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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationTue, 14 Dec 2010 20:11:10 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/14/t129235734682jokwxljonc9bz.htm/, Retrieved Thu, 02 May 2024 19:41:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110127, Retrieved Thu, 02 May 2024 19:41:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact133
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMP         [Standard Deviation-Mean Plot] [Births] [2010-11-29 10:52:49] [b98453cac15ba1066b407e146608df68]
- RMP           [(Partial) Autocorrelation Function] [WS6 - autocorrelatie] [2010-12-14 19:09:35] [8ed0bd3560b9ca2814a2ed0a29182575]
- RMP             [ARIMA Backward Selection] [WS6 - ARIMA] [2010-12-14 20:02:51] [8ed0bd3560b9ca2814a2ed0a29182575]
-   P                 [ARIMA Backward Selection] [WS6 - ARIMA] [2010-12-14 20:11:10] [c9d5faca36bd2ada281161976df30bf1] [Current]
-   PD                  [ARIMA Backward Selection] [Dollar] [2010-12-26 18:41:40] [8ed0bd3560b9ca2814a2ed0a29182575]
-   PD                  [ARIMA Backward Selection] [Yuan] [2010-12-26 18:56:42] [8ed0bd3560b9ca2814a2ed0a29182575]
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Dataseries X:
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110127&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110127&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110127&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationma1sma1
Estimates ( 1 )0.28190.0967
(p-val)(0.0115 )(0.5065 )
Estimates ( 2 )0.28670
(p-val)(0.0091 )(NA )
Estimates ( 3 )NANA
(p-val)(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ma1 & sma1 \tabularnewline
Estimates ( 1 ) & 0.2819 & 0.0967 \tabularnewline
(p-val) & (0.0115 ) & (0.5065 ) \tabularnewline
Estimates ( 2 ) & 0.2867 & 0 \tabularnewline
(p-val) & (0.0091 ) & (NA ) \tabularnewline
Estimates ( 3 ) & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110127&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ma1[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.2819[/C][C]0.0967[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0115 )[/C][C](0.5065 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.2867[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0091 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110127&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110127&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

ARIMA Parameter Estimation and Backward Selection
Iterationma1sma1
Estimates ( 1 )0.28190.0967
(p-val)(0.0115 )(0.5065 )
Estimates ( 2 )0.28670
(p-val)(0.0091 )(NA )
Estimates ( 3 )NANA
(p-val)(NA )(NA )







Estimated ARIMA Residuals
Value
9.25199187349125
35.4431526077437
-55.2430106414591
64.2834921629593
-272.925350059468
189.407783210307
-156.91119930471
-568.911284913756
-551.312762120859
-449.770100992454
36.2068921896828
-298.862294595804
148.373903516348
-177.126486076251
-340.654524230306
172.328346684357
55.9367065629444
-179.583594564251
-381.397370201889
689.485924887796
-285.875549451592
587.796191756399
66.0112690911104
-184.825420698669
382.927728387463
-270.836655203312
213.102246201808
270.552143332641
33.6305523945287
478.355294296119
393.918022327040
-431.299806535454
76.4261952601543
439.425161555443
-4.26853538390034
688.270815443423
-17.9972160772676
247.820854091193
472.921056112502
228.725789618158
-61.0996808343572
0.0635815372659525
466.865746412940
399.354547995092
587.790547087017
-105.255596766082
-299.891460489539
346.116761349116
95.416579184982
405.636081975011
-195.816651205628
-105.799865517923
425.497671220934
-52.2851448788474
190.604267767676
-133.058459619837
612.80250019599
81.4138275349699
382.909499611329
14.7738158243778
501.179312558274
384.908267910514
371.373757629384
-353.122932261400

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
9.25199187349125 \tabularnewline
35.4431526077437 \tabularnewline
-55.2430106414591 \tabularnewline
64.2834921629593 \tabularnewline
-272.925350059468 \tabularnewline
189.407783210307 \tabularnewline
-156.91119930471 \tabularnewline
-568.911284913756 \tabularnewline
-551.312762120859 \tabularnewline
-449.770100992454 \tabularnewline
36.2068921896828 \tabularnewline
-298.862294595804 \tabularnewline
148.373903516348 \tabularnewline
-177.126486076251 \tabularnewline
-340.654524230306 \tabularnewline
172.328346684357 \tabularnewline
55.9367065629444 \tabularnewline
-179.583594564251 \tabularnewline
-381.397370201889 \tabularnewline
689.485924887796 \tabularnewline
-285.875549451592 \tabularnewline
587.796191756399 \tabularnewline
66.0112690911104 \tabularnewline
-184.825420698669 \tabularnewline
382.927728387463 \tabularnewline
-270.836655203312 \tabularnewline
213.102246201808 \tabularnewline
270.552143332641 \tabularnewline
33.6305523945287 \tabularnewline
478.355294296119 \tabularnewline
393.918022327040 \tabularnewline
-431.299806535454 \tabularnewline
76.4261952601543 \tabularnewline
439.425161555443 \tabularnewline
-4.26853538390034 \tabularnewline
688.270815443423 \tabularnewline
-17.9972160772676 \tabularnewline
247.820854091193 \tabularnewline
472.921056112502 \tabularnewline
228.725789618158 \tabularnewline
-61.0996808343572 \tabularnewline
0.0635815372659525 \tabularnewline
466.865746412940 \tabularnewline
399.354547995092 \tabularnewline
587.790547087017 \tabularnewline
-105.255596766082 \tabularnewline
-299.891460489539 \tabularnewline
346.116761349116 \tabularnewline
95.416579184982 \tabularnewline
405.636081975011 \tabularnewline
-195.816651205628 \tabularnewline
-105.799865517923 \tabularnewline
425.497671220934 \tabularnewline
-52.2851448788474 \tabularnewline
190.604267767676 \tabularnewline
-133.058459619837 \tabularnewline
612.80250019599 \tabularnewline
81.4138275349699 \tabularnewline
382.909499611329 \tabularnewline
14.7738158243778 \tabularnewline
501.179312558274 \tabularnewline
384.908267910514 \tabularnewline
371.373757629384 \tabularnewline
-353.122932261400 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110127&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]9.25199187349125[/C][/ROW]
[ROW][C]35.4431526077437[/C][/ROW]
[ROW][C]-55.2430106414591[/C][/ROW]
[ROW][C]64.2834921629593[/C][/ROW]
[ROW][C]-272.925350059468[/C][/ROW]
[ROW][C]189.407783210307[/C][/ROW]
[ROW][C]-156.91119930471[/C][/ROW]
[ROW][C]-568.911284913756[/C][/ROW]
[ROW][C]-551.312762120859[/C][/ROW]
[ROW][C]-449.770100992454[/C][/ROW]
[ROW][C]36.2068921896828[/C][/ROW]
[ROW][C]-298.862294595804[/C][/ROW]
[ROW][C]148.373903516348[/C][/ROW]
[ROW][C]-177.126486076251[/C][/ROW]
[ROW][C]-340.654524230306[/C][/ROW]
[ROW][C]172.328346684357[/C][/ROW]
[ROW][C]55.9367065629444[/C][/ROW]
[ROW][C]-179.583594564251[/C][/ROW]
[ROW][C]-381.397370201889[/C][/ROW]
[ROW][C]689.485924887796[/C][/ROW]
[ROW][C]-285.875549451592[/C][/ROW]
[ROW][C]587.796191756399[/C][/ROW]
[ROW][C]66.0112690911104[/C][/ROW]
[ROW][C]-184.825420698669[/C][/ROW]
[ROW][C]382.927728387463[/C][/ROW]
[ROW][C]-270.836655203312[/C][/ROW]
[ROW][C]213.102246201808[/C][/ROW]
[ROW][C]270.552143332641[/C][/ROW]
[ROW][C]33.6305523945287[/C][/ROW]
[ROW][C]478.355294296119[/C][/ROW]
[ROW][C]393.918022327040[/C][/ROW]
[ROW][C]-431.299806535454[/C][/ROW]
[ROW][C]76.4261952601543[/C][/ROW]
[ROW][C]439.425161555443[/C][/ROW]
[ROW][C]-4.26853538390034[/C][/ROW]
[ROW][C]688.270815443423[/C][/ROW]
[ROW][C]-17.9972160772676[/C][/ROW]
[ROW][C]247.820854091193[/C][/ROW]
[ROW][C]472.921056112502[/C][/ROW]
[ROW][C]228.725789618158[/C][/ROW]
[ROW][C]-61.0996808343572[/C][/ROW]
[ROW][C]0.0635815372659525[/C][/ROW]
[ROW][C]466.865746412940[/C][/ROW]
[ROW][C]399.354547995092[/C][/ROW]
[ROW][C]587.790547087017[/C][/ROW]
[ROW][C]-105.255596766082[/C][/ROW]
[ROW][C]-299.891460489539[/C][/ROW]
[ROW][C]346.116761349116[/C][/ROW]
[ROW][C]95.416579184982[/C][/ROW]
[ROW][C]405.636081975011[/C][/ROW]
[ROW][C]-195.816651205628[/C][/ROW]
[ROW][C]-105.799865517923[/C][/ROW]
[ROW][C]425.497671220934[/C][/ROW]
[ROW][C]-52.2851448788474[/C][/ROW]
[ROW][C]190.604267767676[/C][/ROW]
[ROW][C]-133.058459619837[/C][/ROW]
[ROW][C]612.80250019599[/C][/ROW]
[ROW][C]81.4138275349699[/C][/ROW]
[ROW][C]382.909499611329[/C][/ROW]
[ROW][C]14.7738158243778[/C][/ROW]
[ROW][C]501.179312558274[/C][/ROW]
[ROW][C]384.908267910514[/C][/ROW]
[ROW][C]371.373757629384[/C][/ROW]
[ROW][C]-353.122932261400[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110127&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110127&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
9.25199187349125
35.4431526077437
-55.2430106414591
64.2834921629593
-272.925350059468
189.407783210307
-156.91119930471
-568.911284913756
-551.312762120859
-449.770100992454
36.2068921896828
-298.862294595804
148.373903516348
-177.126486076251
-340.654524230306
172.328346684357
55.9367065629444
-179.583594564251
-381.397370201889
689.485924887796
-285.875549451592
587.796191756399
66.0112690911104
-184.825420698669
382.927728387463
-270.836655203312
213.102246201808
270.552143332641
33.6305523945287
478.355294296119
393.918022327040
-431.299806535454
76.4261952601543
439.425161555443
-4.26853538390034
688.270815443423
-17.9972160772676
247.820854091193
472.921056112502
228.725789618158
-61.0996808343572
0.0635815372659525
466.865746412940
399.354547995092
587.790547087017
-105.255596766082
-299.891460489539
346.116761349116
95.416579184982
405.636081975011
-195.816651205628
-105.799865517923
425.497671220934
-52.2851448788474
190.604267767676
-133.058459619837
612.80250019599
81.4138275349699
382.909499611329
14.7738158243778
501.179312558274
384.908267910514
371.373757629384
-353.122932261400



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 1 ; par8 = 0 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 0 ; par7 = 1 ; par8 = 0 ; par9 = 1 ;
R code (references can be found in the software module):
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]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$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='mytable.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='mytable1.tab')