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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 computationWed, 04 Dec 2013 15:20:24 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/04/t1386188450dnw33eihqn3bx34.htm/, Retrieved Fri, 29 Mar 2024 09:51:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=230792, Retrieved Fri, 29 Mar 2024 09:51:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2008-12-08 19:22:39] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMP   [ARIMA Backward Selection] [] [2011-12-06 19:59:13] [b98453cac15ba1066b407e146608df68]
- R         [ARIMA Backward Selection] [] [2013-12-04 20:20:24] [12aa97dfde985f95a295900f01959e26] [Current]
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Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 13 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230792&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230792&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230792&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 time13 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1sar1sar2sma1
Estimates ( 1 )-0.2546-0.6236-0.35830.0553
(p-val)(0.0479 )(0.3026 )(0.2078 )(0.9324 )
Estimates ( 2 )-0.2532-0.5739-0.33750
(p-val)(0.0476 )(0 )(0.0331 )(NA )
Estimates ( 3 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
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 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.2546 & -0.6236 & -0.3583 & 0.0553 \tabularnewline
(p-val) & (0.0479 ) & (0.3026 ) & (0.2078 ) & (0.9324 ) \tabularnewline
Estimates ( 2 ) & -0.2532 & -0.5739 & -0.3375 & 0 \tabularnewline
(p-val) & (0.0476 ) & (0 ) & (0.0331 ) & (NA ) \tabularnewline
Estimates ( 3 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \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=230792&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.2546[/C][C]-0.6236[/C][C]-0.3583[/C][C]0.0553[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0479 )[/C][C](0.3026 )[/C][C](0.2078 )[/C][C](0.9324 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.2532[/C][C]-0.5739[/C][C]-0.3375[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0476 )[/C][C](0 )[/C][C](0.0331 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/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 ( 4 )[/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 ( 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=230792&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230792&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
Iterationar1sar1sar2sma1
Estimates ( 1 )-0.2546-0.6236-0.35830.0553
(p-val)(0.0479 )(0.3026 )(0.2078 )(0.9324 )
Estimates ( 2 )-0.2532-0.5739-0.33750
(p-val)(0.0476 )(0 )(0.0331 )(NA )
Estimates ( 3 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
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
867.887523349344
-2281.19235728193
33995.4062612879
9358.87058578244
24.1779685670895
23874.675258339
19813.1686532612
267978.217710728
-114267.214620167
-40265.306508172
73786.350086527
29000.1997827658
-76434.0156682785
-20664.0087744748
-50658.493382462
23501.6403174908
100262.20162646
-185115.892788044
88358.9504684404
-164419.034226518
124021.529714842
185074.446181391
112403.506649358
104376.88770705
136706.372582719
83407.2435349616
29494.659130923
-21038.6356843677
-27161.476938448
-107933.932032702
-102445.035617911
-153461.719808505
27650.9499343898
47573.6827366892
-3435.74188907323
-32393.1581258751
-9778.24359202163
-48930.9351671306
56212.035198271
63865.4211866991
-175593.61251862
61686.9599393504
-12215.4393794863
-115544.539966771
151857.999525218
64734.4898074722
6285.31098228317
8638.47590946478
-1569.06672539419
27036.8827216908
-45406.3731457943
-82079.2131424946
-30290.0039515995
-50933.0724551137
-52161.8966121828
-49100.5170625373
44943.2612207427
-20436.2833597664
-20192.3975896362
-33876.5705349554
-35416.4142591565

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
867.887523349344 \tabularnewline
-2281.19235728193 \tabularnewline
33995.4062612879 \tabularnewline
9358.87058578244 \tabularnewline
24.1779685670895 \tabularnewline
23874.675258339 \tabularnewline
19813.1686532612 \tabularnewline
267978.217710728 \tabularnewline
-114267.214620167 \tabularnewline
-40265.306508172 \tabularnewline
73786.350086527 \tabularnewline
29000.1997827658 \tabularnewline
-76434.0156682785 \tabularnewline
-20664.0087744748 \tabularnewline
-50658.493382462 \tabularnewline
23501.6403174908 \tabularnewline
100262.20162646 \tabularnewline
-185115.892788044 \tabularnewline
88358.9504684404 \tabularnewline
-164419.034226518 \tabularnewline
124021.529714842 \tabularnewline
185074.446181391 \tabularnewline
112403.506649358 \tabularnewline
104376.88770705 \tabularnewline
136706.372582719 \tabularnewline
83407.2435349616 \tabularnewline
29494.659130923 \tabularnewline
-21038.6356843677 \tabularnewline
-27161.476938448 \tabularnewline
-107933.932032702 \tabularnewline
-102445.035617911 \tabularnewline
-153461.719808505 \tabularnewline
27650.9499343898 \tabularnewline
47573.6827366892 \tabularnewline
-3435.74188907323 \tabularnewline
-32393.1581258751 \tabularnewline
-9778.24359202163 \tabularnewline
-48930.9351671306 \tabularnewline
56212.035198271 \tabularnewline
63865.4211866991 \tabularnewline
-175593.61251862 \tabularnewline
61686.9599393504 \tabularnewline
-12215.4393794863 \tabularnewline
-115544.539966771 \tabularnewline
151857.999525218 \tabularnewline
64734.4898074722 \tabularnewline
6285.31098228317 \tabularnewline
8638.47590946478 \tabularnewline
-1569.06672539419 \tabularnewline
27036.8827216908 \tabularnewline
-45406.3731457943 \tabularnewline
-82079.2131424946 \tabularnewline
-30290.0039515995 \tabularnewline
-50933.0724551137 \tabularnewline
-52161.8966121828 \tabularnewline
-49100.5170625373 \tabularnewline
44943.2612207427 \tabularnewline
-20436.2833597664 \tabularnewline
-20192.3975896362 \tabularnewline
-33876.5705349554 \tabularnewline
-35416.4142591565 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230792&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]867.887523349344[/C][/ROW]
[ROW][C]-2281.19235728193[/C][/ROW]
[ROW][C]33995.4062612879[/C][/ROW]
[ROW][C]9358.87058578244[/C][/ROW]
[ROW][C]24.1779685670895[/C][/ROW]
[ROW][C]23874.675258339[/C][/ROW]
[ROW][C]19813.1686532612[/C][/ROW]
[ROW][C]267978.217710728[/C][/ROW]
[ROW][C]-114267.214620167[/C][/ROW]
[ROW][C]-40265.306508172[/C][/ROW]
[ROW][C]73786.350086527[/C][/ROW]
[ROW][C]29000.1997827658[/C][/ROW]
[ROW][C]-76434.0156682785[/C][/ROW]
[ROW][C]-20664.0087744748[/C][/ROW]
[ROW][C]-50658.493382462[/C][/ROW]
[ROW][C]23501.6403174908[/C][/ROW]
[ROW][C]100262.20162646[/C][/ROW]
[ROW][C]-185115.892788044[/C][/ROW]
[ROW][C]88358.9504684404[/C][/ROW]
[ROW][C]-164419.034226518[/C][/ROW]
[ROW][C]124021.529714842[/C][/ROW]
[ROW][C]185074.446181391[/C][/ROW]
[ROW][C]112403.506649358[/C][/ROW]
[ROW][C]104376.88770705[/C][/ROW]
[ROW][C]136706.372582719[/C][/ROW]
[ROW][C]83407.2435349616[/C][/ROW]
[ROW][C]29494.659130923[/C][/ROW]
[ROW][C]-21038.6356843677[/C][/ROW]
[ROW][C]-27161.476938448[/C][/ROW]
[ROW][C]-107933.932032702[/C][/ROW]
[ROW][C]-102445.035617911[/C][/ROW]
[ROW][C]-153461.719808505[/C][/ROW]
[ROW][C]27650.9499343898[/C][/ROW]
[ROW][C]47573.6827366892[/C][/ROW]
[ROW][C]-3435.74188907323[/C][/ROW]
[ROW][C]-32393.1581258751[/C][/ROW]
[ROW][C]-9778.24359202163[/C][/ROW]
[ROW][C]-48930.9351671306[/C][/ROW]
[ROW][C]56212.035198271[/C][/ROW]
[ROW][C]63865.4211866991[/C][/ROW]
[ROW][C]-175593.61251862[/C][/ROW]
[ROW][C]61686.9599393504[/C][/ROW]
[ROW][C]-12215.4393794863[/C][/ROW]
[ROW][C]-115544.539966771[/C][/ROW]
[ROW][C]151857.999525218[/C][/ROW]
[ROW][C]64734.4898074722[/C][/ROW]
[ROW][C]6285.31098228317[/C][/ROW]
[ROW][C]8638.47590946478[/C][/ROW]
[ROW][C]-1569.06672539419[/C][/ROW]
[ROW][C]27036.8827216908[/C][/ROW]
[ROW][C]-45406.3731457943[/C][/ROW]
[ROW][C]-82079.2131424946[/C][/ROW]
[ROW][C]-30290.0039515995[/C][/ROW]
[ROW][C]-50933.0724551137[/C][/ROW]
[ROW][C]-52161.8966121828[/C][/ROW]
[ROW][C]-49100.5170625373[/C][/ROW]
[ROW][C]44943.2612207427[/C][/ROW]
[ROW][C]-20436.2833597664[/C][/ROW]
[ROW][C]-20192.3975896362[/C][/ROW]
[ROW][C]-33876.5705349554[/C][/ROW]
[ROW][C]-35416.4142591565[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230792&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230792&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
867.887523349344
-2281.19235728193
33995.4062612879
9358.87058578244
24.1779685670895
23874.675258339
19813.1686532612
267978.217710728
-114267.214620167
-40265.306508172
73786.350086527
29000.1997827658
-76434.0156682785
-20664.0087744748
-50658.493382462
23501.6403174908
100262.20162646
-185115.892788044
88358.9504684404
-164419.034226518
124021.529714842
185074.446181391
112403.506649358
104376.88770705
136706.372582719
83407.2435349616
29494.659130923
-21038.6356843677
-27161.476938448
-107933.932032702
-102445.035617911
-153461.719808505
27650.9499343898
47573.6827366892
-3435.74188907323
-32393.1581258751
-9778.24359202163
-48930.9351671306
56212.035198271
63865.4211866991
-175593.61251862
61686.9599393504
-12215.4393794863
-115544.539966771
151857.999525218
64734.4898074722
6285.31098228317
8638.47590946478
-1569.06672539419
27036.8827216908
-45406.3731457943
-82079.2131424946
-30290.0039515995
-50933.0724551137
-52161.8966121828
-49100.5170625373
44943.2612207427
-20436.2833597664
-20192.3975896362
-33876.5705349554
-35416.4142591565



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 1 ; par7 = 0 ; par8 = 2 ; 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')