5.382,4.459,6.398,4.596,3.024,1.887,2.070,1.511,2.059,2.635,2.867,4.403,5.720,4.502,5.749,5.627,2.846,1.762,2.429,1.579,2.146,2.462,3.695,4.831,5.134,6.250,5.760,6.249,2.917,1.741,2.359) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '0' > 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,] 0.507277752 0.1978808 0.2122518 -0.8138900 0.3088977 -0.2136248 [2,] -0.006815522 0.0000000 0.2585835 -0.2514246 0.3192297 -0.2206174 [3,] 0.000000000 0.0000000 0.2593672 -0.2577688 0.3196415 -0.2208713 [4,] NA NA NA NA NA NA [5,] NA NA NA NA NA NA [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,] -0.999991 [2,] -1.000001 [3,] -1.000001 [4,] NA [5,] NA [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,] 0.00050 0.05315 0.03447 0.00000 0.00399 0.03931 0 [2,] 0.98461 NA 0.01142 0.46173 0.00377 0.02838 0 [3,] NA NA 0.00567 0.00456 0.00308 0.02687 0 [4,] NA NA NA NA NA NA NA [5,] NA NA NA NA NA NA NA [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 0.5073 0.1979 0.2123 -0.8139 0.3089 -0.2136 -1.0000 s.e. 0.1418 0.1014 0.0993 0.1106 0.1053 0.1026 0.1637 sigma^2 estimated as 0.1243: log likelihood = -58.92, aic = 133.84 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5073 0.1979 0.2123 -0.8139 0.3089 -0.2136 -1.0000 s.e. 0.1418 0.1014 0.0993 0.1106 0.1053 0.1026 0.1637 sigma^2 estimated as 0.1243: log likelihood = -58.92, aic = 133.84 [[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 -0.0068 0 0.2586 -0.2514 0.3192 -0.2206 -1.0000 s.e. 0.3526 0 0.1007 0.3406 0.1081 0.0995 0.1427 sigma^2 estimated as 0.1264: log likelihood = -60.32, aic = 134.64 [[3]][[4]] NULL [[3]][[5]] NULL [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 133.8356 134.6398 132.6401 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 > postscript(file="/var/www/rcomp/tmp/14u6r1323974956.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 = 132 Frequency = 1 [1] 0.0012619988 0.0017429989 0.0019639991 0.0032579974 0.0049659963 [6] 0.0049439972 0.0059069958 0.0055609959 0.0053209963 0.0035819973 [11] 0.0017569984 0.0018939972 0.1305348021 0.4026431844 0.2810182326 [16] 0.0033106164 0.0369211864 0.0024554388 -0.7584873081 0.1801605689 [21] 0.3097698702 0.2594387815 0.0099478291 0.0772391405 0.0051721509 [26] 0.0508652015 0.4168452609 0.1738004633 0.1122076886 0.1152247723 [31] -0.1913535167 1.5579815557 -0.1377235958 0.0076074832 -0.1885081428 [36] 0.2128805668 0.2000183518 0.0458883316 0.1997476164 -0.2870503633 [41] -0.6147756649 0.7002048264 1.2558932628 -0.8584255954 0.3060710424 [46] -0.4935362810 0.2898210916 0.1557751193 0.1848495512 0.0036399377 [51] 0.0003542737 0.3644796871 0.0415367061 -0.0875664513 -0.5201006055 [56] 0.0359655431 0.4113966066 -0.0642443559 -0.0055561672 0.1892316629 [61] 0.2616313773 0.1478502132 -0.1804909428 0.0919125482 -0.2146376391 [66] -0.3605770528 0.2644796243 -0.4387163997 -0.3841582977 -0.3958035552 [71] 0.1659112254 0.1505548375 0.1994818255 0.3724183525 0.0408264842 [76] 0.2129006202 -0.1726941600 -0.1527077078 -0.0242118696 -0.4572552358 [81] -0.3872420902 -0.1871451029 0.1395931211 0.2053588491 -0.0490384850 [86] -0.0389430164 0.0751043157 -0.2689981738 -0.0946532867 -0.3181678094 [91] -0.9655837438 0.0475135431 -0.4350458845 -0.0217071610 -0.0889144296 [96] 0.0908273705 -0.1359754944 0.0459110749 -0.1522827769 -0.4139209262 [101] -0.5771488190 0.0589773017 -0.5213556214 0.1218326916 -0.6771265123 [106] -0.2194521587 -0.0979258142 -0.0053196762 0.2024962766 0.0014355830 [111] 0.3678502083 -0.1713225695 -0.2411101539 0.1983743655 -0.6235726162 [116] -0.4809336406 0.1943785972 -0.0903292999 -0.0617611130 0.1178697831 [121] 0.1747359831 0.1301890018 -0.0587097471 0.4272278233 0.2127982595 [126] -0.2016182263 0.7871828151 0.0768387637 0.6881943359 -0.2758834571 [131] -0.1524428349 -0.1876249081 > postscript(file="/var/www/rcomp/tmp/2e4q41323974956.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/3aq9c1323974956.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/4jvl91323974956.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/5wgoe1323974956.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/6hnuy1323974956.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/7i9r91323974956.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/8uhry1323974956.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/9byqp1323974956.tab") > > try(system("convert tmp/14u6r1323974956.ps tmp/14u6r1323974956.png",intern=TRUE)) character(0) > try(system("convert tmp/2e4q41323974956.ps tmp/2e4q41323974956.png",intern=TRUE)) character(0) > try(system("convert tmp/3aq9c1323974956.ps tmp/3aq9c1323974956.png",intern=TRUE)) character(0) > try(system("convert tmp/4jvl91323974956.ps tmp/4jvl91323974956.png",intern=TRUE)) character(0) > try(system("convert tmp/5wgoe1323974956.ps tmp/5wgoe1323974956.png",intern=TRUE)) character(0) > try(system("convert tmp/6hnuy1323974956.ps tmp/6hnuy1323974956.png",intern=TRUE)) character(0) > try(system("convert tmp/7i9r91323974956.ps tmp/7i9r91323974956.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 6.950 0.370 7.299