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
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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 = '1'
> par7 = '0'
> par6 = '0'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '0.5'
> 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]
[1,] -0.067983 -0.7231683
[2,]  0.000000 -1.3200646
[3,]        NA         NA
[4,]        NA         NA

[[2]]
        [,1] [,2]
[1,] 0.35325    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:
         sar1     sma1
      -0.0680  -0.7232
s.e.   0.0731   0.0556

sigma^2 estimated as 0.3063:  log likelihood = -302.09,  aic = 610.17

[[3]][[2]]

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

Coefficients:
         sar1     sma1
      -0.0680  -0.7232
s.e.   0.0731   0.0556

sigma^2 estimated as 0.3063:  log likelihood = -302.09,  aic = 610.17


$aic
[1] 610.1710 609.0208

Warning message:
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/1m4vd1228852887.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  5.059756e-03  2.900309e-03  1.498539e-03 -1.245353e-06
  [6]  1.218875e-03  1.052774e-03  3.875966e-04 -2.267144e-04 -1.287166e-03
 [11] -1.752609e-04 -8.140220e-03 -4.471614e-02 -6.938430e-02  1.894648e-01
 [16]  3.616280e-01  1.544694e+00 -1.272687e-01  8.015646e-01 -3.843203e-01
 [21] -1.726538e-01  1.203348e+00 -1.261010e+00 -2.163424e-01 -8.393531e-02
 [26] -9.566643e-01 -6.850741e-01 -1.000478e+00 -6.564068e-01 -4.256563e-01
 [31] -1.042772e+00 -1.129575e+00  2.975135e-01 -9.625982e-01  6.917724e-01
 [36] -2.595940e-01 -1.504948e+00 -1.316443e+00 -7.717967e-02 -4.287584e-01
 [41] -8.359404e-02  2.014010e-01 -3.584794e-01  2.657616e-01  8.682226e-01
 [46]  1.869559e-01  3.957463e-01 -1.014292e+00 -2.212871e-01 -3.364716e-01
 [51] -4.724892e-01  4.351376e-01  3.818802e-01 -2.123474e-01  1.686034e-01
 [56]  6.109137e-01 -3.429241e-01 -2.898923e-01 -1.360621e-01 -2.480212e-01
 [61]  4.428996e-01 -1.043044e+00  2.850847e-01  7.102660e-01 -3.844039e-01
 [66] -3.124125e-01 -1.212209e-01  2.647820e-01  8.121574e-01  5.492803e-01
 [71]  1.069273e+00  1.214915e+00  1.657155e+00  8.645312e-01  8.725041e-01
 [76]  2.889523e-01  8.044990e-02 -9.349043e-01 -2.622633e-02  4.019644e-01
 [81]  1.301874e-01 -7.767392e-01 -2.805380e-01 -5.108443e-01 -4.090349e-01
 [86] -6.123274e-01 -1.981153e-01  2.959721e-01 -8.079641e-01 -1.724642e-01
 [91] -6.995286e-01  4.652601e-01 -4.755930e-01  5.925905e-01 -1.667230e-03
 [96]  3.779326e-02 -8.323559e-01 -1.961237e-01  5.399483e-01 -5.335194e-01
[101]  1.003926e+00  4.042001e-01 -4.129382e-01 -8.290171e-01 -3.746891e-01
[106]  7.272453e-02  8.655665e-01  4.686356e-02 -4.151720e-01 -5.335377e-01
[111] -3.332390e-01  1.030863e-01  5.931237e-01  6.872099e-01 -5.663388e-01
[116] -7.881651e-02  2.871831e-01  5.179335e-01  1.003889e+00  4.148749e-01
[121]  9.925651e-01  1.417199e+00  5.577698e-01  5.004816e-01 -1.340795e-01
[126] -6.862032e-02  1.301074e-01 -1.631400e-01 -1.001087e+00 -2.509454e-01
[131] -1.145081e+00  4.693688e-01 -6.106000e-01 -2.951699e-01 -5.681001e-01
[136] -1.279131e+00 -2.350792e-01  2.403275e-01 -4.823473e-02  2.948111e-01
[141]  9.074467e-02  6.536404e-01  4.670773e-02 -6.689672e-01 -4.659004e-01
[146] -9.400582e-01  1.155346e+00 -5.829658e-01 -1.615518e-01  1.034631e+00
[151] -3.093184e-01  4.899627e-01 -5.097737e-01  1.031658e+00  1.190550e-01
[156]  9.760157e-01  9.225513e-02  5.403931e-01 -2.370812e-01 -1.575809e-01
[161] -2.514737e-03  2.018773e-01 -3.005101e-01 -3.869075e-01 -3.415415e-01
[166] -2.501332e-01 -8.256827e-01 -1.650553e-01 -4.653946e-01 -5.320164e-01
[171] -1.376522e-01 -5.464299e-02  7.250455e-01 -6.718905e-01 -4.170385e-01
[176]  9.038375e-01 -2.690917e-01 -4.248938e-01  4.629334e-01 -3.480294e-01
[181]  3.507840e-01  4.324482e-01 -7.072406e-01  7.188818e-03  2.376799e-01
[186]  2.687510e-01 -3.356206e-01 -2.647744e-01  6.708098e-02  8.008972e-02
[191]  3.105032e-01 -5.295868e-01 -2.395228e-02 -3.191752e-01 -1.275379e-01
[196]  1.198680e-01 -5.304938e-01  1.100382e+00 -1.182837e+00  3.833555e-01
[201]  3.322046e-02  1.121231e-01 -6.688421e-01  2.598876e-02 -4.438720e-01
[206]  4.301603e-01 -6.896990e-01  5.302488e-01 -3.991226e-01  7.488177e-01
[211] -6.117917e-01 -7.998007e-02 -1.273579e-01 -1.382133e-01 -3.412995e-01
[216] -4.970786e-01 -4.231492e-01 -5.930610e-01  3.105970e-01  1.900817e-01
[221]  6.622754e-01  5.603310e-01 -2.854139e-01 -2.639852e-02 -1.449845e-01
[226]  1.636535e-01 -4.091679e-01  1.658589e-01 -1.761372e-01 -4.467869e-02
[231] -3.329019e-02  2.576447e-02 -2.898743e-01  1.535972e+00  2.688476e-01
[236] -2.194069e-01  7.296459e-01  4.245305e-01 -8.100092e-01 -6.531561e-01
[241] -5.265462e-01  5.252785e-01 -4.047468e-01 -4.539099e-01 -2.264935e-01
[246]  1.652438e+00  1.712724e-01 -5.865066e-01  2.190275e-01 -1.433791e-01
[251] -2.697736e-01 -4.817304e-01 -4.304006e-02 -4.060452e-02  1.759373e-01
[256]  3.410622e-01 -3.350185e-01  6.385777e-01  6.234958e-01 -6.269825e-02
[261]  8.944218e-01 -1.789433e-01 -7.684526e-01 -9.931289e-02  8.351459e-01
[266]  8.214983e-01  5.262389e-01  4.505036e-01  7.270212e-02  3.878392e-01
[271]  6.877953e-01  1.841088e-01  6.152854e-01  1.343893e-01  6.204828e-01
[276]  2.664045e-01  1.504558e-01 -3.087160e-01 -1.684228e-02 -2.723537e-01
[281] -1.778793e-01 -4.651758e-01  5.383227e-01  2.509354e-01 -2.318759e-01
[286] -4.273240e-01  2.704067e-01 -1.280968e-01  8.314988e-03 -4.238984e-01
[291]  1.112516e-01 -3.191689e-01 -2.723418e-01 -4.043023e-01  1.855540e-01
[296]  7.303727e-02 -1.728493e-01 -1.635017e-01 -8.758230e-01 -2.071374e-01
[301] -3.354975e-01  1.489749e-01 -2.517643e-01  1.164043e-01 -3.145981e-01
[306] -2.201908e-01 -1.541819e-02 -6.672510e-02  2.288754e-01 -6.756904e-01
[311]  5.241408e-01  2.153387e-01  6.347280e-01  4.272120e-02 -3.010700e-01
[316] -1.478524e-01  3.223899e-01  1.588830e-01  4.046668e-01 -7.118160e-02
[321]  1.000322e+00  1.334578e-01  1.105929e+00  1.043090e+00  2.176512e+00
[326] -4.404305e-02  7.279736e-01 -4.888856e-03  2.985831e-01 -1.038750e+00
[331] -3.982717e-02 -1.295521e-01 -2.427271e-01 -2.110458e-02 -5.650397e-01
[336] -1.211666e-01 -4.370821e-01 -5.087959e-01 -4.062321e-01 -2.306673e-01
[341] -5.687607e-01  6.576286e-02  4.268138e-01  3.436055e-01 -4.821515e-01
[346]  1.186772e-01  1.621869e-02 -2.214902e-01 -7.119426e-01  3.096312e-01
[351] -4.388939e-01 -8.599502e-01 -1.506406e-01  4.759733e-02 -4.717834e-01
[356]  3.941783e-01 -4.351968e-01  3.296469e-02 -1.999774e-01 -9.701305e-01
[361] -3.374781e-02 -4.924180e-01  1.597725e-01 -4.122049e-01  2.500121e-01
[366] -6.773767e-01  7.796242e-01 -3.751055e-01  2.679585e-02 -2.614306e-01
[371] -5.188069e-02  3.877099e-01
> postscript(file="/var/www/html/rcomp/tmp/21sca1228852887.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/3zoqh1228852887.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/4bint1228852887.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/50ya21228852887.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/6aotm1228852887.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/7shnk1228852887.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/8tyo41228852887.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/9nj011228852887.tab") 
> 
> system("convert tmp/1m4vd1228852887.ps tmp/1m4vd1228852887.png")
> system("convert tmp/21sca1228852887.ps tmp/21sca1228852887.png")
> system("convert tmp/3zoqh1228852887.ps tmp/3zoqh1228852887.png")
> system("convert tmp/4bint1228852887.ps tmp/4bint1228852887.png")
> system("convert tmp/50ya21228852887.ps tmp/50ya21228852887.png")
> system("convert tmp/6aotm1228852887.ps tmp/6aotm1228852887.png")
> system("convert tmp/7shnk1228852887.ps tmp/7shnk1228852887.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.390   1.151   2.737