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R Software Module

rwasp_arimabackwardselection.wasp

Title produced by software

ARIMA Backward Selection

Date of computation

Tue, 06 Dec 2011 16:44:03 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/06/t1323207884sum4i3lltlm5y08.htm/, Retrieved Mon, 29 Apr 2024 00:14:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=151969, Retrieved Mon, 29 Apr 2024 00:14:42 +0000

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IsPrivate?

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User-defined keywords

Estimated Impact

111

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]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Backward Selection] [Unemployment] [2010-11-29 17:10:28] [b98453cac15ba1066b407e146608df68]
-    D      [ARIMA Backward Selection] [WS 9 ARMA Parameters] [2010-12-03 21:54:01] [8081b8996d5947580de3eb171e82db4f]
-   PD        [ARIMA Backward Selection] [Workshop 9, ARIMA] [2010-12-05 19:24:43] [3635fb7041b1998c5a1332cf9de22bce]
-   P           [ARIMA Backward Selection] [Workshop 9, ARIMA] [2010-12-06 22:46:35] [3635fb7041b1998c5a1332cf9de22bce]
-   PD              [ARIMA Backward Selection] [WS 9] [2011-12-06 21:44:03] [6e647d331a8f33aa61a2d78ef323178e] [Current]

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	28 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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 & 28 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151969&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]28 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151969&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151969&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	28 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ar3	ma1	sar1	sar2	sma1
Estimates ( 1 )	0.8461	0.0061	0.0313	-0.7036	0.3805	0.016	-0.8758
(p-val)	(0 )	(0.934 )	(0.6713 )	(0 )	(0 )	(0.8029 )	(0 )
Estimates ( 2 )	0.853	0	0.0326	-0.7077	0.3796	0.0156	-0.8755
(p-val)	(0 )	(NA )	(0.6493 )	(0 )	(0 )	(0.8071 )	(0 )
Estimates ( 3 )	0.8535	0	0.0322	-0.7079	0.3766	0	-0.8682
(p-val)	(0 )	(NA )	(0.6532 )	(0 )	(0 )	(NA )	(0 )
Estimates ( 4 )	0.9079	0	0	-0.7568	0.3762	0	-0.8719
(p-val)	(0 )	(NA )	(NA )	(0 )	(0 )	(NA )	(0 )
Estimates ( 5 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 12 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 13 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 0.8461 & 0.0061 & 0.0313 & -0.7036 & 0.3805 & 0.016 & -0.8758 \tabularnewline
(p-val) & (0 ) & (0.934 ) & (0.6713 ) & (0 ) & (0 ) & (0.8029 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.853 & 0 & 0.0326 & -0.7077 & 0.3796 & 0.0156 & -0.8755 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.6493 ) & (0 ) & (0 ) & (0.8071 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.8535 & 0 & 0.0322 & -0.7079 & 0.3766 & 0 & -0.8682 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.6532 ) & (0 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0.9079 & 0 & 0 & -0.7568 & 0.3762 & 0 & -0.8719 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (0 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151969&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][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.8461[/C][C]0.0061[/C][C]0.0313[/C][C]-0.7036[/C][C]0.3805[/C][C]0.016[/C][C]-0.8758[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.934 )[/C][C](0.6713 )[/C][C](0 )[/C][C](0 )[/C][C](0.8029 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.853[/C][C]0[/C][C]0.0326[/C][C]-0.7077[/C][C]0.3796[/C][C]0.0156[/C][C]-0.8755[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](0.6493 )[/C][C](0 )[/C][C](0 )[/C][C](0.8071 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.8535[/C][C]0[/C][C]0.0322[/C][C]-0.7079[/C][C]0.3766[/C][C]0[/C][C]-0.8682[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](0.6532 )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.9079[/C][C]0[/C][C]0[/C][C]-0.7568[/C][C]0.3762[/C][C]0[/C][C]-0.8719[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/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][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][/ROW]
[ROW][C]Estimates ( 6 )[/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][/ROW]
[ROW][C]Estimates ( 7 )[/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][/ROW]
[ROW][C]Estimates ( 8 )[/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151969&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151969&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
Iteration	ar1	ar2	ar3	ma1	sar1	sar2	sma1
Estimates ( 1 )	0.8461	0.0061	0.0313	-0.7036	0.3805	0.016	-0.8758
(p-val)	(0 )	(0.934 )	(0.6713 )	(0 )	(0 )	(0.8029 )	(0 )
Estimates ( 2 )	0.853	0	0.0326	-0.7077	0.3796	0.0156	-0.8755
(p-val)	(0 )	(NA )	(0.6493 )	(0 )	(0 )	(0.8071 )	(0 )
Estimates ( 3 )	0.8535	0	0.0322	-0.7079	0.3766	0	-0.8682
(p-val)	(0 )	(NA )	(0.6532 )	(0 )	(0 )	(NA )	(0 )
Estimates ( 4 )	0.9079	0	0	-0.7568	0.3762	0	-0.8719
(p-val)	(0 )	(NA )	(NA )	(0 )	(0 )	(NA )	(0 )
Estimates ( 5 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 12 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 13 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )

Estimated ARIMA Residuals
Value
-1.19913390508364
1.70944002411207
1.40570908234284
3.86100995250047
0.690982403526494
-4.57377936086409
-8.31241743798072
-4.47291836953725
-2.95739877717749
1.93773572143175
-0.207242667767925
0.621585248354278
3.86034343096674
2.85926863404059
-1.29720989037496
-2.20137792750795
-3.73429158081325
-3.94762635073143
-1.11751374601136
-4.82850002236797
-0.210103387336889
-0.870303499515927
6.96675801804466
0.455982102306189
-2.2313943135357
-12.4297241548395
-10.2182592889818
-0.51715354050857
-4.41544838843292
-1.99611876700231
-6.62223890357721
5.2401947399561
-2.96301928805609
3.35722188264181
9.77469817522701
-7.04768079069813
-1.99564624161797
3.33162752017481
-9.06152434059661
17.9007527450522
-14.1260800880185
-12.2036613524843
-10.0329989254759
-8.21891223389565
-13.3614497400715
-5.31227027708349
-4.91512491272782
4.71799355894696
0.983690014688281
-2.74573285686953
12.1013424161996
-6.94412913729502
0.00257441110762114
5.03255110540758
4.06274275842212
4.07962946609452
8.46978323225016
-10.504362125252
-8.23215444769539
3.78299190238231
16.528403302336
-8.31675821508116
7.65271380913068
-3.64135725996585
-8.93359538321911
1.6664505831796
-0.671723760660802
-3.26143207648615
1.04948146892067
-1.80624540646647
-9.41649690414509
-0.644116487138372
-4.83159039785691
1.91107762697645
-3.89220230913808
-1.07369918539013
1.83304490358302
-3.10782777782446
-0.880485394767883
-1.04744377308769
0.193120937568097
1.22252714440858
-3.41483277074894
-8.32815361716694
-3.10369440775606
2.75081300194382
2.04693066599277
-2.3849085226171
2.65612772370281
8.81470703801266
-7.83991532938874
0.842521242323225
0.738361470421486
-4.9514030947538
5.29025358906016
-8.90087372555588
0.594690555315218
2.31794342069833
1.30664096858321
0.726156053997162
0.874327926428922
-2.07508008400823
4.99802547997227
0.697812159997853
4.4298814403577
0.646641579686007
-4.92237104427644
4.25152003604664
2.31094810233185
2.13287612093872
4.50999502989668
-0.026241259821979
8.20405244709179
-2.97736950026718
2.38785850627452
-1.32484825587773
1.29009321155609
-2.36267101458706
2.77343090867578
-2.86033992455382
3.90898783771796
0.033533692526753
2.47529832477279
0.853766681526304
-2.75331747672951
3.8165803319008
4.77477964999397
-2.39078897733705
5.83947988776502
-3.6893911967658
1.84899983020072
-1.39590541495755
-0.514059314840369
4.2613946809768
8.2863258312271
2.80539830139134
7.42661002405215
-1.44122490400908
1.76110115461147
1.70233210902517
1.30837755531704
3.15575399625914
2.86961489268456
2.60578241053301
-8.9949768018824
1.33150608885991
-4.01559918746041
-1.72236495644842
-2.14890614393118
-4.10502007912251
-1.58009877373686
-0.831322323075318
-2.28185849485206
-8.12796745819922
4.94598249569745
-2.88340932217312
-3.4123867010004
-6.35400653233633
1.89447050078874
1.96390027475641
3.13965317253098
-3.03005640126931
2.87009884490197
-3.07152962441112
2.13933306148787
3.18048348644747
4.99515023998454
1.49929009088484
1.85329829346931
-7.10951692284254
2.09595434614834
3.81806524595952
-8.53880240832581
-4.87662830314614
-0.0309184948634418
-7.61155316144567
-7.27480815024008
3.95562967489592
3.67528712003924
-7.59724704386127
-10.4062724909478
2.56058656098707
-0.923089425686443
-0.734526583832338
9.38510609993291
5.54516948421477
1.09751008993925
-0.0947128465635038
9.24304823636051
-8.14272448053728
-0.20760957132069
-0.732157821880819
-9.36467053500673
-4.73117706033845
2.28476757076651
-3.56491993518647
3.94855381240965
-0.079355215767371
-3.54924231734706
-0.442979859414875
-6.73725806217659
1.18432514011946
13.5060634118629
-8.94708964025869
-13.7616425623917
-4.10990188736396
3.77049796596542
5.68777739329903
-0.714234773271122
2.15805259621312
-1.35650017909981
3.35475799924875
0.474426819905108
-1.40722340816594
4.36326954482924
-10.3135118790816
-0.694574552784328
-4.67606846434226
-1.92540297338564
2.37641363769932
-0.000431569620764214
-4.35493243274512
8.92448835080528
-4.13770149849815
1.04870130092195
-0.948640045325556
9.93733001732326
-7.61262420035171
0.721874884854604
-6.6230646203787
-4.65548532696546
8.0106882110495
4.51863369082406
4.17000257758184
1.8882717337515
4.8639413161793
-4.21167797543524
1.68704352393051
-2.21241467092575
16.3743363893505
1.25640266684932
-18.7726032190423
0.247985079950255
0.89747771657329
7.71605941363576
6.97771908610149
-3.56935716979302
1.80270139577959
2.96183503839895
7.00644823731842
-18.8332540137293
1.47104751550405
7.26036394300345
11.5159673390523
-0.474292123863309
1.64844183997466
0.862719347917569
0.0242352878720327
6.00453869855121
-2.37518365807751
4.69908151242252
2.93084928011191
-7.00450807557102
1.33915825802033
-6.45383880289557
-6.0227392747088
5.95491144034849
2.24415233856505
3.86775688376137
2.68250645071347
-7.38683114593946
1.833009391808
3.40053393985683
-7.28572801373801
1.48369637151678
6.42026420654356
9.46018146987575
-2.98953112902051
-5.68126974572555
-9.61433236611097
3.05424247849117
4.19796135848426
-1.50141264071235
2.52140575725995
-0.941423407845224
-0.646184497470086
-10.3592700494984
2.55966172314204
-8.3817152397687
1.12687377560163
1.80498264802932
-1.95430442240704
3.02968089335687
-1.04915314134871
4.8290428625407
6.90590979691964
-1.74441879559309
-6.33299233428611
-7.25010524185659
-1.58403970103631
-17.2215999229629
-1.95104042544776
-7.49089353393535
9.94544114838575
-4.41814932655941
-3.29142341135696
4.73390676269074
-7.61892287260463
-8.67832619950483
9.97210260759884
0.633356232195429
-15.2822356227101
10.7797865418913
3.59959413434493
8.9978964095385
0.223866997743927
-0.197444529153014
-1.6507373372401
4.13650027230276
-10.089480394601
15.9958436146942
-5.95013650660439
-7.26006246135406
-1.51694733971284
3.13979430786089
12.9199800455509
9.93060430410355
6.82344798741437
9.03091231624546
11.1789775621309
0.36820470906829
-3.64570830370093
3.28840531258089
-5.31294795733538
-4.01298212118412
-6.87301431570778
-5.19194176499133
3.77092969339989
8.55475988796581
-3.66137728961752
-5.12780170894165
-8.1937578866997
-6.93580935517041
-1.29499341649144
4.02811611993354
5.6538927720921
-8.50709539095184
-4.09796381905937
-4.74354800707634
-2.20571817502825
-5.47838788506649
4.18776415508817
-0.455887877088306
5.22896329233016
0.914836258349717
3.95083996066798
-6.62445302017734
-0.0997218593937749
1.68665230594844
-3.4794530945874
4.33421435074631

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-1.19913390508364 \tabularnewline
1.70944002411207 \tabularnewline
1.40570908234284 \tabularnewline
3.86100995250047 \tabularnewline
0.690982403526494 \tabularnewline
-4.57377936086409 \tabularnewline
-8.31241743798072 \tabularnewline
-4.47291836953725 \tabularnewline
-2.95739877717749 \tabularnewline
1.93773572143175 \tabularnewline
-0.207242667767925 \tabularnewline
0.621585248354278 \tabularnewline
3.86034343096674 \tabularnewline
2.85926863404059 \tabularnewline
-1.29720989037496 \tabularnewline
-2.20137792750795 \tabularnewline
-3.73429158081325 \tabularnewline
-3.94762635073143 \tabularnewline
-1.11751374601136 \tabularnewline
-4.82850002236797 \tabularnewline
-0.210103387336889 \tabularnewline
-0.870303499515927 \tabularnewline
6.96675801804466 \tabularnewline
0.455982102306189 \tabularnewline
-2.2313943135357 \tabularnewline
-12.4297241548395 \tabularnewline
-10.2182592889818 \tabularnewline
-0.51715354050857 \tabularnewline
-4.41544838843292 \tabularnewline
-1.99611876700231 \tabularnewline
-6.62223890357721 \tabularnewline
5.2401947399561 \tabularnewline
-2.96301928805609 \tabularnewline
3.35722188264181 \tabularnewline
9.77469817522701 \tabularnewline
-7.04768079069813 \tabularnewline
-1.99564624161797 \tabularnewline
3.33162752017481 \tabularnewline
-9.06152434059661 \tabularnewline
17.9007527450522 \tabularnewline
-14.1260800880185 \tabularnewline
-12.2036613524843 \tabularnewline
-10.0329989254759 \tabularnewline
-8.21891223389565 \tabularnewline
-13.3614497400715 \tabularnewline
-5.31227027708349 \tabularnewline
-4.91512491272782 \tabularnewline
4.71799355894696 \tabularnewline
0.983690014688281 \tabularnewline
-2.74573285686953 \tabularnewline
12.1013424161996 \tabularnewline
-6.94412913729502 \tabularnewline
0.00257441110762114 \tabularnewline
5.03255110540758 \tabularnewline
4.06274275842212 \tabularnewline
4.07962946609452 \tabularnewline
8.46978323225016 \tabularnewline
-10.504362125252 \tabularnewline
-8.23215444769539 \tabularnewline
3.78299190238231 \tabularnewline
16.528403302336 \tabularnewline
-8.31675821508116 \tabularnewline
7.65271380913068 \tabularnewline
-3.64135725996585 \tabularnewline
-8.93359538321911 \tabularnewline
1.6664505831796 \tabularnewline
-0.671723760660802 \tabularnewline
-3.26143207648615 \tabularnewline
1.04948146892067 \tabularnewline
-1.80624540646647 \tabularnewline
-9.41649690414509 \tabularnewline
-0.644116487138372 \tabularnewline
-4.83159039785691 \tabularnewline
1.91107762697645 \tabularnewline
-3.89220230913808 \tabularnewline
-1.07369918539013 \tabularnewline
1.83304490358302 \tabularnewline
-3.10782777782446 \tabularnewline
-0.880485394767883 \tabularnewline
-1.04744377308769 \tabularnewline
0.193120937568097 \tabularnewline
1.22252714440858 \tabularnewline
-3.41483277074894 \tabularnewline
-8.32815361716694 \tabularnewline
-3.10369440775606 \tabularnewline
2.75081300194382 \tabularnewline
2.04693066599277 \tabularnewline
-2.3849085226171 \tabularnewline
2.65612772370281 \tabularnewline
8.81470703801266 \tabularnewline
-7.83991532938874 \tabularnewline
0.842521242323225 \tabularnewline
0.738361470421486 \tabularnewline
-4.9514030947538 \tabularnewline
5.29025358906016 \tabularnewline
-8.90087372555588 \tabularnewline
0.594690555315218 \tabularnewline
2.31794342069833 \tabularnewline
1.30664096858321 \tabularnewline
0.726156053997162 \tabularnewline
0.874327926428922 \tabularnewline
-2.07508008400823 \tabularnewline
4.99802547997227 \tabularnewline
0.697812159997853 \tabularnewline
4.4298814403577 \tabularnewline
0.646641579686007 \tabularnewline
-4.92237104427644 \tabularnewline
4.25152003604664 \tabularnewline
2.31094810233185 \tabularnewline
2.13287612093872 \tabularnewline
4.50999502989668 \tabularnewline
-0.026241259821979 \tabularnewline
8.20405244709179 \tabularnewline
-2.97736950026718 \tabularnewline
2.38785850627452 \tabularnewline
-1.32484825587773 \tabularnewline
1.29009321155609 \tabularnewline
-2.36267101458706 \tabularnewline
2.77343090867578 \tabularnewline
-2.86033992455382 \tabularnewline
3.90898783771796 \tabularnewline
0.033533692526753 \tabularnewline
2.47529832477279 \tabularnewline
0.853766681526304 \tabularnewline
-2.75331747672951 \tabularnewline
3.8165803319008 \tabularnewline
4.77477964999397 \tabularnewline
-2.39078897733705 \tabularnewline
5.83947988776502 \tabularnewline
-3.6893911967658 \tabularnewline
1.84899983020072 \tabularnewline
-1.39590541495755 \tabularnewline
-0.514059314840369 \tabularnewline
4.2613946809768 \tabularnewline
8.2863258312271 \tabularnewline
2.80539830139134 \tabularnewline
7.42661002405215 \tabularnewline
-1.44122490400908 \tabularnewline
1.76110115461147 \tabularnewline
1.70233210902517 \tabularnewline
1.30837755531704 \tabularnewline
3.15575399625914 \tabularnewline
2.86961489268456 \tabularnewline
2.60578241053301 \tabularnewline
-8.9949768018824 \tabularnewline
1.33150608885991 \tabularnewline
-4.01559918746041 \tabularnewline
-1.72236495644842 \tabularnewline
-2.14890614393118 \tabularnewline
-4.10502007912251 \tabularnewline
-1.58009877373686 \tabularnewline
-0.831322323075318 \tabularnewline
-2.28185849485206 \tabularnewline
-8.12796745819922 \tabularnewline
4.94598249569745 \tabularnewline
-2.88340932217312 \tabularnewline
-3.4123867010004 \tabularnewline
-6.35400653233633 \tabularnewline
1.89447050078874 \tabularnewline
1.96390027475641 \tabularnewline
3.13965317253098 \tabularnewline
-3.03005640126931 \tabularnewline
2.87009884490197 \tabularnewline
-3.07152962441112 \tabularnewline
2.13933306148787 \tabularnewline
3.18048348644747 \tabularnewline
4.99515023998454 \tabularnewline
1.49929009088484 \tabularnewline
1.85329829346931 \tabularnewline
-7.10951692284254 \tabularnewline
2.09595434614834 \tabularnewline
3.81806524595952 \tabularnewline
-8.53880240832581 \tabularnewline
-4.87662830314614 \tabularnewline
-0.0309184948634418 \tabularnewline
-7.61155316144567 \tabularnewline
-7.27480815024008 \tabularnewline
3.95562967489592 \tabularnewline
3.67528712003924 \tabularnewline
-7.59724704386127 \tabularnewline
-10.4062724909478 \tabularnewline
2.56058656098707 \tabularnewline
-0.923089425686443 \tabularnewline
-0.734526583832338 \tabularnewline
9.38510609993291 \tabularnewline
5.54516948421477 \tabularnewline
1.09751008993925 \tabularnewline
-0.0947128465635038 \tabularnewline
9.24304823636051 \tabularnewline
-8.14272448053728 \tabularnewline
-0.20760957132069 \tabularnewline
-0.732157821880819 \tabularnewline
-9.36467053500673 \tabularnewline
-4.73117706033845 \tabularnewline
2.28476757076651 \tabularnewline
-3.56491993518647 \tabularnewline
3.94855381240965 \tabularnewline
-0.079355215767371 \tabularnewline
-3.54924231734706 \tabularnewline
-0.442979859414875 \tabularnewline
-6.73725806217659 \tabularnewline
1.18432514011946 \tabularnewline
13.5060634118629 \tabularnewline
-8.94708964025869 \tabularnewline
-13.7616425623917 \tabularnewline
-4.10990188736396 \tabularnewline
3.77049796596542 \tabularnewline
5.68777739329903 \tabularnewline
-0.714234773271122 \tabularnewline
2.15805259621312 \tabularnewline
-1.35650017909981 \tabularnewline
3.35475799924875 \tabularnewline
0.474426819905108 \tabularnewline
-1.40722340816594 \tabularnewline
4.36326954482924 \tabularnewline
-10.3135118790816 \tabularnewline
-0.694574552784328 \tabularnewline
-4.67606846434226 \tabularnewline
-1.92540297338564 \tabularnewline
2.37641363769932 \tabularnewline
-0.000431569620764214 \tabularnewline
-4.35493243274512 \tabularnewline
8.92448835080528 \tabularnewline
-4.13770149849815 \tabularnewline
1.04870130092195 \tabularnewline
-0.948640045325556 \tabularnewline
9.93733001732326 \tabularnewline
-7.61262420035171 \tabularnewline
0.721874884854604 \tabularnewline
-6.6230646203787 \tabularnewline
-4.65548532696546 \tabularnewline
8.0106882110495 \tabularnewline
4.51863369082406 \tabularnewline
4.17000257758184 \tabularnewline
1.8882717337515 \tabularnewline
4.8639413161793 \tabularnewline
-4.21167797543524 \tabularnewline
1.68704352393051 \tabularnewline
-2.21241467092575 \tabularnewline
16.3743363893505 \tabularnewline
1.25640266684932 \tabularnewline
-18.7726032190423 \tabularnewline
0.247985079950255 \tabularnewline
0.89747771657329 \tabularnewline
7.71605941363576 \tabularnewline
6.97771908610149 \tabularnewline
-3.56935716979302 \tabularnewline
1.80270139577959 \tabularnewline
2.96183503839895 \tabularnewline
7.00644823731842 \tabularnewline
-18.8332540137293 \tabularnewline
1.47104751550405 \tabularnewline
7.26036394300345 \tabularnewline
11.5159673390523 \tabularnewline
-0.474292123863309 \tabularnewline
1.64844183997466 \tabularnewline
0.862719347917569 \tabularnewline
0.0242352878720327 \tabularnewline
6.00453869855121 \tabularnewline
-2.37518365807751 \tabularnewline
4.69908151242252 \tabularnewline
2.93084928011191 \tabularnewline
-7.00450807557102 \tabularnewline
1.33915825802033 \tabularnewline
-6.45383880289557 \tabularnewline
-6.0227392747088 \tabularnewline
5.95491144034849 \tabularnewline
2.24415233856505 \tabularnewline
3.86775688376137 \tabularnewline
2.68250645071347 \tabularnewline
-7.38683114593946 \tabularnewline
1.833009391808 \tabularnewline
3.40053393985683 \tabularnewline
-7.28572801373801 \tabularnewline
1.48369637151678 \tabularnewline
6.42026420654356 \tabularnewline
9.46018146987575 \tabularnewline
-2.98953112902051 \tabularnewline
-5.68126974572555 \tabularnewline
-9.61433236611097 \tabularnewline
3.05424247849117 \tabularnewline
4.19796135848426 \tabularnewline
-1.50141264071235 \tabularnewline
2.52140575725995 \tabularnewline
-0.941423407845224 \tabularnewline
-0.646184497470086 \tabularnewline
-10.3592700494984 \tabularnewline
2.55966172314204 \tabularnewline
-8.3817152397687 \tabularnewline
1.12687377560163 \tabularnewline
1.80498264802932 \tabularnewline
-1.95430442240704 \tabularnewline
3.02968089335687 \tabularnewline
-1.04915314134871 \tabularnewline
4.8290428625407 \tabularnewline
6.90590979691964 \tabularnewline
-1.74441879559309 \tabularnewline
-6.33299233428611 \tabularnewline
-7.25010524185659 \tabularnewline
-1.58403970103631 \tabularnewline
-17.2215999229629 \tabularnewline
-1.95104042544776 \tabularnewline
-7.49089353393535 \tabularnewline
9.94544114838575 \tabularnewline
-4.41814932655941 \tabularnewline
-3.29142341135696 \tabularnewline
4.73390676269074 \tabularnewline
-7.61892287260463 \tabularnewline
-8.67832619950483 \tabularnewline
9.97210260759884 \tabularnewline
0.633356232195429 \tabularnewline
-15.2822356227101 \tabularnewline
10.7797865418913 \tabularnewline
3.59959413434493 \tabularnewline
8.9978964095385 \tabularnewline
0.223866997743927 \tabularnewline
-0.197444529153014 \tabularnewline
-1.6507373372401 \tabularnewline
4.13650027230276 \tabularnewline
-10.089480394601 \tabularnewline
15.9958436146942 \tabularnewline
-5.95013650660439 \tabularnewline
-7.26006246135406 \tabularnewline
-1.51694733971284 \tabularnewline
3.13979430786089 \tabularnewline
12.9199800455509 \tabularnewline
9.93060430410355 \tabularnewline
6.82344798741437 \tabularnewline
9.03091231624546 \tabularnewline
11.1789775621309 \tabularnewline
0.36820470906829 \tabularnewline
-3.64570830370093 \tabularnewline
3.28840531258089 \tabularnewline
-5.31294795733538 \tabularnewline
-4.01298212118412 \tabularnewline
-6.87301431570778 \tabularnewline
-5.19194176499133 \tabularnewline
3.77092969339989 \tabularnewline
8.55475988796581 \tabularnewline
-3.66137728961752 \tabularnewline
-5.12780170894165 \tabularnewline
-8.1937578866997 \tabularnewline
-6.93580935517041 \tabularnewline
-1.29499341649144 \tabularnewline
4.02811611993354 \tabularnewline
5.6538927720921 \tabularnewline
-8.50709539095184 \tabularnewline
-4.09796381905937 \tabularnewline
-4.74354800707634 \tabularnewline
-2.20571817502825 \tabularnewline
-5.47838788506649 \tabularnewline
4.18776415508817 \tabularnewline
-0.455887877088306 \tabularnewline
5.22896329233016 \tabularnewline
0.914836258349717 \tabularnewline
3.95083996066798 \tabularnewline
-6.62445302017734 \tabularnewline
-0.0997218593937749 \tabularnewline
1.68665230594844 \tabularnewline
-3.4794530945874 \tabularnewline
4.33421435074631 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151969&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-1.19913390508364[/C][/ROW]
[ROW][C]1.70944002411207[/C][/ROW]
[ROW][C]1.40570908234284[/C][/ROW]
[ROW][C]3.86100995250047[/C][/ROW]
[ROW][C]0.690982403526494[/C][/ROW]
[ROW][C]-4.57377936086409[/C][/ROW]
[ROW][C]-8.31241743798072[/C][/ROW]
[ROW][C]-4.47291836953725[/C][/ROW]
[ROW][C]-2.95739877717749[/C][/ROW]
[ROW][C]1.93773572143175[/C][/ROW]
[ROW][C]-0.207242667767925[/C][/ROW]
[ROW][C]0.621585248354278[/C][/ROW]
[ROW][C]3.86034343096674[/C][/ROW]
[ROW][C]2.85926863404059[/C][/ROW]
[ROW][C]-1.29720989037496[/C][/ROW]
[ROW][C]-2.20137792750795[/C][/ROW]
[ROW][C]-3.73429158081325[/C][/ROW]
[ROW][C]-3.94762635073143[/C][/ROW]
[ROW][C]-1.11751374601136[/C][/ROW]
[ROW][C]-4.82850002236797[/C][/ROW]
[ROW][C]-0.210103387336889[/C][/ROW]
[ROW][C]-0.870303499515927[/C][/ROW]
[ROW][C]6.96675801804466[/C][/ROW]
[ROW][C]0.455982102306189[/C][/ROW]
[ROW][C]-2.2313943135357[/C][/ROW]
[ROW][C]-12.4297241548395[/C][/ROW]
[ROW][C]-10.2182592889818[/C][/ROW]
[ROW][C]-0.51715354050857[/C][/ROW]
[ROW][C]-4.41544838843292[/C][/ROW]
[ROW][C]-1.99611876700231[/C][/ROW]
[ROW][C]-6.62223890357721[/C][/ROW]
[ROW][C]5.2401947399561[/C][/ROW]
[ROW][C]-2.96301928805609[/C][/ROW]
[ROW][C]3.35722188264181[/C][/ROW]
[ROW][C]9.77469817522701[/C][/ROW]
[ROW][C]-7.04768079069813[/C][/ROW]
[ROW][C]-1.99564624161797[/C][/ROW]
[ROW][C]3.33162752017481[/C][/ROW]
[ROW][C]-9.06152434059661[/C][/ROW]
[ROW][C]17.9007527450522[/C][/ROW]
[ROW][C]-14.1260800880185[/C][/ROW]
[ROW][C]-12.2036613524843[/C][/ROW]
[ROW][C]-10.0329989254759[/C][/ROW]
[ROW][C]-8.21891223389565[/C][/ROW]
[ROW][C]-13.3614497400715[/C][/ROW]
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[ROW][C]-3.4794530945874[/C][/ROW]
[ROW][C]4.33421435074631[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151969&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151969&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
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1.70944002411207
1.40570908234284
3.86100995250047
0.690982403526494
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3.86034343096674
2.85926863404059
-1.29720989037496
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-3.73429158081325
-3.94762635073143
-1.11751374601136
-4.82850002236797
-0.210103387336889
-0.870303499515927
6.96675801804466
0.455982102306189
-2.2313943135357
-12.4297241548395
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-4.41544838843292
-1.99611876700231
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5.2401947399561
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3.35722188264181
9.77469817522701
-7.04768079069813
-1.99564624161797
3.33162752017481
-9.06152434059661
17.9007527450522
-14.1260800880185
-12.2036613524843
-10.0329989254759
-8.21891223389565
-13.3614497400715
-5.31227027708349
-4.91512491272782
4.71799355894696
0.983690014688281
-2.74573285686953
12.1013424161996
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0.00257441110762114
5.03255110540758
4.06274275842212
4.07962946609452
8.46978323225016
-10.504362125252
-8.23215444769539
3.78299190238231
16.528403302336
-8.31675821508116
7.65271380913068
-3.64135725996585
-8.93359538321911
1.6664505831796
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1.04948146892067
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0.193120937568097
1.22252714440858
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2.75081300194382
2.04693066599277
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2.65612772370281
8.81470703801266
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0.842521242323225
0.738361470421486
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5.29025358906016
-8.90087372555588
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2.31794342069833
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0.726156053997162
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2.31094810233185
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4.50999502989668
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2.38785850627452
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1.29009321155609
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3.90898783771796
0.033533692526753
2.47529832477279
0.853766681526304
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3.8165803319008
4.77477964999397
-2.39078897733705
5.83947988776502
-3.6893911967658
1.84899983020072
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Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Parameters (Session):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code