Free Statistics

of Irreproducible Research!

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, 10 Dec 2008 09:27:45 -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/2008/Dec/10/t1228926888vsugqd2g52coui6.htm/, Retrieved Thu, 31 Oct 2024 23:09:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=32023, Retrieved Thu, 31 Oct 2024 23:09:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact198
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
F RMP   [Variance Reduction Matrix] [Q1 Identification...] [2008-12-07 12:23:19] [c993f605b206b366f754f7f8c1fcc291]
F RMPD    [ARIMA Backward Selection] [backword selection] [2008-12-08 21:26:59] [c993f605b206b366f754f7f8c1fcc291]
-   P         [ARIMA Backward Selection] [Assessment verbet...] [2008-12-10 16:27:45] [b23db733701c4d62df5e228d507c1c6a] [Current]
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Post a new message
Dataseries X:
7.8
7.6
7.5
7.6
7.5
7.3
7.6
7.5
7.6
7.9
7.9
8.1
8.2
8.0
7.5
6.8
6.5
6.6
7.6
8.0
8.0
7.7
7.5
7.6
7.7
7.9
7.8
7.5
7.5
7.1
7.5
7.5
7.6
7.7
7.7
7.9
8.1
8.2
8.2
8.1
7.9
7.3
6.9
6.6
6.7
6.9
7.0
7.1
7.2
7.1
6.9
7.0
6.8
6.4
6.7
6.7
6.4
6.3
6.2
6.5
6.8
6.8
6.5
6.3
5.9
5.9
6.4
6.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32023&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32023&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32023&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ma1sar1sar2sma1
Estimates ( 1 )1.1521-0.6407-0.69511.2391-0.2397-0.9743
(p-val)(0 )(0 )(0 )(0 )(0.1825 )(0 )
Estimates ( 2 )1.1764-0.6184-0.72650.99030-0.8772
(p-val)(0 )(0 )(0 )(0 )(NA )(0 )
Estimates ( 3 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 1.1521 & -0.6407 & -0.6951 & 1.2391 & -0.2397 & -0.9743 \tabularnewline
(p-val) & (0 ) & (0 ) & (0 ) & (0 ) & (0.1825 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 1.1764 & -0.6184 & -0.7265 & 0.9903 & 0 & -0.8772 \tabularnewline
(p-val) & (0 ) & (0 ) & (0 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 3 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32023&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]1.1521[/C][C]-0.6407[/C][C]-0.6951[/C][C]1.2391[/C][C]-0.2397[/C][C]-0.9743[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](0.1825 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]1.1764[/C][C]-0.6184[/C][C]-0.7265[/C][C]0.9903[/C][C]0[/C][C]-0.8772[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32023&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32023&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
Iterationar1ar2ma1sar1sar2sma1
Estimates ( 1 )1.1521-0.6407-0.69511.2391-0.2397-0.9743
(p-val)(0 )(0 )(0 )(0 )(0.1825 )(0 )
Estimates ( 2 )1.1764-0.6184-0.72650.99030-0.8772
(p-val)(0 )(0 )(0 )(0 )(NA )(0 )
Estimates ( 3 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.00779998933069292
-0.120924350357558
-0.00941772294825195
0.0595114683322163
-0.16426840182402
-0.120514099808022
0.272798688790504
-0.248153933534529
0.126474988051417
0.167401419304136
-0.106058664552406
0.230473880146808
0.0898576486234971
-0.057341528260448
-0.275877719562129
-0.447365080267442
0.00291132578218415
0.00875295925384537
0.398893613172807
-0.0651870417224587
-0.0878712438711904
-0.160257609263455
0.166721300517670
0.0571356655309087
-0.016927743279899
0.216952676268446
-0.0213635993147717
0.0207234477900078
0.251372524758872
-0.367971870569302
0.171154103447484
-0.163092052532199
0.0647766625750751
0.0265181037640465
-0.0312390930761064
0.103570179190851
0.135150314747611
0.0677839430783833
0.176027260626297
0.127177506620357
-0.0759109428768199
-0.230040180887698
-0.490998682334009
-0.111049012728197
-0.122783678013072
-0.193800440808781
-0.146382138306611
-0.165227289192349
-0.0119975562035179
-0.136565707660434
-0.0781536855367497
0.231846278575789
-0.237477952914019
-0.0440747637718611
0.286175831559669
-0.072169908089123
-0.333140381663609
0.0296565028258376
-0.090878553664439
0.142085663295919
0.0196582326038044
-0.0658739944818399
-0.107993814979621
-0.00689004292605648
-0.195996848026978
0.313261990149112
0.0182525904931838
-0.115411284955267

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.00779998933069292 \tabularnewline
-0.120924350357558 \tabularnewline
-0.00941772294825195 \tabularnewline
0.0595114683322163 \tabularnewline
-0.16426840182402 \tabularnewline
-0.120514099808022 \tabularnewline
0.272798688790504 \tabularnewline
-0.248153933534529 \tabularnewline
0.126474988051417 \tabularnewline
0.167401419304136 \tabularnewline
-0.106058664552406 \tabularnewline
0.230473880146808 \tabularnewline
0.0898576486234971 \tabularnewline
-0.057341528260448 \tabularnewline
-0.275877719562129 \tabularnewline
-0.447365080267442 \tabularnewline
0.00291132578218415 \tabularnewline
0.00875295925384537 \tabularnewline
0.398893613172807 \tabularnewline
-0.0651870417224587 \tabularnewline
-0.0878712438711904 \tabularnewline
-0.160257609263455 \tabularnewline
0.166721300517670 \tabularnewline
0.0571356655309087 \tabularnewline
-0.016927743279899 \tabularnewline
0.216952676268446 \tabularnewline
-0.0213635993147717 \tabularnewline
0.0207234477900078 \tabularnewline
0.251372524758872 \tabularnewline
-0.367971870569302 \tabularnewline
0.171154103447484 \tabularnewline
-0.163092052532199 \tabularnewline
0.0647766625750751 \tabularnewline
0.0265181037640465 \tabularnewline
-0.0312390930761064 \tabularnewline
0.103570179190851 \tabularnewline
0.135150314747611 \tabularnewline
0.0677839430783833 \tabularnewline
0.176027260626297 \tabularnewline
0.127177506620357 \tabularnewline
-0.0759109428768199 \tabularnewline
-0.230040180887698 \tabularnewline
-0.490998682334009 \tabularnewline
-0.111049012728197 \tabularnewline
-0.122783678013072 \tabularnewline
-0.193800440808781 \tabularnewline
-0.146382138306611 \tabularnewline
-0.165227289192349 \tabularnewline
-0.0119975562035179 \tabularnewline
-0.136565707660434 \tabularnewline
-0.0781536855367497 \tabularnewline
0.231846278575789 \tabularnewline
-0.237477952914019 \tabularnewline
-0.0440747637718611 \tabularnewline
0.286175831559669 \tabularnewline
-0.072169908089123 \tabularnewline
-0.333140381663609 \tabularnewline
0.0296565028258376 \tabularnewline
-0.090878553664439 \tabularnewline
0.142085663295919 \tabularnewline
0.0196582326038044 \tabularnewline
-0.0658739944818399 \tabularnewline
-0.107993814979621 \tabularnewline
-0.00689004292605648 \tabularnewline
-0.195996848026978 \tabularnewline
0.313261990149112 \tabularnewline
0.0182525904931838 \tabularnewline
-0.115411284955267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32023&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.00779998933069292[/C][/ROW]
[ROW][C]-0.120924350357558[/C][/ROW]
[ROW][C]-0.00941772294825195[/C][/ROW]
[ROW][C]0.0595114683322163[/C][/ROW]
[ROW][C]-0.16426840182402[/C][/ROW]
[ROW][C]-0.120514099808022[/C][/ROW]
[ROW][C]0.272798688790504[/C][/ROW]
[ROW][C]-0.248153933534529[/C][/ROW]
[ROW][C]0.126474988051417[/C][/ROW]
[ROW][C]0.167401419304136[/C][/ROW]
[ROW][C]-0.106058664552406[/C][/ROW]
[ROW][C]0.230473880146808[/C][/ROW]
[ROW][C]0.0898576486234971[/C][/ROW]
[ROW][C]-0.057341528260448[/C][/ROW]
[ROW][C]-0.275877719562129[/C][/ROW]
[ROW][C]-0.447365080267442[/C][/ROW]
[ROW][C]0.00291132578218415[/C][/ROW]
[ROW][C]0.00875295925384537[/C][/ROW]
[ROW][C]0.398893613172807[/C][/ROW]
[ROW][C]-0.0651870417224587[/C][/ROW]
[ROW][C]-0.0878712438711904[/C][/ROW]
[ROW][C]-0.160257609263455[/C][/ROW]
[ROW][C]0.166721300517670[/C][/ROW]
[ROW][C]0.0571356655309087[/C][/ROW]
[ROW][C]-0.016927743279899[/C][/ROW]
[ROW][C]0.216952676268446[/C][/ROW]
[ROW][C]-0.0213635993147717[/C][/ROW]
[ROW][C]0.0207234477900078[/C][/ROW]
[ROW][C]0.251372524758872[/C][/ROW]
[ROW][C]-0.367971870569302[/C][/ROW]
[ROW][C]0.171154103447484[/C][/ROW]
[ROW][C]-0.163092052532199[/C][/ROW]
[ROW][C]0.0647766625750751[/C][/ROW]
[ROW][C]0.0265181037640465[/C][/ROW]
[ROW][C]-0.0312390930761064[/C][/ROW]
[ROW][C]0.103570179190851[/C][/ROW]
[ROW][C]0.135150314747611[/C][/ROW]
[ROW][C]0.0677839430783833[/C][/ROW]
[ROW][C]0.176027260626297[/C][/ROW]
[ROW][C]0.127177506620357[/C][/ROW]
[ROW][C]-0.0759109428768199[/C][/ROW]
[ROW][C]-0.230040180887698[/C][/ROW]
[ROW][C]-0.490998682334009[/C][/ROW]
[ROW][C]-0.111049012728197[/C][/ROW]
[ROW][C]-0.122783678013072[/C][/ROW]
[ROW][C]-0.193800440808781[/C][/ROW]
[ROW][C]-0.146382138306611[/C][/ROW]
[ROW][C]-0.165227289192349[/C][/ROW]
[ROW][C]-0.0119975562035179[/C][/ROW]
[ROW][C]-0.136565707660434[/C][/ROW]
[ROW][C]-0.0781536855367497[/C][/ROW]
[ROW][C]0.231846278575789[/C][/ROW]
[ROW][C]-0.237477952914019[/C][/ROW]
[ROW][C]-0.0440747637718611[/C][/ROW]
[ROW][C]0.286175831559669[/C][/ROW]
[ROW][C]-0.072169908089123[/C][/ROW]
[ROW][C]-0.333140381663609[/C][/ROW]
[ROW][C]0.0296565028258376[/C][/ROW]
[ROW][C]-0.090878553664439[/C][/ROW]
[ROW][C]0.142085663295919[/C][/ROW]
[ROW][C]0.0196582326038044[/C][/ROW]
[ROW][C]-0.0658739944818399[/C][/ROW]
[ROW][C]-0.107993814979621[/C][/ROW]
[ROW][C]-0.00689004292605648[/C][/ROW]
[ROW][C]-0.195996848026978[/C][/ROW]
[ROW][C]0.313261990149112[/C][/ROW]
[ROW][C]0.0182525904931838[/C][/ROW]
[ROW][C]-0.115411284955267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32023&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32023&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
0.00779998933069292
-0.120924350357558
-0.00941772294825195
0.0595114683322163
-0.16426840182402
-0.120514099808022
0.272798688790504
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Parameters (Session):
par1 = FALSE ; par2 = 1.0 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1.0 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; 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')