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Type 'q()' to quit R. > x <- c(1770,2203,2836,1976,2150,2180,2631,1781,2327,2260,2051,2250,2102,2957,2485,2871,2447,2570,2622,1840,2682,2369,2119,2531,2214,3206,2709,2734,2348,2702,2642,2064,2647,2534,2297,2718,2321,3112,2664,2808,2668,2934,2616,2228,2463,2416,2407,2582,2101,3305,2818,2401,3019,2507,2948,2210,2467,2596,2451,2233,2393,3122,2801,2656,2782,2604,2803,2178,2324,2536,2408,2261,2166,3243,2296,2719,2734,2297,2732,1904,2397,2473,1967,2471,2203,3053,2350,2807,2639,2646,2577,1860,2624,2590,2261,3342,2840,3328,3245,3025,2915,3579,2787,2397,3065,2154,2689,3187,2540,3469,3005,2573,2998,2768,2556,2414,2467,2136,2493,2735,2316,3042,2364,2248,2714,2583,2631,1965,2209,1964,2132) > 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,] -0.12009155 0.3394023 0.5829004 0.09309274 0.2815152 -0.10015165 [2,] -0.05221874 0.3300473 0.5564446 0.00000000 0.2698022 -0.09079464 [3,] 0.00000000 0.3207387 0.5336060 0.00000000 0.2699572 -0.10148697 [4,] 0.00000000 0.3253228 0.5424928 0.00000000 0.3234777 0.00000000 [5,] 0.00000000 0.3238810 0.5597406 0.00000000 0.0000000 0.00000000 [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,] -0.6766519 [2,] -0.6775902 [3,] -0.6893543 [4,] -0.7663601 [5,] -0.5185038 [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,] 0.39629 3e-05 0 0.57134 0.12764 0.43537 0.00016 [2,] 0.51822 3e-05 0 NA 0.14466 0.47804 0.00019 [3,] NA 5e-05 0 NA 0.13075 0.41875 0.00008 [4,] NA 2e-05 0 NA 0.05736 NA 0.00000 [5,] NA 1e-05 0 NA NA NA 0.00005 [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 -0.1201 0.3394 0.5829 0.0931 0.2815 -0.1002 -0.6767 s.e. 0.1411 0.0777 0.0913 0.1640 0.1835 0.1280 0.1740 sigma^2 estimated as 45961: log likelihood = -810.58, aic = 1637.16 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.1201 0.3394 0.5829 0.0931 0.2815 -0.1002 -0.6767 s.e. 0.1411 0.0777 0.0913 0.1640 0.1835 0.1280 0.1740 sigma^2 estimated as 45961: log likelihood = -810.58, aic = 1637.16 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 -0.0522 0.3300 0.5564 0 0.2698 -0.0908 -0.6776 s.e. 0.0806 0.0768 0.0843 0 0.1838 0.1276 0.1759 sigma^2 estimated as 46104: log likelihood = -810.73, aic = 1635.46 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0 0.3207 0.5336 0 0.2700 -0.1015 -0.6894 s.e. 0 0.0759 0.0771 0 0.1775 0.1251 0.1696 sigma^2 estimated as 46112: log likelihood = -810.94, aic = 1633.88 [[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 0.3253 0.5425 0 0.3235 0 -0.7664 s.e. 0 0.0742 0.0747 0 0.1686 0 0.1453 sigma^2 estimated as 46361: log likelihood = -811.24, aic = 1632.49 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] 1637.162 1635.463 1633.879 1632.485 1634.217 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 > postscript(file="/var/www/rcomp/tmp/1z8rj1323972933.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] 1.769998 2.202997 2.835994 1.975992 2.149991 2.179989 [7] 2.630987 1.780987 2.326985 2.259985 2.050985 2.249983 [13] 230.179516 478.218512 -472.170973 444.014660 39.169005 295.565333 [19] -517.159942 -153.420799 155.297895 100.532377 21.895022 38.257605 [25] 66.212387 271.329811 -80.666274 -113.744330 -265.220705 159.863609 [31] -39.059609 198.131870 -44.797144 117.250147 112.915092 157.646812 [37] -1.691476 -87.656080 -239.978547 52.408374 285.269976 323.954694 [43] -231.209601 -13.870069 -289.552214 -95.363287 138.009117 62.917067 [49] -176.410228 192.796193 175.877159 -321.035913 279.854991 -208.306605 [55] 315.783297 -51.480625 10.421690 -14.298098 128.672954 -356.301578 [61] 108.995309 -6.427291 109.856370 27.244377 15.818807 -11.395072 [67] -108.569194 48.321846 -180.633942 16.519424 99.968362 -11.465791 [73] -154.994356 159.558892 -386.294084 116.000776 104.678382 -69.612273 [79] -105.268826 -131.884770 172.202934 66.908133 -245.518292 140.312677 [85] 154.101390 58.362775 -220.717916 170.211615 53.810411 254.586467 [91] -222.382480 -158.067280 134.046006 244.914113 158.951430 753.122648 [97] 525.344033 -116.145299 70.951859 -128.275501 -119.537498 476.486592 [103] -111.655781 -2.211459 -58.924736 -605.548597 35.916308 93.492814 [109] 44.071434 -75.149073 -73.668310 -362.772752 46.155706 -290.849766 [115] -98.931107 206.646409 -84.853589 -127.087369 18.896372 31.283424 [121] -51.228445 -217.873120 -360.312849 -233.849290 163.974336 218.247569 [127] 271.889083 -152.768054 -220.151486 -196.793299 -15.633445 > postscript(file="/var/www/rcomp/tmp/2tv7f1323972933.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/3csl31323972933.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/4w68l1323972933.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/5ehjj1323972933.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/6orjl1323972933.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/7k3191323972933.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/8v4qu1323972933.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/9cie61323972933.tab") > > try(system("convert tmp/1z8rj1323972933.ps tmp/1z8rj1323972933.png",intern=TRUE)) character(0) > try(system("convert tmp/2tv7f1323972933.ps tmp/2tv7f1323972933.png",intern=TRUE)) character(0) > try(system("convert tmp/3csl31323972933.ps tmp/3csl31323972933.png",intern=TRUE)) character(0) > try(system("convert tmp/4w68l1323972933.ps tmp/4w68l1323972933.png",intern=TRUE)) character(0) > try(system("convert tmp/5ehjj1323972933.ps tmp/5ehjj1323972933.png",intern=TRUE)) character(0) > try(system("convert tmp/6orjl1323972933.ps tmp/6orjl1323972933.png",intern=TRUE)) character(0) > try(system("convert tmp/7k3191323972933.ps tmp/7k3191323972933.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 7.930 0.430 8.383