R version 2.15.2 (2012-10-26) -- "Trick or Treat"
Copyright (C) 2012 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i686-pc-linux-gnu (32-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
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 = '2'
> par5 = '12'
> par4 = '1'
> par3 = '1'
> par2 = '0.5'
> par1 = 'FALSE'
> par9 <- '1'
> par8 <- '0'
> par7 <- '1'
> par6 <- '2'
> 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]       [,3]       [,4]
[1,] 0.4618559 0.1881573 -0.3767971 -0.7209662
[2,] 0.1094252 0.2417225  0.0000000 -0.7239618
[3,]        NA        NA         NA         NA
[4,]        NA        NA         NA         NA
[5,]        NA        NA         NA         NA
[6,]        NA        NA         NA         NA
[7,]        NA        NA         NA         NA
[8,]        NA        NA         NA         NA

[[2]]
        [,1]    [,2]    [,3] [,4]
[1,] 0.00772 0.00444 0.03062    0
[2,] 0.03483 0.00000      NA    0
[3,]      NA      NA      NA   NA
[4,]      NA      NA      NA   NA
[5,]      NA      NA      NA   NA
[6,]      NA      NA      NA   NA
[7,]      NA      NA      NA   NA
[8,]      NA      NA      NA   NA

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

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

Coefficients:
         ar1     ar2      ma1     sma1
      0.4619  0.1882  -0.3768  -0.7210
s.e.  0.1724  0.0657   0.1736   0.0403

sigma^2 estimated as 0.2826:  log likelihood = -287.01,  aic = 584.02

[[3]][[2]]

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

Coefficients:
         ar1     ar2      ma1     sma1
      0.4619  0.1882  -0.3768  -0.7210
s.e.  0.1724  0.0657   0.1736   0.0403

sigma^2 estimated as 0.2826:  log likelihood = -287.01,  aic = 584.02

[[3]][[3]]
NULL

[[3]][[4]]
NULL


$aic
[1] 584.0182 584.7512

> postscript(file="/var/wessaorg/rcomp/tmp/1l2mf1356095157.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 = 372 
Frequency = 1 
  [1]  8.852493e-03  5.059755e-03  2.900304e-03  1.498531e-03 -1.256034e-06
  [6]  1.218862e-03  1.052758e-03  3.875784e-04 -2.267322e-04 -1.287178e-03
 [11] -1.752666e-04 -8.140184e-03 -4.471360e-02 -6.885108e-02  1.997589e-01
 [16]  3.692377e-01  1.528910e+00 -3.624614e-01  4.563328e-01 -5.801910e-01
 [21] -3.643333e-01  1.274504e+00 -1.354406e+00 -3.635987e-01  1.657852e-01
 [26] -8.506014e-01 -5.594346e-01 -7.318979e-01 -4.028139e-01 -4.777244e-02
 [31] -7.768497e-01 -8.393535e-01  7.089711e-01 -6.996897e-01  8.950863e-01
 [36] -9.563425e-02 -1.579748e+00 -1.097236e+00  4.170399e-01  4.399976e-02
 [41]  1.674963e-01  3.652383e-01 -2.517252e-01  3.188371e-01  8.657342e-01
 [46]  1.241892e-01  9.033296e-02 -1.205157e+00 -1.832147e-01 -6.327399e-02
 [51] -3.487333e-01  5.934741e-01  4.927996e-01 -3.148441e-01  1.008702e-01
 [56]  5.730393e-01 -4.943568e-01 -4.212258e-01 -1.190858e-01 -1.170128e-01
 [61]  5.203324e-01 -9.931171e-01  3.379836e-01  8.531171e-01 -4.576100e-01
 [66] -4.136073e-01 -7.664371e-02  3.064280e-01  8.750792e-01  4.686372e-01
 [71]  8.231426e-01  9.157088e-01  1.203669e+00  4.162212e-01  2.734559e-01
 [76] -2.245001e-01 -2.438462e-01 -1.106470e+00 -3.260916e-02  5.490573e-01
 [81]  1.031717e-01 -8.842838e-01 -3.336791e-01 -4.147714e-01 -3.351512e-01
 [86] -4.450114e-01 -1.285758e-02  5.195569e-01 -6.969808e-01 -4.985990e-02
 [91] -5.142523e-01  5.836686e-01 -3.333191e-01  6.628372e-01  5.619179e-02
 [96] -4.115250e-02 -8.531897e-01 -1.130915e-01  7.345073e-01 -5.064136e-01
[101]  1.023427e+00  4.110214e-01 -6.002464e-01 -9.989668e-01 -2.527664e-01
[106]  2.522996e-01  1.005711e+00  1.022469e-02 -5.372931e-01 -5.636392e-01
[111] -2.751127e-01  3.074550e-01  6.426454e-01  6.334669e-01 -7.039232e-01
[116] -1.618179e-01  3.607365e-01  5.110949e-01  8.358577e-01  1.898919e-01
[121]  7.229498e-01  1.181068e+00  1.649139e-01  1.001649e-02 -5.027618e-01
[126] -3.183456e-01  1.343474e-01 -1.686548e-01 -1.043407e+00 -1.783223e-01
[131] -9.572278e-01  6.909304e-01 -3.953689e-01 -3.118096e-01 -4.153787e-01
[136] -1.114443e+00  7.773776e-02  6.268228e-01  1.057953e-01  3.257092e-01
[141]  1.543544e-01  5.980115e-01  1.143042e-02 -8.847083e-01 -4.528632e-01
[146] -7.580322e-01  1.412506e+00 -3.404431e-01 -2.723563e-01  1.073834e+00
[151] -3.416846e-01  2.887131e-01 -5.701784e-01  9.208646e-01  9.906183e-02
[156]  8.235731e-01 -6.090907e-02  3.328373e-01 -4.972308e-01 -2.743574e-01
[161]  2.113201e-02  1.566608e-01 -2.806950e-01 -4.221163e-01 -2.169526e-01
[166] -1.829301e-01 -6.980858e-01 -5.551922e-02 -2.275266e-01 -3.953953e-01
[171]  8.779047e-02  1.508352e-01  8.241890e-01 -6.994428e-01 -4.807034e-01
[176]  1.062533e+00 -2.019202e-01 -5.430026e-01  5.493115e-01 -2.913247e-01
[181]  3.306251e-01  4.825714e-01 -8.085212e-01 -5.840598e-02  2.912891e-01
[186]  3.357773e-01 -3.605631e-01 -3.839467e-01  1.514645e-01  1.865257e-01
[191]  2.793241e-01 -5.495425e-01 -7.433185e-02 -2.608155e-01 -5.350426e-03
[196]  2.208017e-01 -5.021890e-01  1.117714e+00 -1.133976e+00  3.102387e-01
[201]  1.768605e-01  8.139486e-02 -7.114253e-01  9.133186e-02 -3.046663e-01
[206]  5.317625e-01 -6.099822e-01  5.228668e-01 -2.778089e-01  6.365589e-01
[211] -5.313007e-01 -1.916499e-01 -5.078028e-02 -8.532339e-02 -2.343266e-01
[216] -4.260803e-01 -2.661415e-01 -4.493919e-01  5.522497e-01  3.134449e-01
[221]  6.672941e-01  4.155198e-01 -4.538000e-01 -1.738808e-01 -1.455313e-01
[226]  1.847805e-01 -3.729144e-01  2.072469e-01 -8.968992e-02 -4.690309e-03
[231] -3.009311e-02  2.947832e-02 -3.222507e-01  1.528908e+00  2.373914e-01
[236] -5.445941e-01  5.811913e-01  3.306690e-01 -9.868250e-01 -7.545722e-01
[241] -3.444285e-01  7.595260e-01 -2.600335e-01 -4.678034e-01 -9.401963e-02
[246]  1.688628e+00  1.150402e-01 -8.898803e-01  6.760167e-02 -1.172027e-01
[251] -2.095071e-01 -3.824105e-01  9.105597e-02  4.174729e-02  2.569540e-01
[256]  3.893184e-01 -3.844693e-01  4.585321e-01  6.046555e-01 -1.772483e-01
[261]  7.099304e-01 -3.033944e-01 -9.538530e-01 -4.829912e-02  9.915259e-01
[266]  8.259561e-01  2.849965e-01  1.397351e-01 -1.444100e-01  1.663067e-01
[271]  5.309249e-01  2.375065e-02  3.538001e-01 -1.128040e-02  4.997442e-01
[276]  1.219306e-01 -1.142156e-01 -5.042808e-01 -9.042123e-02 -2.433439e-01
[281] -1.258285e-01 -4.003773e-01  6.047487e-01  3.320809e-01 -3.505574e-01
[286] -4.885617e-01  2.951547e-01 -5.658705e-02  4.788402e-03 -3.710905e-01
[291]  1.589281e-01 -2.166490e-01 -2.240680e-01 -2.826264e-01  2.645149e-01
[296]  1.575222e-01 -1.511062e-01 -1.284814e-01 -8.483837e-01 -7.955638e-02
[301] -1.078177e-01  3.280712e-01 -1.547682e-01  1.657990e-01 -2.477126e-01
[306] -1.788642e-01  5.109253e-02 -1.893171e-03  2.757804e-01 -6.586736e-01
[311]  6.013209e-01  3.105611e-01  5.583857e-01 -1.060503e-01 -4.611521e-01
[316] -2.058090e-01  3.923786e-01  1.900885e-01  3.326048e-01 -1.604788e-01
[321]  8.769193e-01  6.980712e-02  8.243015e-01  8.117983e-01  1.761167e+00
[326] -5.600995e-01  1.574285e-01 -2.712664e-01  2.981501e-02 -1.168446e+00
[331] -7.600718e-02  7.624422e-02 -2.131331e-01  5.542423e-02 -5.451060e-01
[336] -9.701878e-02 -4.145618e-01 -3.541642e-01 -2.474885e-01 -1.731777e-02
[341] -4.015748e-01  3.025489e-01  5.905919e-01  3.501612e-01 -5.743603e-01
[346]  5.271312e-02  1.100624e-01 -2.184773e-01 -6.717923e-01  4.432230e-01
[351] -2.729799e-01 -8.230821e-01  4.648026e-02  2.661244e-01 -4.001714e-01
[356]  4.438339e-01 -3.127849e-01  2.422148e-02 -1.307289e-01 -9.159207e-01
[361]  1.464654e-01 -2.859447e-01  3.168503e-01 -2.230992e-01  3.017623e-01
[366] -6.187474e-01  8.454459e-01 -3.307901e-01 -3.717502e-02 -2.268285e-01
[371] -1.213929e-02  5.168911e-01
> postscript(file="/var/wessaorg/rcomp/tmp/2xg2p1356095157.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/3xobx1356095157.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/4dsh41356095157.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/5gyz21356095157.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/6pc5p1356095157.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/745lm1356095157.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/84k6s1356095157.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/9qjty1356095157.tab") 
> 
> try(system("convert tmp/1l2mf1356095157.ps tmp/1l2mf1356095157.png",intern=TRUE))
character(0)
> try(system("convert tmp/2xg2p1356095157.ps tmp/2xg2p1356095157.png",intern=TRUE))
character(0)
> try(system("convert tmp/3xobx1356095157.ps tmp/3xobx1356095157.png",intern=TRUE))
character(0)
> try(system("convert tmp/4dsh41356095157.ps tmp/4dsh41356095157.png",intern=TRUE))
character(0)
> try(system("convert tmp/5gyz21356095157.ps tmp/5gyz21356095157.png",intern=TRUE))
character(0)
> try(system("convert tmp/6pc5p1356095157.ps tmp/6pc5p1356095157.png",intern=TRUE))
character(0)
> try(system("convert tmp/745lm1356095157.ps tmp/745lm1356095157.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  7.556   1.156   8.807