R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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.

R is a collaborative project with many contributors.
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(122.36
+ ,123.33
+ ,123.04
+ ,124.53
+ ,125.13
+ ,125.85
+ ,126.50
+ ,126.53
+ ,127.07
+ ,124.55
+ ,124.90
+ ,124.32
+ ,122.84
+ ,123.31
+ ,123.31
+ ,124.87
+ ,124.64
+ ,124.73
+ ,124.90
+ ,124.04
+ ,123.28
+ ,123.86
+ ,122.29
+ ,124.09
+ ,124.54
+ ,125.65
+ ,125.70
+ ,125.53
+ ,125.61
+ ,125.55
+ ,125.41
+ ,127.60
+ ,124.68
+ ,124.41
+ ,126.43
+ ,126.38
+ ,125.78
+ ,124.70
+ ,125.07
+ ,125.25
+ ,126.58
+ ,127.13
+ ,125.82
+ ,123.70
+ ,124.39
+ ,123.70
+ ,124.42
+ ,121.05
+ ,121.02
+ ,123.23
+ ,121.32
+ ,120.91
+ ,120.72
+ ,123.31
+ ,119.58
+ ,119.53
+ ,120.59
+ ,118.63
+ ,118.47
+ ,111.81
+ ,114.71
+ ,117.34
+ ,115.77
+ ,118.38
+ ,117.84
+ ,118.83
+ ,120.02
+ ,116.21
+ ,117.08
+ ,120.20
+ ,119.83
+ ,118.92
+ ,118.03
+ ,117.71
+ ,119.55
+ ,116.13
+ ,115.97
+ ,115.99
+ ,114.96
+ ,116.46
+ ,116.55
+ ,113.05
+ ,117.44
+ ,118.84
+ ,117.06
+ ,117.54
+ ,119.31
+ ,118.72
+ ,121.55
+ ,122.61
+ ,121.53
+ ,123.31
+ ,124.07
+ ,123.59
+ ,122.97
+ ,123.22
+ ,123.04
+ ,122.96
+ ,122.81
+ ,122.81
+ ,122.62
+ ,120.82
+ ,119.41
+ ,121.56
+ ,121.59
+ ,118.50
+ ,118.77
+ ,118.86
+ ,117.60
+ ,119.90
+ ,121.83
+ ,121.84
+ ,122.12
+ ,122.12
+ ,121.36
+ ,119.66
+ ,119.32
+ ,120.36
+ ,117.06
+ ,117.48
+ ,115.60
+ ,113.86
+ ,116.92
+ ,117.75
+ ,117.75
+ ,115.31
+ ,116.28
+ ,115.22
+ ,115.65
+ ,115.11
+ ,118.67
+ ,118.04
+ ,116.50
+ ,119.78
+ ,119.95
+ ,120.37
+ ,119.79
+ ,119.43
+ ,121.06
+ ,121.74
+ ,121.09
+ ,122.97
+ ,120.50
+ ,117.18
+ ,115.03
+ ,113.36
+ ,112.59
+ ,111.65
+ ,111.98
+ ,114.87
+ ,114.67
+ ,114.09
+ ,114.77
+ ,117.05
+ ,117.22
+ ,113.18
+ ,110.95
+ ,112.14
+ ,112.72
+ ,110.01
+ ,110.29
+ ,110.74
+ ,110.32
+ ,105.89
+ ,108.97
+ ,109.34
+ ,106.57
+ ,99.49
+ ,101.81
+ ,104.29
+ ,109.73
+ ,105.06
+ ,107.97
+ ,108.13
+ ,109.86
+ ,108.95
+ ,111.20
+ ,110.69
+ ,106.10
+ ,105.68
+ ,104.12
+ ,104.71
+ ,104.30
+ ,103.52
+ ,107.76
+ ,107.80
+ ,107.30
+ ,108.64
+ ,105.03
+ ,108.30
+ ,107.21
+ ,109.27
+ ,109.50
+ ,111.68
+ ,111.80
+ ,111.75
+ ,106.68
+ ,106.37
+ ,105.76
+ ,109.01
+ ,109.01
+ ,109.01
+ ,109.01
+ ,107.69
+ ,105.19
+ ,105.48
+ ,102.22
+ ,100.54
+ ,105.00
+ ,105.44
+ ,107.89
+ ,108.64
+ ,106.70
+ ,109.10
+ ,105.23
+ ,108.41
+ ,108.80
+ ,110.39
+ ,110.22
+ ,110.86
+ ,108.58
+ ,107.70
+ ,106.62
+ ,109.84
+ ,107.16
+ ,107.26
+ ,108.70
+ ,109.85
+ ,109.41
+ ,112.36
+ ,111.03
+ ,110.67
+ ,109.21
+ ,113.58
+ ,113.88
+ ,114.08
+ ,112.33
+ ,113.92
+ ,114.41
+ ,114.57
+ ,115.35
+ ,113.13
+ ,113.29
+ ,112.56
+ ,113.06
+ ,113.46
+ ,115.39
+ ,116.62
+ ,117.04
+ ,117.42
+ ,115.62
+ ,115.16
+ ,115.69
+ ,112.85
+ ,114.05
+ ,112.00
+ ,113.74
+ ,116.26
+ ,118.63
+ ,116.49
+ ,118.23
+ ,116.83
+ ,118.82
+ ,114.36
+ ,112.02
+ ,113.24
+ ,109.75
+ ,110.33
+ ,112.86
+ ,113.04
+ ,113.80
+ ,110.90
+ ,109.96
+ ,108.69
+ ,108.84
+ ,108.47
+ ,108.07
+ ,107.94
+ ,108.11
+ ,108.11
+ ,106.81
+ ,105.58
+ ,105.61
+ ,106.52
+ ,103.86
+ ,104.60
+ ,104.73
+ ,105.12
+ ,104.76
+ ,103.85
+ ,103.83
+ ,103.22
+ ,101.64
+ ,102.13
+ ,104.33
+ ,104.92
+ ,107.78
+ ,104.49
+ ,102.80
+ ,102.86
+ ,104.51
+ ,104.73
+ ,102.58
+ ,99.93
+ ,101.41
+ ,101.05
+ ,99.86
+ ,101.11
+ ,100.89
+ ,101.09
+ ,98.31
+ ,98.08
+ ,99.55
+ ,99.62
+ ,97.37
+ ,98.16
+ ,97.98
+ ,98.15
+ ,97.10
+ ,97.24
+ ,96.70
+ ,96.64
+ ,100.65
+ ,96.75
+ ,97.74
+ ,97.92
+ ,98.34
+ ,93.84
+ ,97.80
+ ,96.20
+ ,95.99
+ ,95.18
+ ,95.95
+ ,92.23
+ ,91.78
+ ,92.97
+ ,89.76
+ ,92.88
+ ,96.23
+ ,95.79
+ ,93.97
+ ,93.90
+ ,93.60
+ ,93.96
+ ,88.69
+ ,88.57
+ ,85.62
+ ,86.25
+ ,85.33
+ ,83.33
+ ,77.78
+ ,78.70
+ ,72.05
+ ,80.75
+ ,81.41
+ ,82.65
+ ,75.85
+ ,75.70
+ ,78.25
+ ,77.41
+ ,76.84
+ ,74.25
+ ,74.95
+ ,68.78
+ ,73.21
+ ,73.26
+ ,78.67
+ ,75.63
+ ,74.99
+ ,83.87
+ ,79.62
+ ,80.13
+ ,79.76
+ ,78.20
+ ,78.05
+ ,79.05
+ ,73.32
+ ,75.17
+ ,73.26
+ ,73.72
+ ,73.57
+ ,70.60
+ ,71.25
+ ,74.22
+ ,73.32
+ ,73.01
+ ,74.21
+ ,75.32
+ ,71.73
+ ,71.94
+ ,72.94
+ ,72.47
+ ,71.94
+ ,74.30
+ ,74.30)
> par9 = '1'
> par8 = '2'
> par7 = '1'
> par6 = '3'
> par5 = '1'
> par4 = '0'
> par3 = '1'
> 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,] 0.7004162 0.2619634 -0.2887440 -0.4947819 0.02713884 -0.3224219
 [2,] 0.6739276 0.2620525 -0.2863938 -0.4684447 0.00000000 -0.3294362
 [3,] 0.4896255 0.3603398 -0.1708048  0.0000000 0.00000000 -0.2618429
 [4,]        NA        NA         NA         NA         NA         NA
 [5,]        NA        NA         NA         NA         NA         NA
 [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,] -0.4947819
 [2,] -0.4684447
 [3,] -0.7477063
 [4,]         NA
 [5,]         NA
 [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,] 0.00769 0.01225 0.00174 0.33580 0.8573 0.00098 0.33580
 [2,] 0.00319 0.01087 0.00178 0.47767     NA 0.00020 0.47767
 [3,] 0.01387 0.00104 0.03282      NA     NA 0.00585 0.00014
 [4,]      NA      NA      NA      NA     NA      NA      NA
 [5,]      NA      NA      NA      NA     NA      NA      NA
 [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
      0.7004  0.2620  -0.2887  -0.4948  0.0271  -0.3224  -0.4948
s.e.  0.2614  0.1041   0.0916   0.5134  0.1508   0.0970   0.5134

sigma^2 estimated as 4.069:  log likelihood = -835.62,  aic = 1687.24

[[3]][[2]]

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

Coefficients:
         ar1     ar2      ar3      ma1    sar1     sar2     sma1
      0.7004  0.2620  -0.2887  -0.4948  0.0271  -0.3224  -0.4948
s.e.  0.2614  0.1041   0.0916   0.5134  0.1508   0.0970   0.5134

sigma^2 estimated as 4.069:  log likelihood = -835.62,  aic = 1687.24

[[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
      0.6739  0.2621  -0.2864  -0.4684     0  -0.3294  -0.4684
s.e.  0.2271  0.1024   0.0910   0.6591     0   0.0876   0.6591

sigma^2 estimated as 4.07:  log likelihood = -835.63,  aic = 1685.27

[[3]][[4]]
NULL

[[3]][[5]]
NULL

[[3]][[6]]
NULL

[[3]][[7]]
NULL


$aic
[1] 1687.238 1685.270 1684.118

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
> postscript(file="/var/www/html/rcomp/tmp/17ffm1229896707.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 = 395 
Frequency = 1 
  [1]  0.122359934  0.931905596 -0.055775726  1.468985842  1.055123054
  [6]  0.944996527  1.068459063  0.316141992  0.760539013 -2.202172787
 [11] -0.131541222 -0.532225208 -1.741322456  0.204725666 -0.147865680
 [16]  1.396025514  0.162345262  0.015666288  0.217673744 -0.917485781
 [21] -0.903482714  0.370579584 -1.528186349  1.425442787  0.823775929
 [26]  1.164161248  0.548322957 -0.145197036  0.144017288 -0.008729191
 [31] -0.044489033  2.265084751 -2.300246922 -0.802974525  1.905081539
 [36]  0.141274919 -0.274558080 -1.089390206 -0.106056039  0.153839731
 [41]  1.378581169  0.984071326 -1.093840755 -2.335854693  0.019621557
 [46] -0.773231734  0.531269082 -3.130461108 -1.063832193  1.864411469
 [51] -1.765417043 -0.680077917 -0.465236069  2.087200592 -3.083702974
 [56] -0.865873388  0.852274976 -2.170886096 -0.425425937 -6.885803333
 [61]  0.797289131  2.743745263 -1.351254567  2.723775171 -0.265750755
 [66]  0.628597530  1.685367043 -3.512412580  0.213872453  3.188286520
 [71]  0.271502856 -0.398481712 -0.968285809 -0.812342066  1.714817302
 [76] -2.910379943 -0.863810636 -0.211153272 -1.471741884  1.321517273
 [81]  0.263207098 -3.586107810  3.524639579  2.073870189 -1.349714920
 [86]  0.647566687  1.610374519 -0.205131167  3.117975634  1.983810868
 [91] -0.574765128  2.005568966  1.261019545 -0.024940312 -0.237550537
 [96]  0.211769365 -0.068298575  0.020922774 -0.049293556 -0.046741896
[101] -0.199106302 -1.867970285 -1.910721165  1.572337220  0.278962986
[106] -3.002325459 -0.429295243 -0.360255037 -1.544442043  2.074267330
[111]  2.268283376  0.470843049  0.612021235  0.088860143 -0.741153473
[116] -1.702438314 -0.701120741  0.848784970 -3.118554905 -0.340791575
[121] -2.084450086 -2.657138306  2.483969471  1.092183691  0.214914712
[126] -2.252555448  0.081925764 -1.090015923  0.091750871 -0.302970998
[131]  3.266721370  0.272180955 -1.509282258  3.118011256  0.709336378
[136]  0.797929140  0.024986737 -0.510022431  1.672039357  1.178117084
[141] -0.195061581  2.003888355 -1.991462993 -3.780993818 -2.943062048
[146] -2.755028959 -1.547871004 -1.464064146 -0.355533211  2.484193199
[151]  0.128415145 -0.645551321  0.493286866  2.155228978  0.842823904
[156] -3.609266348 -3.028955229  0.253121456  0.469935287 -2.414710740
[161] -0.332056729  0.021685149 -0.723476787 -4.492503053  1.736093193
[166]  0.568688187 -2.866460743 -7.457541373 -0.113158144  2.050206431
[171]  5.688885370 -2.789872356  1.853020511  0.587480939  1.425742419
[176]  0.245486421  2.215449852  0.306220314 -4.486786592 -1.245225093
[181] -2.081228318 -0.102783496 -0.231230469 -1.155246159  3.839470343
[186]  0.756141438 -0.334518553  1.501939916 -3.503250490  2.545823756
[191] -0.249883320  1.826896494  1.183287202  2.178453749  0.982881985
[196]  0.192963947 -4.748270309 -1.546277280 -0.957344448  2.771840184
[201]  0.988635391  0.046129962  0.058912641 -1.613051854 -2.827961953
[206] -0.409646068 -3.472100498 -2.673525090  3.739603237  0.940574309
[211]  2.715237703  1.590585661 -1.901195126  2.168243541 -3.291257961
[216]  2.431989507  1.379622969  1.660956700  0.803375241  0.635350965
[221] -1.935088158 -1.364117077 -1.288076747  2.763029923 -1.850949651
[226] -0.426026674  1.416557927  1.054518309  0.100735725  3.018210605
[231] -0.582956689 -0.465121334 -1.278449270  3.852169169  1.516126435
[236]  0.679012976 -1.187703544  1.025404593  0.921134111  0.473278620
[241]  1.218772205 -1.967712687 -0.284285454 -0.739335743  0.177296649
[246]  0.617737556  1.963704391  1.770795540  0.862954725  0.736632609
[251] -1.576707386 -0.757226076  0.459526189 -2.740545624  0.642872965
[256] -1.942390746  0.969546743  2.916260560  2.804886241 -1.177268053
[261]  1.442032870 -1.008278560  1.685049169 -3.575122896 -3.361699988
[266]  0.544584540 -3.812969288 -0.255479308  2.361074697  0.231398228
[271]  0.961587337 -2.766404865 -1.889328557 -1.722861957 -0.492311616
[276] -0.378296718 -0.661148805 -0.391162940 -0.147328237 -0.155177726
[281] -1.410345582 -1.656751963 -0.498315548  0.635940363 -2.533052392
[286]  0.066689344  0.010332959  0.100325649 -0.159212040 -1.118767758
[291] -0.360505710 -0.794670844 -1.826370124  0.020284677  2.059692363
[296]  1.014687146  3.219689747 -2.433588054 -2.372305349 -0.370562103
[301]  1.266050396  0.824590573 -1.853000435 -3.161641656  0.442928319
[306] -0.428942210 -1.310961721  1.014820062 -0.305111109  0.018050634
[311] -2.696505889 -1.107643021  1.141463952  0.159322694 -2.055277552
[316]  0.202726697 -0.367170684 -0.056145345 -0.897022304 -0.261083326
[321] -0.651847074 -0.339317082  3.954885709 -2.972658465  0.291576179
[326]  0.387897774  0.061920969 -4.019271224  2.799937552 -0.961853011
[331] -0.689421377 -0.528085968  0.088821992 -3.596177221 -1.464789543
[336]  0.759085864 -3.392064053  2.383755653  3.773197179  0.172713955
[341] -1.373663785 -0.554513572 -0.616706791  0.338243087 -4.969502210
[346] -1.459225866 -3.458393446 -0.708572169 -0.967101476 -2.764883774
[351] -6.358398381 -1.216800723 -7.378603167  6.326254252  2.244552581
[356]  1.064937853 -5.865035378 -2.548311712  1.936102797 -0.678053253
[361] -0.197816088 -2.763460078 -0.426953645 -6.333456209  2.598461871
[366]  0.687910739  5.047098077 -1.221071427 -1.439612118  8.787593236
[371] -2.386724706  0.539774154  0.369474608 -2.149167481  0.019333116
[376]  1.016869042 -5.485953366  0.506223805 -1.934615077 -0.521110903
[381]  0.055715779 -3.494649978 -0.275821333  2.610697245 -0.486156828
[386] -0.247611392  1.077842842  1.091541130 -3.133113798 -0.451611547
[391]  0.819605490 -0.476519148 -0.344001389  2.175325103  0.390570383
> postscript(file="/var/www/html/rcomp/tmp/2bbfz1229896707.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/3drkn1229896707.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/4ccok1229896707.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/5jpnm1229896707.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/6d6521229896707.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/79fk61229896707.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/8hnwc1229896707.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/9y2cm1229896707.tab") 
> 
> system("convert tmp/17ffm1229896707.ps tmp/17ffm1229896707.png")
> system("convert tmp/2bbfz1229896707.ps tmp/2bbfz1229896707.png")
> system("convert tmp/3drkn1229896707.ps tmp/3drkn1229896707.png")
> system("convert tmp/4ccok1229896707.ps tmp/4ccok1229896707.png")
> system("convert tmp/5jpnm1229896707.ps tmp/5jpnm1229896707.png")
> system("convert tmp/6d6521229896707.ps tmp/6d6521229896707.png")
> system("convert tmp/79fk61229896707.ps tmp/79fk61229896707.png")
> 
> 
> proc.time()
   user  system elapsed 
  7.185   2.630   7.568