R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> x <- c(235.1
+ ,280.7
+ ,264.6
+ ,240.7
+ ,201.4
+ ,240.8
+ ,241.1
+ ,223.8
+ ,206.1
+ ,174.7
+ ,203.3
+ ,220.5
+ ,299.5
+ ,347.4
+ ,338.3
+ ,327.7
+ ,351.6
+ ,396.6
+ ,438.8
+ ,395.6
+ ,363.5
+ ,378.8
+ ,357
+ ,369
+ ,464.8
+ ,479.1
+ ,431.3
+ ,366.5
+ ,326.3
+ ,355.1
+ ,331.6
+ ,261.3
+ ,249
+ ,205.5
+ ,235.6
+ ,240.9
+ ,264.9
+ ,253.8
+ ,232.3
+ ,193.8
+ ,177
+ ,213.2
+ ,207.2
+ ,180.6
+ ,188.6
+ ,175.4
+ ,199
+ ,179.6
+ ,225.8
+ ,234
+ ,200.2
+ ,183.6
+ ,178.2
+ ,203.2
+ ,208.5
+ ,191.8
+ ,172.8
+ ,148
+ ,159.4
+ ,154.5
+ ,213.2
+ ,196.4
+ ,182.8
+ ,176.4
+ ,153.6
+ ,173.2
+ ,171
+ ,151.2
+ ,161.9
+ ,157.2
+ ,201.7
+ ,236.4
+ ,356.1
+ ,398.3
+ ,403.7
+ ,384.6
+ ,365.8
+ ,368.1
+ ,367.9
+ ,347
+ ,343.3
+ ,292.9
+ ,311.5
+ ,300.9
+ ,366.9
+ ,356.9
+ ,329.7
+ ,316.2
+ ,269
+ ,289.3
+ ,266.2
+ ,253.6
+ ,233.8
+ ,228.4
+ ,253.6
+ ,260.1
+ ,306.6
+ ,309.2
+ ,309.5
+ ,271
+ ,279.9
+ ,317.9
+ ,298.4
+ ,246.7
+ ,227.3
+ ,209.1
+ ,259.9
+ ,266
+ ,320.6
+ ,308.5
+ ,282.2
+ ,262.7
+ ,263.5
+ ,313.1
+ ,284.3
+ ,252.6
+ ,250.3
+ ,246.5
+ ,312.7
+ ,333.2
+ ,446.4
+ ,511.6
+ ,515.5
+ ,506.4
+ ,483.2
+ ,522.3
+ ,509.8
+ ,460.7
+ ,405.8
+ ,375
+ ,378.5
+ ,406.8
+ ,467.8
+ ,469.8
+ ,429.8
+ ,355.8
+ ,332.7
+ ,378
+ ,360.5
+ ,334.7
+ ,319.5
+ ,323.1
+ ,363.6
+ ,352.1
+ ,411.9
+ ,388.6
+ ,416.4
+ ,360.7
+ ,338
+ ,417.2
+ ,388.4
+ ,371.1
+ ,331.5
+ ,353.7
+ ,396.7
+ ,447
+ ,533.5
+ ,565.4
+ ,542.3
+ ,488.7
+ ,467.1
+ ,531.3
+ ,496.1
+ ,444
+ ,403.4
+ ,386.3
+ ,394.1
+ ,404.1
+ ,462.1
+ ,448.1
+ ,432.3
+ ,386.3
+ ,395.2
+ ,421.9
+ ,382.9
+ ,384.2
+ ,345.5
+ ,323.4
+ ,372.6
+ ,376
+ ,462.7
+ ,487
+ ,444.2
+ ,399.3
+ ,394.9
+ ,455.4
+ ,414
+ ,375.5
+ ,347
+ ,339.4
+ ,385.8
+ ,378.8
+ ,451.8
+ ,446.1
+ ,422.5
+ ,383.1
+ ,352.8
+ ,445.3
+ ,367.5
+ ,355.1
+ ,326.2
+ ,319.8
+ ,331.8
+ ,340.9
+ ,394.1
+ ,417.2
+ ,369.9
+ ,349.2
+ ,321.4
+ ,405.7
+ ,342.9
+ ,316.5
+ ,284.2
+ ,270.9
+ ,288.8
+ ,278.8
+ ,324.4
+ ,310.9
+ ,299
+ ,273
+ ,279.3
+ ,359.2
+ ,305
+ ,282.1
+ ,250.3
+ ,246.5
+ ,257.9
+ ,266.5
+ ,315.9
+ ,318.4
+ ,295.4
+ ,266.4
+ ,245.8
+ ,362.8
+ ,324.9
+ ,294.2
+ ,289.5
+ ,295.2
+ ,290.3
+ ,272
+ ,307.4
+ ,328.7
+ ,292.9
+ ,249.1
+ ,230.4
+ ,361.5
+ ,321.7
+ ,277.2
+ ,260.7
+ ,251
+ ,257.6
+ ,241.8
+ ,287.5
+ ,292.3
+ ,274.7
+ ,254.2
+ ,230
+ ,339
+ ,318.2
+ ,287
+ ,295.8
+ ,284
+ ,271
+ ,262.7
+ ,340.6
+ ,379.4
+ ,373.3
+ ,355.2
+ ,338.4
+ ,466.9
+ ,451
+ ,422
+ ,429.2
+ ,425.9
+ ,460.7
+ ,463.6
+ ,541.4
+ ,544.2
+ ,517.5
+ ,469.4
+ ,439.4
+ ,549
+ ,533
+ ,506.1
+ ,484
+ ,457
+ ,481.5
+ ,469.5
+ ,544.7
+ ,541.2
+ ,521.5
+ ,469.7
+ ,434.4
+ ,542.6
+ ,517.3
+ ,485.7
+ ,465.8
+ ,447
+ ,426.6
+ ,411.6
+ ,467.5
+ ,484.5
+ ,451.2
+ ,417.4
+ ,379.9
+ ,484.7
+ ,455
+ ,420.8
+ ,416.5
+ ,376.3
+ ,405.6
+ ,405.8
+ ,500.8
+ ,514
+ ,475.5
+ ,430.1
+ ,414.4
+ ,538
+ ,526
+ ,488.5
+ ,520.2
+ ,504.4
+ ,568.5
+ ,610.6
+ ,818
+ ,830.9
+ ,835.9
+ ,782
+ ,762.3
+ ,856.9
+ ,820.9
+ ,769.6
+ ,752.2
+ ,724.4
+ ,723.1
+ ,719.5
+ ,817.4
+ ,803.3
+ ,752.5
+ ,689
+ ,630.4
+ ,765.5
+ ,757.7
+ ,732.2
+ ,702.6
+ ,683.3
+ ,709.5
+ ,702.2
+ ,784.8
+ ,810.9
+ ,755.6
+ ,656.8
+ ,615.1
+ ,745.3
+ ,694.1
+ ,675.7
+ ,643.7
+ ,622.1
+ ,634.6
+ ,588
+ ,689.7
+ ,673.9
+ ,647.9
+ ,568.8
+ ,545.7
+ ,632.6
+ ,643.8
+ ,593.1
+ ,579.7
+ ,546
+ ,562.9
+ ,572.5)
> par9 = '1'
> par8 = '0'
> par7 = '1'
> par6 = '0'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '0.5'
> par1 = 'TRUE'
> #'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]
[1,] 0.09504976 -0.7463127
[2,] 0.00000000 -0.7575387
[3,]         NA         NA
[4,]         NA         NA

[[2]]
        [,1] [,2]
[1,] 0.03126    0
[2,]      NA    0
[3,]      NA   NA
[4,]      NA   NA

[[3]]
[[3]][[1]]

Call:
arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, 
    method = "ML")

Coefficients:
        ma1     sma1
      0.095  -0.7463
s.e.  0.044   0.0388

sigma^2 estimated as 0.3034:  log likelihood = -300.19,  aic = 606.38

[[3]][[2]]

Call:
arima(x = series, order = order, seasonal = seasonal, include.mean = include.mean, 
    method = "ML")

Coefficients:
        ma1     sma1
      0.095  -0.7463
s.e.  0.044   0.0388

sigma^2 estimated as 0.3034:  log likelihood = -300.19,  aic = 606.38


$aic
[1] 606.3806 609.0202

> postscript(file="/var/www/html/rcomp/tmp/1fxl81228831415.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 = 372 
Frequency = 1 
  [1]  8.852493e-03  5.059755e-03  2.900308e-03  1.498538e-03 -1.246684e-06
  [6]  1.218873e-03  1.052773e-03  3.875947e-04 -2.267161e-04 -1.287167e-03
 [11] -1.752613e-04 -8.140214e-03 -4.471582e-02 -7.062877e-02  2.004599e-01
 [16]  3.508216e-01  1.546582e+00 -2.771780e-01  8.461906e-01 -4.735224e-01
 [21] -1.315898e-01  1.243299e+00 -1.407958e+00 -8.745954e-02 -8.160144e-02
 [26] -9.472934e-01 -6.030854e-01 -9.580165e-01 -6.248208e-01 -3.619031e-01
 [31] -1.040014e+00 -1.017361e+00  4.010984e-01 -1.047558e+00  8.400503e-01
 [36] -3.315123e-01 -1.486889e+00 -1.131928e+00  6.791113e-02 -3.782009e-01
 [41]  1.247695e-02  2.186672e-01 -3.125160e-01  3.441908e-01  8.158026e-01
 [46]  1.781182e-01  3.212772e-01 -1.033594e+00 -6.601574e-02 -2.770181e-01
 [51] -4.490326e-01  4.877604e-01  3.440976e-01 -2.619530e-01  2.040548e-01
 [56]  5.570609e-01 -4.364276e-01 -2.594690e-01 -1.331983e-01 -1.891482e-01
 [61]  4.367260e-01 -1.091924e+00  4.072406e-01  6.379632e-01 -4.581855e-01
 [66] -2.585402e-01 -1.116232e-01  2.381716e-01  8.186956e-01  4.893904e-01
 [71]  1.029265e+00  1.108705e+00  1.502171e+00  7.511604e-01  7.752986e-01
 [76]  1.810894e-01  9.170399e-02 -9.320869e-01  6.698522e-02  3.862790e-01
 [81]  5.559138e-02 -8.097020e-01 -2.569166e-01 -5.583420e-01 -4.451519e-01
 [86] -6.428188e-01 -1.793558e-01  3.102509e-01 -8.411885e-01 -5.643992e-02
 [91] -6.960034e-01  5.174966e-01 -5.137501e-01  6.865747e-01 -3.599069e-02
 [96]  7.684461e-02 -7.997302e-01 -9.616566e-02  5.718733e-01 -5.901467e-01
[101]  1.099339e+00  2.852244e-01 -4.101157e-01 -8.004533e-01 -2.645408e-01
[106]  6.068390e-02  8.634045e-01 -4.225643e-02 -3.710320e-01 -5.075057e-01
[111] -3.061345e-01  1.696428e-01  5.167693e-01  6.027932e-01 -6.198428e-01
[116]  3.352632e-02  3.004699e-01  4.929379e-01  9.212533e-01  3.239612e-01
[121]  9.672903e-01  1.334132e+00  4.600550e-01  4.502215e-01 -1.916319e-01
[126] -8.562275e-02  1.469963e-01 -1.798176e-01 -1.002817e+00 -1.747638e-01
[131] -1.153807e+00  5.599226e-01 -7.236834e-01 -3.117209e-01 -5.617574e-01
[136] -1.245403e+00 -9.363196e-02  2.614476e-01 -1.015100e-01  3.086469e-01
[141]  1.091286e-01  6.679310e-01  6.668201e-02 -6.884579e-01 -3.628879e-01
[146] -8.777593e-01  1.278186e+00 -6.351761e-01 -8.249752e-02  1.034593e+00
[151] -4.176896e-01  5.107944e-01 -5.817132e-01  1.060493e+00  1.338978e-02
[156]  1.018648e+00  7.835693e-03  5.814054e-01 -3.474929e-01 -1.172458e-01
[161]  1.780177e-02  1.611678e-01 -3.126110e-01 -3.768128e-01 -2.916480e-01
[166] -2.522785e-01 -8.037053e-01 -1.373142e-01 -4.697675e-01 -5.295579e-01
[171] -5.347234e-02 -6.358676e-02  7.301532e-01 -7.247125e-01 -3.486694e-01
[176]  9.649059e-01 -3.629638e-01 -3.484217e-01  5.375311e-01 -3.729633e-01
[181]  4.006001e-01  4.191491e-01 -7.327603e-01  6.215219e-02  1.989411e-01
[186]  2.994879e-01 -3.608004e-01 -2.727073e-01  8.699771e-02  1.051461e-01
[191]  2.658800e-01 -5.323334e-01 -3.839300e-03 -3.505290e-01 -5.804909e-02
[196]  1.116171e-01 -5.372914e-01  1.139814e+00 -1.295063e+00  5.369517e-01
[201] -3.865956e-02  1.153819e-01 -6.916276e-01  1.144311e-01 -4.558229e-01
[206]  4.894793e-01 -7.411195e-01  5.855933e-01 -4.180108e-01  7.474548e-01
[211] -6.492833e-01 -3.034456e-02 -1.370019e-01 -1.247469e-01 -2.934644e-01
[216] -4.806729e-01 -3.610420e-01 -5.849861e-01  3.891712e-01  1.244533e-01
[221]  6.665638e-01  4.907548e-01 -3.420726e-01  2.250605e-02 -1.492821e-01
[226]  1.900091e-01 -4.220507e-01  2.229430e-01 -1.897889e-01  4.790092e-03
[231] -6.733194e-02  3.035162e-02 -3.299073e-01  1.573889e+00  9.591855e-02
[236] -2.208923e-01  7.485801e-01  3.473016e-01 -8.366493e-01 -5.953614e-01
[241] -4.775798e-01  5.625734e-01 -4.625846e-01 -4.041346e-01 -1.658617e-01
[246]  1.631890e+00 -2.670883e-02 -5.694600e-01  2.315729e-01 -1.796274e-01
[251] -2.325540e-01 -4.358009e-01  8.268817e-03 -7.110965e-02  1.944906e-01
[256]  3.480829e-01 -3.594678e-01  6.522801e-01  5.371928e-01 -8.836306e-02
[261]  9.017080e-01 -2.466480e-01 -7.600198e-01 -2.355242e-02  8.189181e-01
[266]  7.515612e-01  4.341577e-01  3.885108e-01  4.628999e-02  4.229204e-01
[271]  6.096682e-01  1.171764e-01  5.722905e-01  9.223147e-02  6.233252e-01
[276]  1.907663e-01  8.066782e-02 -3.514271e-01 -1.151483e-02 -2.869200e-01
[281] -1.632939e-01 -4.089029e-01  5.510215e-01  1.823192e-01 -2.530803e-01
[286] -4.091585e-01  2.495997e-01 -1.797452e-01  2.504934e-02 -3.941271e-01
[291]  1.560761e-01 -3.106096e-01 -2.392257e-01 -3.113821e-01  2.062350e-01
[296]  4.005102e-02 -1.392865e-01 -1.276577e-01 -8.842381e-01 -1.236040e-01
[301] -3.193467e-01  2.027044e-01 -2.733043e-01  1.579491e-01 -3.246312e-01
[306] -1.451336e-01  9.993845e-03 -6.812089e-02  2.564884e-01 -6.993845e-01
[311]  6.266396e-01  1.568005e-01  6.382577e-01 -2.864433e-02 -2.825823e-01
[316] -1.306593e-01  3.386577e-01  1.495975e-01  4.079812e-01 -1.042441e-01
[321]  1.007432e+00  6.131475e-02  1.055556e+00  9.232625e-01  2.056001e+00
[326] -2.398942e-01  7.620799e-01 -7.055598e-02  2.777357e-01 -1.065665e+00
[331]  5.502005e-02 -1.311207e-01 -2.643301e-01 -2.019411e-02 -6.145044e-01
[336] -1.117765e-01 -5.131292e-01 -4.567144e-01 -4.022701e-01 -1.956132e-01
[341] -5.656941e-01  1.749807e-01  4.290175e-01  3.077983e-01 -4.730211e-01
[346]  1.561282e-01  4.570992e-02 -2.017189e-01 -6.231960e-01  3.916676e-01
[351] -4.471576e-01 -8.091679e-01 -4.500780e-02  3.381030e-02 -4.837057e-01
[356]  4.217322e-01 -4.416681e-01  6.223054e-02 -2.078237e-01 -9.327217e-01
[361]  1.074499e-01 -5.266941e-01  2.265490e-01 -4.014275e-01  2.847601e-01
[366] -7.126814e-01  8.852219e-01 -4.717308e-01  9.143377e-02 -2.745839e-01
[371] -1.505282e-02  4.346020e-01
> postscript(file="/var/www/html/rcomp/tmp/20m1m1228831415.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/rcomp/tmp/3bctf1228831415.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/rcomp/tmp/48smb1228831415.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/rcomp/tmp/5v0en1228831415.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/rcomp/tmp/6ayt81228831415.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/rcomp/tmp/7v92a1228831415.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/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/859e41228831415.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/9yk6j1228831415.tab") 
> 
> system("convert tmp/1fxl81228831415.ps tmp/1fxl81228831415.png")
> system("convert tmp/20m1m1228831415.ps tmp/20m1m1228831415.png")
> system("convert tmp/3bctf1228831415.ps tmp/3bctf1228831415.png")
> system("convert tmp/48smb1228831415.ps tmp/48smb1228831415.png")
> system("convert tmp/5v0en1228831415.ps tmp/5v0en1228831415.png")
> system("convert tmp/6ayt81228831415.ps tmp/6ayt81228831415.png")
> system("convert tmp/7v92a1228831415.ps tmp/7v92a1228831415.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.524   1.098   3.026