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Type 'q()' to quit R. > x <- c(27.951,29.781,32.914,33.488,35.652,36.488,35.387,35.676,34.844,32.447,31.068,29.010,29.812,30.951,32.974,32.936,34.012,32.946,31.948,30.599,27.691,25.073,23.406,22.248,22.896,25.317,26.558,26.471,27.543,26.198,24.725,25.005,23.462,20.780,19.815,19.761,21.454,23.899,24.939,23.580,24.562,24.696,23.785,23.812,21.917,19.713,19.282,18.788,21.453,24.482,27.474,27.264,27.349,30.632,29.429,30.084,26.290,24.379,23.335,21.346,21.106,24.514,28.353,30.805,31.348,34.556,33.855,34.787,32.529,29.998,29.257,28.155,30.466,35.704,39.327,39.351,42.234,43.630,43.722,43.121,37.985,37.135,34.646,33.026,35.087,38.846,42.013,43.908,42.868,44.423,44.167,43.636,44.382,42.142,43.452,36.912,42.413,45.344,44.873,47.510,49.554,47.369,45.998,48.140,48.441,44.928,40.454,38.661,37.246,36.843,36.424,37.594,38.144,38.737,34.560,36.080,33.508,35.462,33.374,32.110,35.533,35.532,37.903,36.763,40.399,44.164,44.496,43.110,43.880,43.930,44.327) > par9 = '1' > par8 = '2' > par7 = '1' > par6 = '3' > par5 = '12' > par4 = '1' > par3 = '1' > 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,] 0.52378991 0.1839730 0.0294141 -0.6285951 0.1536890 -0.04803831 [2,] 0.57866789 0.1962401 0.0000000 -0.6792432 0.1515848 -0.04536232 [3,] 0.58878263 0.1917530 0.0000000 -0.6875001 0.1539075 0.00000000 [4,] 0.68024742 0.1427230 0.0000000 -0.7398641 0.0000000 0.00000000 [5,] -0.66918070 0.0000000 0.0000000 0.6018823 0.0000000 0.00000000 [6,] -0.05298085 0.0000000 0.0000000 0.0000000 0.0000000 0.00000000 [7,] 0.00000000 0.0000000 0.0000000 0.0000000 0.0000000 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,] -0.9999884 [2,] -0.9999953 [3,] -1.0000064 [4,] -1.0000429 [5,] -0.9999404 [6,] -1.0002684 [7,] -1.0000125 [8,] NA [9,] NA [10,] NA [11,] NA [12,] NA [13,] NA [14,] NA [[2]] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 0.14819 0.09550 0.80609 0.06901 0.16316 0.66255 0.00000 [2,] 0.02305 0.04921 NA 0.00497 0.16760 0.67845 0.00000 [3,] 0.02134 0.05316 NA 0.00466 0.16211 NA 0.00000 [4,] 0.01201 0.14179 NA 0.00426 NA NA 0.05304 [5,] 0.11020 NA NA 0.17499 NA NA 0.14344 [6,] 0.56587 NA NA NA NA NA 0.01400 [7,] NA NA NA NA NA NA 0.00138 [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 0.5238 0.1840 0.0294 -0.6286 0.1537 -0.0480 -1.0000 s.e. 0.3600 0.1095 0.1196 0.3427 0.1095 0.1098 0.1598 sigma^2 estimated as 2.456: log likelihood = -233.64, aic = 483.28 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5238 0.1840 0.0294 -0.6286 0.1537 -0.0480 -1.0000 s.e. 0.3600 0.1095 0.1196 0.3427 0.1095 0.1098 0.1598 sigma^2 estimated as 2.456: log likelihood = -233.64, aic = 483.28 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5787 0.1962 0 -0.6792 0.1516 -0.0454 -1.0000 s.e. 0.2515 0.0988 0 0.2375 0.1092 0.1092 0.1609 sigma^2 estimated as 2.458: log likelihood = -233.67, aic = 481.33 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.5888 0.1918 0 -0.6875 0.1539 0 -1.0000 s.e. 0.2526 0.0982 0 0.2386 0.1094 0 0.1453 sigma^2 estimated as 2.485: log likelihood = -233.75, aic = 479.51 [[3]][[5]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.6802 0.1427 0 -0.7399 0 0 -1.0000 s.e. 0.2669 0.0965 0 0.2542 0 0 0.5121 sigma^2 estimated as 2.459: log likelihood = -234.76, aic = 479.51 [[3]][[6]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.6692 0 0 0.6019 0 0 -0.9999 s.e. 0.4160 0 0 0.4413 0 0 0.6792 sigma^2 estimated as 2.497: log likelihood = -235.73, aic = 479.45 [[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 -0.053 0 0 0 0 0 -1.0003 s.e. 0.092 0 0 0 0 0 0.4015 sigma^2 estimated as 2.51: log likelihood = -236.04, aic = 478.07 $aic [1] 483.2756 481.3349 479.5052 479.5148 479.4515 478.0702 476.4011 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/1grbn1292593450.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 = 131 Frequency = 1 [1] 0.0161375120 0.0086344194 0.0083004117 0.0066929684 0.0072965359 [6] 0.0068400715 0.0048324914 0.0044973396 0.0032089796 0.0006072213 [11] -0.0007657798 -0.0224077871 -0.1057308634 -0.4879223601 -0.8107200067 [16] -0.4743448409 -0.7922508698 -1.3856140397 0.0014287678 -1.1544000790 [21] -1.5293136412 -0.2342410868 -0.2121097398 0.6245747862 -0.0626262805 [26] 0.7598386374 -1.0510381765 -0.3476952898 -0.4627973862 -1.0279358797 [31] -0.3990330193 0.6428503235 0.3018749225 -0.1284471278 0.4478555740 [36] 1.2926116267 0.8581283080 0.6008027131 -0.9161425821 -1.3565149857 [41] -0.4635335786 0.5496915348 0.2723356613 0.2612739591 -0.1029465771 [46] 0.3069332955 0.8010025469 0.5576461256 1.4258381238 1.0287775219 [51] 1.0637177743 0.0692960481 -1.1068027447 3.1994633403 0.0990182243 [56] 0.7501741085 -1.7482734477 0.4098074335 0.0861214876 -0.9339475584 [61] -1.5670506505 1.0485847814 1.6599475196 2.5272757698 -0.3569319097 [66] 2.5660428323 0.5354210148 0.8896217224 -0.0120821662 -0.1570188575 [71] 0.3168931914 0.0618011073 1.0899487904 2.7035774488 1.2927128345 [76] -0.1222445434 1.7453874830 0.6061052927 1.0977230787 -0.6283312086 [81] -2.7495479446 1.2822414801 -1.2678582614 -0.5128314789 0.6610012510 [86] 0.9448223839 0.6197424003 1.6214626539 -2.0648755764 0.4792191914 [91] 0.6330540144 -0.4959250400 3.1236592698 0.1018699145 2.3862851354 [96] -4.8574504159 3.5452540113 0.2213235960 -2.9242709536 1.9477692514 [101] 1.1232428409 -2.9489727514 -0.6795805973 2.0267382183 2.4688155069 [106] -1.1324186552 -3.4115489732 -0.0978299680 -3.1560365554 -3.3099663820 [111] -2.7337734886 0.3532187655 -0.4862088788 -0.0776345398 -3.1298744883 [116] 1.0817504446 -0.5482857002 4.0285045420 -0.5133768498 0.5340952403 [121] 1.7947726220 -2.3672278235 0.2073172405 -1.7417857667 2.3855235471 [126] 3.1096235412 1.6277215562 -1.5641669607 2.5433241655 1.9975695550 [131] 1.8085055918 > postscript(file="/var/www/html/rcomp/tmp/2grbn1292593450.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/38isq1292593450.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/48isq1292593450.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/58isq1292593450.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/68isq1292593450.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/78isq1292593450.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/85aqz1292593450.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/98s641292593450.tab") > > try(system("convert tmp/1grbn1292593450.ps tmp/1grbn1292593450.png",intern=TRUE)) character(0) > try(system("convert tmp/2grbn1292593450.ps tmp/2grbn1292593450.png",intern=TRUE)) character(0) > try(system("convert tmp/38isq1292593450.ps tmp/38isq1292593450.png",intern=TRUE)) character(0) > try(system("convert tmp/48isq1292593450.ps tmp/48isq1292593450.png",intern=TRUE)) character(0) > try(system("convert tmp/58isq1292593450.ps tmp/58isq1292593450.png",intern=TRUE)) character(0) > try(system("convert tmp/68isq1292593450.ps tmp/68isq1292593450.png",intern=TRUE)) character(0) > try(system("convert tmp/78isq1292593450.ps tmp/78isq1292593450.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 14.299 2.193 45.295