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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 computationThu, 10 Dec 2009 03:59:35 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/10/t12604428758yb400oxnq8y6tt.htm/, Retrieved Fri, 29 Mar 2024 05:53:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65272, Retrieved Fri, 29 Mar 2024 05:53:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2009-12-07 09:18:36] [b98453cac15ba1066b407e146608df68]
- R PD    [ARIMA Backward Selection] [Granger Causality...] [2009-12-10 10:59:35] [cf272a759dc2b193d9a85354803ede7b] [Current]
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Dataseries X:
108.5
112.3
116.6
115.5
120.1
132.9
128.1
129.3
132.5
131
124.9
120.8
122
122.1
127.4
135.2
137.3
135
136
138.4
134.7
138.4
133.9
133.6
141.2
151.8
155.4
156.6
161.6
160.7
156
159.5
168.7
169.9
169.9
185.9
190.8
195.8
211.9
227.1
251.3
256.7
251.9
251.2
270.3
267.2
243
229.9
187.2
178.2
175.2
192.4
187
184
194.1
212.7
217.5
200.5
205.9
196.5
206.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65272&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]3 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=65272&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65272&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1
Estimates ( 1 )-0.07010.1881-0.0580.3931
(p-val)(0.8978 )(0.3704 )(0.6705 )(0.4604 )
Estimates ( 2 )00.1666-0.06120.326
(p-val)(NA )(0.2234 )(0.641 )(0.0133 )
Estimates ( 3 )00.160700.3185
(p-val)(NA )(0.2455 )(NA )(0.0144 )
Estimates ( 4 )0000.2804
(p-val)(NA )(NA )(NA )(0.0127 )
Estimates ( 5 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANA
(p-val)(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 \tabularnewline
Estimates ( 1 ) & -0.0701 & 0.1881 & -0.058 & 0.3931 \tabularnewline
(p-val) & (0.8978 ) & (0.3704 ) & (0.6705 ) & (0.4604 ) \tabularnewline
Estimates ( 2 ) & 0 & 0.1666 & -0.0612 & 0.326 \tabularnewline
(p-val) & (NA ) & (0.2234 ) & (0.641 ) & (0.0133 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.1607 & 0 & 0.3185 \tabularnewline
(p-val) & (NA ) & (0.2455 ) & (NA ) & (0.0144 ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & 0 & 0.2804 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0.0127 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65272&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.0701[/C][C]0.1881[/C][C]-0.058[/C][C]0.3931[/C][/ROW]
[ROW][C](p-val)[/C][C](0.8978 )[/C][C](0.3704 )[/C][C](0.6705 )[/C][C](0.4604 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]0.1666[/C][C]-0.0612[/C][C]0.326[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.2234 )[/C][C](0.641 )[/C][C](0.0133 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.1607[/C][C]0[/C][C]0.3185[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.2455 )[/C][C](NA )[/C][C](0.0144 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0.2804[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0127 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65272&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65272&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
Iterationar1ar2ar3ma1
Estimates ( 1 )-0.07010.1881-0.0580.3931
(p-val)(0.8978 )(0.3704 )(0.6705 )(0.4604 )
Estimates ( 2 )00.1666-0.06120.326
(p-val)(NA )(0.2234 )(0.641 )(0.0133 )
Estimates ( 3 )00.160700.3185
(p-val)(NA )(0.2455 )(NA )(0.0144 )
Estimates ( 4 )0000.2804
(p-val)(NA )(NA )(NA )(0.0127 )
Estimates ( 5 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANA
(p-val)(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.108499938663651
3.57371278875682
3.01971387803031
-2.67033936381383
4.75925319690563
11.4610381200995
-9.18929473165453
2.06974801534877
3.31214914157832
-2.74768588851810
-5.73913126910734
-2.03115984332694
2.82710855834484
-0.141540135003223
5.15224688381121
6.14304046540286
-0.708108529960128
-3.32788202617027
1.72241155324809
2.22103800828805
-4.56804976197941
4.76917310190919
-5.42432683505854
0.83298054008128
8.05782776781791
8.08194650368236
-0.195204887736622
-0.441170597209492
4.56201156533766
-2.54574273203761
-4.69269257067495
5.13915436407112
8.31853374885418
-2.01171443436843
-0.837678307917656
16.0739533347356
-0.219241100151464
2.49874545109503
14.5168054244357
9.77321813113255
18.5002725459758
-2.93450115800601
-7.75417486654806
0.901814666772736
19.5841131621778
-9.22467395812413
-24.3313460354736
-4.85279366283629
-37.2657233889915
4.97347773591625
2.27761013126835
17.9208572245992
-10.6253667891914
-2.37993706684256
11.7257026242498
15.3476693016479
-1.71092712692379
-19.4439818672235
10.8212059862425
-10.1145723856102
12.1535553083339

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.108499938663651 \tabularnewline
3.57371278875682 \tabularnewline
3.01971387803031 \tabularnewline
-2.67033936381383 \tabularnewline
4.75925319690563 \tabularnewline
11.4610381200995 \tabularnewline
-9.18929473165453 \tabularnewline
2.06974801534877 \tabularnewline
3.31214914157832 \tabularnewline
-2.74768588851810 \tabularnewline
-5.73913126910734 \tabularnewline
-2.03115984332694 \tabularnewline
2.82710855834484 \tabularnewline
-0.141540135003223 \tabularnewline
5.15224688381121 \tabularnewline
6.14304046540286 \tabularnewline
-0.708108529960128 \tabularnewline
-3.32788202617027 \tabularnewline
1.72241155324809 \tabularnewline
2.22103800828805 \tabularnewline
-4.56804976197941 \tabularnewline
4.76917310190919 \tabularnewline
-5.42432683505854 \tabularnewline
0.83298054008128 \tabularnewline
8.05782776781791 \tabularnewline
8.08194650368236 \tabularnewline
-0.195204887736622 \tabularnewline
-0.441170597209492 \tabularnewline
4.56201156533766 \tabularnewline
-2.54574273203761 \tabularnewline
-4.69269257067495 \tabularnewline
5.13915436407112 \tabularnewline
8.31853374885418 \tabularnewline
-2.01171443436843 \tabularnewline
-0.837678307917656 \tabularnewline
16.0739533347356 \tabularnewline
-0.219241100151464 \tabularnewline
2.49874545109503 \tabularnewline
14.5168054244357 \tabularnewline
9.77321813113255 \tabularnewline
18.5002725459758 \tabularnewline
-2.93450115800601 \tabularnewline
-7.75417486654806 \tabularnewline
0.901814666772736 \tabularnewline
19.5841131621778 \tabularnewline
-9.22467395812413 \tabularnewline
-24.3313460354736 \tabularnewline
-4.85279366283629 \tabularnewline
-37.2657233889915 \tabularnewline
4.97347773591625 \tabularnewline
2.27761013126835 \tabularnewline
17.9208572245992 \tabularnewline
-10.6253667891914 \tabularnewline
-2.37993706684256 \tabularnewline
11.7257026242498 \tabularnewline
15.3476693016479 \tabularnewline
-1.71092712692379 \tabularnewline
-19.4439818672235 \tabularnewline
10.8212059862425 \tabularnewline
-10.1145723856102 \tabularnewline
12.1535553083339 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65272&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.108499938663651[/C][/ROW]
[ROW][C]3.57371278875682[/C][/ROW]
[ROW][C]3.01971387803031[/C][/ROW]
[ROW][C]-2.67033936381383[/C][/ROW]
[ROW][C]4.75925319690563[/C][/ROW]
[ROW][C]11.4610381200995[/C][/ROW]
[ROW][C]-9.18929473165453[/C][/ROW]
[ROW][C]2.06974801534877[/C][/ROW]
[ROW][C]3.31214914157832[/C][/ROW]
[ROW][C]-2.74768588851810[/C][/ROW]
[ROW][C]-5.73913126910734[/C][/ROW]
[ROW][C]-2.03115984332694[/C][/ROW]
[ROW][C]2.82710855834484[/C][/ROW]
[ROW][C]-0.141540135003223[/C][/ROW]
[ROW][C]5.15224688381121[/C][/ROW]
[ROW][C]6.14304046540286[/C][/ROW]
[ROW][C]-0.708108529960128[/C][/ROW]
[ROW][C]-3.32788202617027[/C][/ROW]
[ROW][C]1.72241155324809[/C][/ROW]
[ROW][C]2.22103800828805[/C][/ROW]
[ROW][C]-4.56804976197941[/C][/ROW]
[ROW][C]4.76917310190919[/C][/ROW]
[ROW][C]-5.42432683505854[/C][/ROW]
[ROW][C]0.83298054008128[/C][/ROW]
[ROW][C]8.05782776781791[/C][/ROW]
[ROW][C]8.08194650368236[/C][/ROW]
[ROW][C]-0.195204887736622[/C][/ROW]
[ROW][C]-0.441170597209492[/C][/ROW]
[ROW][C]4.56201156533766[/C][/ROW]
[ROW][C]-2.54574273203761[/C][/ROW]
[ROW][C]-4.69269257067495[/C][/ROW]
[ROW][C]5.13915436407112[/C][/ROW]
[ROW][C]8.31853374885418[/C][/ROW]
[ROW][C]-2.01171443436843[/C][/ROW]
[ROW][C]-0.837678307917656[/C][/ROW]
[ROW][C]16.0739533347356[/C][/ROW]
[ROW][C]-0.219241100151464[/C][/ROW]
[ROW][C]2.49874545109503[/C][/ROW]
[ROW][C]14.5168054244357[/C][/ROW]
[ROW][C]9.77321813113255[/C][/ROW]
[ROW][C]18.5002725459758[/C][/ROW]
[ROW][C]-2.93450115800601[/C][/ROW]
[ROW][C]-7.75417486654806[/C][/ROW]
[ROW][C]0.901814666772736[/C][/ROW]
[ROW][C]19.5841131621778[/C][/ROW]
[ROW][C]-9.22467395812413[/C][/ROW]
[ROW][C]-24.3313460354736[/C][/ROW]
[ROW][C]-4.85279366283629[/C][/ROW]
[ROW][C]-37.2657233889915[/C][/ROW]
[ROW][C]4.97347773591625[/C][/ROW]
[ROW][C]2.27761013126835[/C][/ROW]
[ROW][C]17.9208572245992[/C][/ROW]
[ROW][C]-10.6253667891914[/C][/ROW]
[ROW][C]-2.37993706684256[/C][/ROW]
[ROW][C]11.7257026242498[/C][/ROW]
[ROW][C]15.3476693016479[/C][/ROW]
[ROW][C]-1.71092712692379[/C][/ROW]
[ROW][C]-19.4439818672235[/C][/ROW]
[ROW][C]10.8212059862425[/C][/ROW]
[ROW][C]-10.1145723856102[/C][/ROW]
[ROW][C]12.1535553083339[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65272&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65272&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
0.108499938663651
3.57371278875682
3.01971387803031
-2.67033936381383
4.75925319690563
11.4610381200995
-9.18929473165453
2.06974801534877
3.31214914157832
-2.74768588851810
-5.73913126910734
-2.03115984332694
2.82710855834484
-0.141540135003223
5.15224688381121
6.14304046540286
-0.708108529960128
-3.32788202617027
1.72241155324809
2.22103800828805
-4.56804976197941
4.76917310190919
-5.42432683505854
0.83298054008128
8.05782776781791
8.08194650368236
-0.195204887736622
-0.441170597209492
4.56201156533766
-2.54574273203761
-4.69269257067495
5.13915436407112
8.31853374885418
-2.01171443436843
-0.837678307917656
16.0739533347356
-0.219241100151464
2.49874545109503
14.5168054244357
9.77321813113255
18.5002725459758
-2.93450115800601
-7.75417486654806
0.901814666772736
19.5841131621778
-9.22467395812413
-24.3313460354736
-4.85279366283629
-37.2657233889915
4.97347773591625
2.27761013126835
17.9208572245992
-10.6253667891914
-2.37993706684256
11.7257026242498
15.3476693016479
-1.71092712692379
-19.4439818672235
10.8212059862425
-10.1145723856102
12.1535553083339



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 0 ;
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')