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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 = '6' > par4 = '1' > par3 = '0' > par2 = '-2.0' > 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,] 0.07398851 0.1235555 0.08803031 0.07079477 -0.2781440 0.6872962 [2,] 0.14404255 0.1131051 0.07973084 0.00000000 -0.2782708 0.6874290 [3,] 0.15006799 0.1154499 0.00000000 0.00000000 -0.2690926 0.6947023 [4,] 0.15407154 0.0000000 0.00000000 0.00000000 -0.2738699 0.6925123 [5,] 0.00000000 0.0000000 0.00000000 0.00000000 -0.2644183 0.7081285 [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,] -0.9999959 [2,] -0.9999994 [3,] -1.0000018 [4,] -1.0000294 [5,] -0.9999507 [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,] 0.92013 0.39975 0.49140 0.9238 0.00163 0 0 [2,] 0.13190 0.24480 0.43109 NA 0.00161 0 0 [3,] 0.11779 0.23792 NA NA 0.00199 0 0 [4,] 0.11118 NA NA NA 0.00171 0 0 [5,] NA NA NA NA 0.00273 0 0 [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 0.0740 0.1236 0.0880 0.0708 -0.2781 0.6873 -1.0000 s.e. 0.7364 0.1462 0.1276 0.7386 0.0863 0.0868 0.0784 sigma^2 estimated as 0.001738: log likelihood = 206.46, aic = -396.92 [[3]][[2]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.0740 0.1236 0.0880 0.0708 -0.2781 0.6873 -1.0000 s.e. 0.7364 0.1462 0.1276 0.7386 0.0863 0.0868 0.0784 sigma^2 estimated as 0.001738: log likelihood = 206.46, aic = -396.92 [[3]][[3]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.144 0.1131 0.0797 0 -0.2783 0.6874 -1.0000 s.e. 0.095 0.0968 0.1009 0 0.0863 0.0868 0.0784 sigma^2 estimated as 0.001738: log likelihood = 206.46, aic = -398.91 [[3]][[4]] Call: arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, fixed = last.arma$next.vector, method = "ML") Coefficients: ar1 ar2 ar3 ma1 sar1 sar2 sma1 0.1501 0.1154 0 0 -0.2691 0.6947 -1.0000 s.e. 0.0953 0.0974 0 0 0.0852 0.0861 0.0783 sigma^2 estimated as 0.001753: log likelihood = 206.14, aic = -400.29 [[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 0.1541 0 0 0 -0.2739 0.6925 -1.0000 s.e. 0.0960 0 0 0 0.0855 0.0865 0.0772 sigma^2 estimated as 0.001765: log likelihood = 205.44, aic = -400.88 [[3]][[6]] NULL [[3]][[7]] NULL $aic [1] -396.9185 -398.9101 -400.2873 -400.8827 -400.3181 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 log(s2) : NaNs produced 3: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE 4: In log(s2) : NaNs produced 5: In arima(series, order = order, seasonal = seasonal, include.mean = include.mean, : some AR parameters were fixed: setting transform.pars = FALSE > postscript(file="/var/wessaorg/rcomp/tmp/1iuyy1323878509.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] 6.278754e-04 3.291492e-04 2.592420e-04 9.420677e-05 4.054865e-05 [6] 4.091393e-05 -9.986201e-02 -3.449941e-02 -3.010987e-02 2.720220e-03 [11] 4.593065e-02 1.817016e-02 -1.257131e-01 -9.554325e-02 -2.374170e-02 [16] 8.741672e-03 -7.739417e-03 -6.477243e-03 -1.518317e-02 -2.318967e-02 [21] -9.817720e-03 3.444706e-03 -7.884146e-03 -4.318093e-02 4.247534e-02 [26] -1.297668e-02 -7.002817e-02 7.074848e-03 -7.251335e-03 -1.520403e-02 [31] -1.158706e-02 -2.531643e-02 -6.866282e-03 1.793870e-03 -6.137025e-02 [36] -1.956517e-02 -7.508623e-02 2.267188e-02 -1.645438e-02 2.519090e-02 [41] -9.402690e-03 -2.499878e-02 -4.129086e-02 9.511387e-03 -2.746997e-02 [46] 2.682287e-02 -4.243329e-03 -4.443046e-02 -1.118760e-02 2.326668e-02 [51] 7.351688e-03 -2.576154e-02 -1.100721e-02 -1.415924e-02 -1.925752e-02 [56] -1.583618e-03 -1.237785e-02 1.255566e-02 1.021635e-02 -3.251270e-02 [61] -4.565153e-02 -1.534998e-02 4.202245e-02 -8.675863e-03 -7.776894e-03 [66] -1.044174e-02 -3.273641e-02 -2.008629e-03 8.931308e-03 -3.613685e-03 [71] -1.791120e-02 2.177061e-02 -2.857498e-02 -5.638428e-02 1.066184e-02 [76] -7.036110e-04 -1.155501e-02 -9.526585e-03 -2.918131e-02 -1.472971e-02 [81] 7.255560e-03 -9.513037e-03 -4.782805e-04 -1.722367e-03 1.328193e-01 [86] 2.485567e-03 -3.385562e-02 3.360712e-02 -1.627394e-02 -7.143825e-03 [91] 2.257769e-02 -2.090488e-02 -1.722017e-03 1.021311e-02 1.718889e-02 [96] 3.872442e-03 2.034033e-01 -2.528493e-02 2.843212e-02 3.059359e-02 [101] -1.605999e-03 -1.216011e-02 5.287213e-02 -1.762947e-02 6.398205e-03 [106] 2.036301e-02 8.171509e-03 2.787230e-02 -2.227591e-01 5.127542e-02 [111] -4.837043e-02 5.580625e-03 -1.951321e-03 -3.456758e-03 -2.283353e-02 [116] 5.460946e-03 -2.619712e-02 2.742218e-02 4.294420e-02 -6.398337e-02 [121] -3.642077e-02 -6.801600e-03 1.219347e-02 -3.614485e-02 4.509994e-03 [126] -8.366988e-03 -5.187968e-02 1.427058e-03 -1.376151e-02 -2.239151e-03 [131] 2.270607e-02 -2.224773e-03 > postscript(file="/var/wessaorg/rcomp/tmp/21pqj1323878509.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/wessaorg/rcomp/tmp/3jyci1323878509.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/wessaorg/rcomp/tmp/4zzq51323878509.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/wessaorg/rcomp/tmp/5i86c1323878509.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/wessaorg/rcomp/tmp/6dn1p1323878509.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/wessaorg/rcomp/tmp/7nmql1323878509.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/wessaorg/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab > load(file="/var/wessaorg/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/wessaorg/rcomp/tmp/8z4ph1323878509.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/wessaorg/rcomp/tmp/9znf11323878509.tab") > > try(system("convert tmp/1iuyy1323878509.ps tmp/1iuyy1323878509.png",intern=TRUE)) character(0) > try(system("convert tmp/21pqj1323878509.ps tmp/21pqj1323878509.png",intern=TRUE)) character(0) > try(system("convert tmp/3jyci1323878509.ps tmp/3jyci1323878509.png",intern=TRUE)) character(0) > try(system("convert tmp/4zzq51323878509.ps tmp/4zzq51323878509.png",intern=TRUE)) character(0) > try(system("convert tmp/5i86c1323878509.ps tmp/5i86c1323878509.png",intern=TRUE)) character(0) > try(system("convert tmp/6dn1p1323878509.ps tmp/6dn1p1323878509.png",intern=TRUE)) character(0) > try(system("convert tmp/7nmql1323878509.ps tmp/7nmql1323878509.png",intern=TRUE)) character(0) > > > proc.time() user system elapsed 5.018 0.403 5.445