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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 11:23:46 -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/t12604694982ut9hpzy4rtrop0.htm/, Retrieved Thu, 28 Mar 2024 19:42:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65687, Retrieved Thu, 28 Mar 2024 19:42:21 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsSHW WS 10 ARIMA Backward Selection
Estimated Impact108
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2009-12-07 09:20:41] [b98453cac15ba1066b407e146608df68]
-    D    [ARIMA Backward Selection] [WS 10 ARIMA Backw...] [2009-12-10 18:23:46] [a45cc820faa25ce30779915639528ec2] [Current]
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Dataseries X:
14.2
13.5
11.9
14.6
15.6
14.1
14.9
14.2
14.6
17.2
15.4
14.3
17.5
14.5
14.4
16.6
16.7
16.6
16.9
15.7
16.4
18.4
16.9
16.5
18.3
15.1
15.7
18.1
16.8
18.9
19
18.1
17.8
21.5
17.1
18.7
19
16.4
16.9
18.6
19.3
19.4
17.6
18.6
18.1
20.4
18.1
19.6
19.9
19.2
17.8
19.2
22
21.1
19.5
22.2
20.9
22.2
23.5
21.5
24.3
22.8
20.3
23.7
23.3
19.6
18
17.3
16.8
18.2
16.5
16
18.4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65687&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]4 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=65687&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65687&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )-0.5623-0.38440.03160.05530.14370.1887-0.0249-0.0421-0.2475-0.5368-0.5061
(p-val)(0 )(5e-04 )(0.776 )(0.6271 )(0.2299 )(0.1153 )(0.8447 )(0.7527 )(0.0676 )(0 )(0 )
Estimates ( 2 )-0.5655-0.38550.0330.05790.15180.20110-0.0269-0.2384-0.5375-0.5073
(p-val)(0 )(4e-04 )(0.7665 )(0.608 )(0.1764 )(0.0486 )(NA )(0.8063 )(0.0636 )(0 )(0 )
Estimates ( 3 )-0.5607-0.38470.03470.0570.14560.201900-0.2223-0.524-0.5058
(p-val)(0 )(5e-04 )(0.7554 )(0.614 )(0.1827 )(0.0476 )(NA )(NA )(0.0459 )(0 )(0 )
Estimates ( 4 )-0.5711-0.400800.04040.13570.203300-0.2192-0.5242-0.5052
(p-val)(0 )(0 )(NA )(0.6838 )(0.1929 )(0.0458 )(NA )(NA )(0.0484 )(0 )(0 )
Estimates ( 5 )-0.5627-0.4043000.11870.18500-0.2172-0.5163-0.5044
(p-val)(0 )(0 )(NA )(NA )(0.2152 )(0.0428 )(NA )(NA )(0.0498 )(0 )(0 )
Estimates ( 6 )-0.5647-0.37410000.136300-0.2109-0.527-0.4814
(p-val)(0 )(1e-04 )(NA )(NA )(NA )(0.1018 )(NA )(NA )(0.0573 )(0 )(0 )
Estimates ( 7 )-0.5915-0.393000000-0.1569-0.5227-0.4922
(p-val)(0 )(0 )(NA )(NA )(NA )(NA )(NA )(NA )(0.1539 )(0 )(0 )
Estimates ( 8 )-0.5678-0.35560000000-0.4226-0.4108
(p-val)(0 )(1e-04 )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(0 )(1e-04 )
Estimates ( 9 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ar4 & ar5 & ar6 & ar7 & ar8 & ar9 & ar10 & ar11 \tabularnewline
Estimates ( 1 ) & -0.5623 & -0.3844 & 0.0316 & 0.0553 & 0.1437 & 0.1887 & -0.0249 & -0.0421 & -0.2475 & -0.5368 & -0.5061 \tabularnewline
(p-val) & (0 ) & (5e-04 ) & (0.776 ) & (0.6271 ) & (0.2299 ) & (0.1153 ) & (0.8447 ) & (0.7527 ) & (0.0676 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 2 ) & -0.5655 & -0.3855 & 0.033 & 0.0579 & 0.1518 & 0.2011 & 0 & -0.0269 & -0.2384 & -0.5375 & -0.5073 \tabularnewline
(p-val) & (0 ) & (4e-04 ) & (0.7665 ) & (0.608 ) & (0.1764 ) & (0.0486 ) & (NA ) & (0.8063 ) & (0.0636 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 3 ) & -0.5607 & -0.3847 & 0.0347 & 0.057 & 0.1456 & 0.2019 & 0 & 0 & -0.2223 & -0.524 & -0.5058 \tabularnewline
(p-val) & (0 ) & (5e-04 ) & (0.7554 ) & (0.614 ) & (0.1827 ) & (0.0476 ) & (NA ) & (NA ) & (0.0459 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 4 ) & -0.5711 & -0.4008 & 0 & 0.0404 & 0.1357 & 0.2033 & 0 & 0 & -0.2192 & -0.5242 & -0.5052 \tabularnewline
(p-val) & (0 ) & (0 ) & (NA ) & (0.6838 ) & (0.1929 ) & (0.0458 ) & (NA ) & (NA ) & (0.0484 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 5 ) & -0.5627 & -0.4043 & 0 & 0 & 0.1187 & 0.185 & 0 & 0 & -0.2172 & -0.5163 & -0.5044 \tabularnewline
(p-val) & (0 ) & (0 ) & (NA ) & (NA ) & (0.2152 ) & (0.0428 ) & (NA ) & (NA ) & (0.0498 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 6 ) & -0.5647 & -0.3741 & 0 & 0 & 0 & 0.1363 & 0 & 0 & -0.2109 & -0.527 & -0.4814 \tabularnewline
(p-val) & (0 ) & (1e-04 ) & (NA ) & (NA ) & (NA ) & (0.1018 ) & (NA ) & (NA ) & (0.0573 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 7 ) & -0.5915 & -0.393 & 0 & 0 & 0 & 0 & 0 & 0 & -0.1569 & -0.5227 & -0.4922 \tabularnewline
(p-val) & (0 ) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.1539 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 8 ) & -0.5678 & -0.3556 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & -0.4226 & -0.4108 \tabularnewline
(p-val) & (0 ) & (1e-04 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0 ) & (1e-04 ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 14 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 15 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 16 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 17 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 18 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 19 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 20 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 21 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65687&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]ar4[/C][C]ar5[/C][C]ar6[/C][C]ar7[/C][C]ar8[/C][C]ar9[/C][C]ar10[/C][C]ar11[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.5623[/C][C]-0.3844[/C][C]0.0316[/C][C]0.0553[/C][C]0.1437[/C][C]0.1887[/C][C]-0.0249[/C][C]-0.0421[/C][C]-0.2475[/C][C]-0.5368[/C][C]-0.5061[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](5e-04 )[/C][C](0.776 )[/C][C](0.6271 )[/C][C](0.2299 )[/C][C](0.1153 )[/C][C](0.8447 )[/C][C](0.7527 )[/C][C](0.0676 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.5655[/C][C]-0.3855[/C][C]0.033[/C][C]0.0579[/C][C]0.1518[/C][C]0.2011[/C][C]0[/C][C]-0.0269[/C][C]-0.2384[/C][C]-0.5375[/C][C]-0.5073[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](4e-04 )[/C][C](0.7665 )[/C][C](0.608 )[/C][C](0.1764 )[/C][C](0.0486 )[/C][C](NA )[/C][C](0.8063 )[/C][C](0.0636 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.5607[/C][C]-0.3847[/C][C]0.0347[/C][C]0.057[/C][C]0.1456[/C][C]0.2019[/C][C]0[/C][C]0[/C][C]-0.2223[/C][C]-0.524[/C][C]-0.5058[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](5e-04 )[/C][C](0.7554 )[/C][C](0.614 )[/C][C](0.1827 )[/C][C](0.0476 )[/C][C](NA )[/C][C](NA )[/C][C](0.0459 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.5711[/C][C]-0.4008[/C][C]0[/C][C]0.0404[/C][C]0.1357[/C][C]0.2033[/C][C]0[/C][C]0[/C][C]-0.2192[/C][C]-0.5242[/C][C]-0.5052[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0.6838 )[/C][C](0.1929 )[/C][C](0.0458 )[/C][C](NA )[/C][C](NA )[/C][C](0.0484 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.5627[/C][C]-0.4043[/C][C]0[/C][C]0[/C][C]0.1187[/C][C]0.185[/C][C]0[/C][C]0[/C][C]-0.2172[/C][C]-0.5163[/C][C]-0.5044[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.2152 )[/C][C](0.0428 )[/C][C](NA )[/C][C](NA )[/C][C](0.0498 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]-0.5647[/C][C]-0.3741[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1363[/C][C]0[/C][C]0[/C][C]-0.2109[/C][C]-0.527[/C][C]-0.4814[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](1e-04 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.1018 )[/C][C](NA )[/C][C](NA )[/C][C](0.0573 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]-0.5915[/C][C]-0.393[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.1569[/C][C]-0.5227[/C][C]-0.4922[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.1539 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]-0.5678[/C][C]-0.3556[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.4226[/C][C]-0.4108[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](1e-04 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](1e-04 )[/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][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][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][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][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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 14 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 15 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 16 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 17 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 18 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 19 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 20 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 21 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][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=65687&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65687&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
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )-0.5623-0.38440.03160.05530.14370.1887-0.0249-0.0421-0.2475-0.5368-0.5061
(p-val)(0 )(5e-04 )(0.776 )(0.6271 )(0.2299 )(0.1153 )(0.8447 )(0.7527 )(0.0676 )(0 )(0 )
Estimates ( 2 )-0.5655-0.38550.0330.05790.15180.20110-0.0269-0.2384-0.5375-0.5073
(p-val)(0 )(4e-04 )(0.7665 )(0.608 )(0.1764 )(0.0486 )(NA )(0.8063 )(0.0636 )(0 )(0 )
Estimates ( 3 )-0.5607-0.38470.03470.0570.14560.201900-0.2223-0.524-0.5058
(p-val)(0 )(5e-04 )(0.7554 )(0.614 )(0.1827 )(0.0476 )(NA )(NA )(0.0459 )(0 )(0 )
Estimates ( 4 )-0.5711-0.400800.04040.13570.203300-0.2192-0.5242-0.5052
(p-val)(0 )(0 )(NA )(0.6838 )(0.1929 )(0.0458 )(NA )(NA )(0.0484 )(0 )(0 )
Estimates ( 5 )-0.5627-0.4043000.11870.18500-0.2172-0.5163-0.5044
(p-val)(0 )(0 )(NA )(NA )(0.2152 )(0.0428 )(NA )(NA )(0.0498 )(0 )(0 )
Estimates ( 6 )-0.5647-0.37410000.136300-0.2109-0.527-0.4814
(p-val)(0 )(1e-04 )(NA )(NA )(NA )(0.1018 )(NA )(NA )(0.0573 )(0 )(0 )
Estimates ( 7 )-0.5915-0.393000000-0.1569-0.5227-0.4922
(p-val)(0 )(0 )(NA )(NA )(NA )(NA )(NA )(NA )(0.1539 )(0 )(0 )
Estimates ( 8 )-0.5678-0.35560000000-0.4226-0.4108
(p-val)(0 )(1e-04 )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(0 )(1e-04 )
Estimates ( 9 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.0141999839978062
-0.466262332021041
-1.30482992729826
1.25651169620269
1.29329455690839
0.214314467103281
0.425657989264161
-0.731488086035056
0.260664881207224
1.89627497874234
0.0475601880379774
-0.824323798399137
1.08456453790871
-0.75888299421062
0.999442410061244
0.795384259178794
0.932107980623606
0.914478995667736
0.552535222059852
0.211619012699458
0.274410166437599
0.98350378127629
0.618737054377398
-0.509899696736621
-0.209910384257476
-1.17612225397734
0.533984908330687
1.54118332147055
0.274841427362585
1.9045773822869
0.92022355308703
1.13912591726865
-0.655447007772519
2.50380517226216
-2.08729016098987
-0.240948901904364
-1.36787931721248
-0.447868493832328
-0.088866560270187
1.44729771062258
2.84680630429249
0.714002499832642
-1.48508878302991
1.07057739685619
-0.843626409645601
1.11498524865990
-0.599550871095612
-0.0893683530602267
-0.468396158598779
1.31146670830234
-0.477879955740825
0.411171598497972
2.34315161203158
0.864843649522474
-0.440208588383712
1.99507532151492
-0.166559234344806
1.29126776114182
2.34330741119775
-1.15787711929764
1.27123984135785
-0.147914527697516
-0.275400887584976
1.98835108991627
-0.227125841790848
-2.18047015991271
-3.09251462914564
-2.85716219847092
-0.537353428498328
0.862826235806644
-0.824576184095928
-0.753375222124419
-0.0755635942294504

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0141999839978062 \tabularnewline
-0.466262332021041 \tabularnewline
-1.30482992729826 \tabularnewline
1.25651169620269 \tabularnewline
1.29329455690839 \tabularnewline
0.214314467103281 \tabularnewline
0.425657989264161 \tabularnewline
-0.731488086035056 \tabularnewline
0.260664881207224 \tabularnewline
1.89627497874234 \tabularnewline
0.0475601880379774 \tabularnewline
-0.824323798399137 \tabularnewline
1.08456453790871 \tabularnewline
-0.75888299421062 \tabularnewline
0.999442410061244 \tabularnewline
0.795384259178794 \tabularnewline
0.932107980623606 \tabularnewline
0.914478995667736 \tabularnewline
0.552535222059852 \tabularnewline
0.211619012699458 \tabularnewline
0.274410166437599 \tabularnewline
0.98350378127629 \tabularnewline
0.618737054377398 \tabularnewline
-0.509899696736621 \tabularnewline
-0.209910384257476 \tabularnewline
-1.17612225397734 \tabularnewline
0.533984908330687 \tabularnewline
1.54118332147055 \tabularnewline
0.274841427362585 \tabularnewline
1.9045773822869 \tabularnewline
0.92022355308703 \tabularnewline
1.13912591726865 \tabularnewline
-0.655447007772519 \tabularnewline
2.50380517226216 \tabularnewline
-2.08729016098987 \tabularnewline
-0.240948901904364 \tabularnewline
-1.36787931721248 \tabularnewline
-0.447868493832328 \tabularnewline
-0.088866560270187 \tabularnewline
1.44729771062258 \tabularnewline
2.84680630429249 \tabularnewline
0.714002499832642 \tabularnewline
-1.48508878302991 \tabularnewline
1.07057739685619 \tabularnewline
-0.843626409645601 \tabularnewline
1.11498524865990 \tabularnewline
-0.599550871095612 \tabularnewline
-0.0893683530602267 \tabularnewline
-0.468396158598779 \tabularnewline
1.31146670830234 \tabularnewline
-0.477879955740825 \tabularnewline
0.411171598497972 \tabularnewline
2.34315161203158 \tabularnewline
0.864843649522474 \tabularnewline
-0.440208588383712 \tabularnewline
1.99507532151492 \tabularnewline
-0.166559234344806 \tabularnewline
1.29126776114182 \tabularnewline
2.34330741119775 \tabularnewline
-1.15787711929764 \tabularnewline
1.27123984135785 \tabularnewline
-0.147914527697516 \tabularnewline
-0.275400887584976 \tabularnewline
1.98835108991627 \tabularnewline
-0.227125841790848 \tabularnewline
-2.18047015991271 \tabularnewline
-3.09251462914564 \tabularnewline
-2.85716219847092 \tabularnewline
-0.537353428498328 \tabularnewline
0.862826235806644 \tabularnewline
-0.824576184095928 \tabularnewline
-0.753375222124419 \tabularnewline
-0.0755635942294504 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65687&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0141999839978062[/C][/ROW]
[ROW][C]-0.466262332021041[/C][/ROW]
[ROW][C]-1.30482992729826[/C][/ROW]
[ROW][C]1.25651169620269[/C][/ROW]
[ROW][C]1.29329455690839[/C][/ROW]
[ROW][C]0.214314467103281[/C][/ROW]
[ROW][C]0.425657989264161[/C][/ROW]
[ROW][C]-0.731488086035056[/C][/ROW]
[ROW][C]0.260664881207224[/C][/ROW]
[ROW][C]1.89627497874234[/C][/ROW]
[ROW][C]0.0475601880379774[/C][/ROW]
[ROW][C]-0.824323798399137[/C][/ROW]
[ROW][C]1.08456453790871[/C][/ROW]
[ROW][C]-0.75888299421062[/C][/ROW]
[ROW][C]0.999442410061244[/C][/ROW]
[ROW][C]0.795384259178794[/C][/ROW]
[ROW][C]0.932107980623606[/C][/ROW]
[ROW][C]0.914478995667736[/C][/ROW]
[ROW][C]0.552535222059852[/C][/ROW]
[ROW][C]0.211619012699458[/C][/ROW]
[ROW][C]0.274410166437599[/C][/ROW]
[ROW][C]0.98350378127629[/C][/ROW]
[ROW][C]0.618737054377398[/C][/ROW]
[ROW][C]-0.509899696736621[/C][/ROW]
[ROW][C]-0.209910384257476[/C][/ROW]
[ROW][C]-1.17612225397734[/C][/ROW]
[ROW][C]0.533984908330687[/C][/ROW]
[ROW][C]1.54118332147055[/C][/ROW]
[ROW][C]0.274841427362585[/C][/ROW]
[ROW][C]1.9045773822869[/C][/ROW]
[ROW][C]0.92022355308703[/C][/ROW]
[ROW][C]1.13912591726865[/C][/ROW]
[ROW][C]-0.655447007772519[/C][/ROW]
[ROW][C]2.50380517226216[/C][/ROW]
[ROW][C]-2.08729016098987[/C][/ROW]
[ROW][C]-0.240948901904364[/C][/ROW]
[ROW][C]-1.36787931721248[/C][/ROW]
[ROW][C]-0.447868493832328[/C][/ROW]
[ROW][C]-0.088866560270187[/C][/ROW]
[ROW][C]1.44729771062258[/C][/ROW]
[ROW][C]2.84680630429249[/C][/ROW]
[ROW][C]0.714002499832642[/C][/ROW]
[ROW][C]-1.48508878302991[/C][/ROW]
[ROW][C]1.07057739685619[/C][/ROW]
[ROW][C]-0.843626409645601[/C][/ROW]
[ROW][C]1.11498524865990[/C][/ROW]
[ROW][C]-0.599550871095612[/C][/ROW]
[ROW][C]-0.0893683530602267[/C][/ROW]
[ROW][C]-0.468396158598779[/C][/ROW]
[ROW][C]1.31146670830234[/C][/ROW]
[ROW][C]-0.477879955740825[/C][/ROW]
[ROW][C]0.411171598497972[/C][/ROW]
[ROW][C]2.34315161203158[/C][/ROW]
[ROW][C]0.864843649522474[/C][/ROW]
[ROW][C]-0.440208588383712[/C][/ROW]
[ROW][C]1.99507532151492[/C][/ROW]
[ROW][C]-0.166559234344806[/C][/ROW]
[ROW][C]1.29126776114182[/C][/ROW]
[ROW][C]2.34330741119775[/C][/ROW]
[ROW][C]-1.15787711929764[/C][/ROW]
[ROW][C]1.27123984135785[/C][/ROW]
[ROW][C]-0.147914527697516[/C][/ROW]
[ROW][C]-0.275400887584976[/C][/ROW]
[ROW][C]1.98835108991627[/C][/ROW]
[ROW][C]-0.227125841790848[/C][/ROW]
[ROW][C]-2.18047015991271[/C][/ROW]
[ROW][C]-3.09251462914564[/C][/ROW]
[ROW][C]-2.85716219847092[/C][/ROW]
[ROW][C]-0.537353428498328[/C][/ROW]
[ROW][C]0.862826235806644[/C][/ROW]
[ROW][C]-0.824576184095928[/C][/ROW]
[ROW][C]-0.753375222124419[/C][/ROW]
[ROW][C]-0.0755635942294504[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65687&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65687&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
0.0141999839978062
-0.466262332021041
-1.30482992729826
1.25651169620269
1.29329455690839
0.214314467103281
0.425657989264161
-0.731488086035056
0.260664881207224
1.89627497874234
0.0475601880379774
-0.824323798399137
1.08456453790871
-0.75888299421062
0.999442410061244
0.795384259178794
0.932107980623606
0.914478995667736
0.552535222059852
0.211619012699458
0.274410166437599
0.98350378127629
0.618737054377398
-0.509899696736621
-0.209910384257476
-1.17612225397734
0.533984908330687
1.54118332147055
0.274841427362585
1.9045773822869
0.92022355308703
1.13912591726865
-0.655447007772519
2.50380517226216
-2.08729016098987
-0.240948901904364
-1.36787931721248
-0.447868493832328
-0.088866560270187
1.44729771062258
2.84680630429249
0.714002499832642
-1.48508878302991
1.07057739685619
-0.843626409645601
1.11498524865990
-0.599550871095612
-0.0893683530602267
-0.468396158598779
1.31146670830234
-0.477879955740825
0.411171598497972
2.34315161203158
0.864843649522474
-0.440208588383712
1.99507532151492
-0.166559234344806
1.29126776114182
2.34330741119775
-1.15787711929764
1.27123984135785
-0.147914527697516
-0.275400887584976
1.98835108991627
-0.227125841790848
-2.18047015991271
-3.09251462914564
-2.85716219847092
-0.537353428498328
0.862826235806644
-0.824576184095928
-0.753375222124419
-0.0755635942294504



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 = 0 ; 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
par6 <- 11
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')