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Author's title

Author*Unverified author*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationSat, 22 Dec 2012 08:14:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/22/t1356182122opqjptnabdx2dhl.htm/, Retrieved Fri, 29 Mar 2024 06:05:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204503, Retrieved Fri, 29 Mar 2024 06:05:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158
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   [Spectral Analysis] [Unemployment] [2010-11-29 09:21:38] [b98453cac15ba1066b407e146608df68]
- R  D    [Spectral Analysis] [Workshop 9 (4)] [2012-12-02 11:24:15] [e31fe164d58995c48777312ee804d655]
-   P       [Spectral Analysis] [Workshop 9 (5)] [2012-12-02 11:28:47] [e31fe164d58995c48777312ee804d655]
-   P         [Spectral Analysis] [Workshop 9 (6)] [2012-12-02 11:29:39] [e31fe164d58995c48777312ee804d655]
- RMP           [ARIMA Backward Selection] [Workshop 9 (13)] [2012-12-02 12:58:53] [e31fe164d58995c48777312ee804d655]
- R P               [ARIMA Backward Selection] [Paper arima voors...] [2012-12-22 13:14:50] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum

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Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204503&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204503&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204503&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 time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ma1sar1sar2sma1
Estimates ( 1 )0.17360.2032-0.4073-0.5353-0.3111-0.0273
(p-val)(0.6817 )(0.1885 )(0.3296 )(0.4067 )(0.3146 )(0.9682 )
Estimates ( 2 )0.17880.2039-0.4131-0.5603-0.32140
(p-val)(0.6694 )(0.184 )(0.3155 )(0 )(0.046 )(NA )
Estimates ( 3 )00.1576-0.2427-0.564-0.32230
(p-val)(NA )(0.2481 )(0.0563 )(0 )(0.0448 )(NA )
Estimates ( 4 )00-0.2016-0.5829-0.35550
(p-val)(NA )(NA )(0.0681 )(0 )(0.0226 )(NA )
Estimates ( 5 )000-0.5949-0.38930
(p-val)(NA )(NA )(NA )(0 )(0.0115 )(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 ) & 0.1736 & 0.2032 & -0.4073 & -0.5353 & -0.3111 & -0.0273 \tabularnewline
(p-val) & (0.6817 ) & (0.1885 ) & (0.3296 ) & (0.4067 ) & (0.3146 ) & (0.9682 ) \tabularnewline
Estimates ( 2 ) & 0.1788 & 0.2039 & -0.4131 & -0.5603 & -0.3214 & 0 \tabularnewline
(p-val) & (0.6694 ) & (0.184 ) & (0.3155 ) & (0 ) & (0.046 ) & (NA ) \tabularnewline
Estimates ( 3 ) & 0 & 0.1576 & -0.2427 & -0.564 & -0.3223 & 0 \tabularnewline
(p-val) & (NA ) & (0.2481 ) & (0.0563 ) & (0 ) & (0.0448 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & -0.2016 & -0.5829 & -0.3555 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.0681 ) & (0 ) & (0.0226 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & -0.5949 & -0.3893 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (0.0115 ) & (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=204503&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]0.1736[/C][C]0.2032[/C][C]-0.4073[/C][C]-0.5353[/C][C]-0.3111[/C][C]-0.0273[/C][/ROW]
[ROW][C](p-val)[/C][C](0.6817 )[/C][C](0.1885 )[/C][C](0.3296 )[/C][C](0.4067 )[/C][C](0.3146 )[/C][C](0.9682 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1788[/C][C]0.2039[/C][C]-0.4131[/C][C]-0.5603[/C][C]-0.3214[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.6694 )[/C][C](0.184 )[/C][C](0.3155 )[/C][C](0 )[/C][C](0.046 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.1576[/C][C]-0.2427[/C][C]-0.564[/C][C]-0.3223[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.2481 )[/C][C](0.0563 )[/C][C](0 )[/C][C](0.0448 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]-0.2016[/C][C]-0.5829[/C][C]-0.3555[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.0681 )[/C][C](0 )[/C][C](0.0226 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.5949[/C][C]-0.3893[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.0115 )[/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=204503&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204503&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 )0.17360.2032-0.4073-0.5353-0.3111-0.0273
(p-val)(0.6817 )(0.1885 )(0.3296 )(0.4067 )(0.3146 )(0.9682 )
Estimates ( 2 )0.17880.2039-0.4131-0.5603-0.32140
(p-val)(0.6694 )(0.184 )(0.3155 )(0 )(0.046 )(NA )
Estimates ( 3 )00.1576-0.2427-0.564-0.32230
(p-val)(NA )(0.2481 )(0.0563 )(0 )(0.0448 )(NA )
Estimates ( 4 )00-0.2016-0.5829-0.35550
(p-val)(NA )(NA )(0.0681 )(0 )(0.0226 )(NA )
Estimates ( 5 )000-0.5949-0.38930
(p-val)(NA )(NA )(NA )(0 )(0.0115 )(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
867.887547070964
-2295.84619358662
33867.5488585595
7369.76445574945
1370.93557555042
24009.3717195274
18469.7429258204
266312.986248155
-126608.293437306
-19593.6070169109
67778.2702973566
24260.014517325
-74158.8346092945
-15958.7239614546
-52862.9722682513
25327.7283414855
95505.2851060353
-187570.884577506
102586.775494468
-178238.665967173
137821.196929789
167317.925427787
109838.367612594
106478.992617829
135519.036425288
81352.4540733087
31758.3164444665
-18190.5315768208
-23689.0334021426
-108848.180796023
-96792.0586664734
-150755.117628151
29784.4276677356
39197.7255802089
-1720.68110411517
-28854.2930598759
-8532.05461747034
-49323.2568998833
57901.599770025
58527.9023719264
-173920.235895278
68411.680375476
-24212.3962278838
-115053.72190265
159175.212384398
54913.9927717611
14384.1744027395
12677.6146878707
904.308884151444
28609.8531579105
-45872.4145855542
-77728.5897658589
-31474.4287629373
-55102.0137628162
-53168.4578773642
-56825.4928809449
49331.2286471484
-23345.5465756268
-16932.5737647195
-34819.0357488006
-33689.0122477512

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
867.887547070964 \tabularnewline
-2295.84619358662 \tabularnewline
33867.5488585595 \tabularnewline
7369.76445574945 \tabularnewline
1370.93557555042 \tabularnewline
24009.3717195274 \tabularnewline
18469.7429258204 \tabularnewline
266312.986248155 \tabularnewline
-126608.293437306 \tabularnewline
-19593.6070169109 \tabularnewline
67778.2702973566 \tabularnewline
24260.014517325 \tabularnewline
-74158.8346092945 \tabularnewline
-15958.7239614546 \tabularnewline
-52862.9722682513 \tabularnewline
25327.7283414855 \tabularnewline
95505.2851060353 \tabularnewline
-187570.884577506 \tabularnewline
102586.775494468 \tabularnewline
-178238.665967173 \tabularnewline
137821.196929789 \tabularnewline
167317.925427787 \tabularnewline
109838.367612594 \tabularnewline
106478.992617829 \tabularnewline
135519.036425288 \tabularnewline
81352.4540733087 \tabularnewline
31758.3164444665 \tabularnewline
-18190.5315768208 \tabularnewline
-23689.0334021426 \tabularnewline
-108848.180796023 \tabularnewline
-96792.0586664734 \tabularnewline
-150755.117628151 \tabularnewline
29784.4276677356 \tabularnewline
39197.7255802089 \tabularnewline
-1720.68110411517 \tabularnewline
-28854.2930598759 \tabularnewline
-8532.05461747034 \tabularnewline
-49323.2568998833 \tabularnewline
57901.599770025 \tabularnewline
58527.9023719264 \tabularnewline
-173920.235895278 \tabularnewline
68411.680375476 \tabularnewline
-24212.3962278838 \tabularnewline
-115053.72190265 \tabularnewline
159175.212384398 \tabularnewline
54913.9927717611 \tabularnewline
14384.1744027395 \tabularnewline
12677.6146878707 \tabularnewline
904.308884151444 \tabularnewline
28609.8531579105 \tabularnewline
-45872.4145855542 \tabularnewline
-77728.5897658589 \tabularnewline
-31474.4287629373 \tabularnewline
-55102.0137628162 \tabularnewline
-53168.4578773642 \tabularnewline
-56825.4928809449 \tabularnewline
49331.2286471484 \tabularnewline
-23345.5465756268 \tabularnewline
-16932.5737647195 \tabularnewline
-34819.0357488006 \tabularnewline
-33689.0122477512 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204503&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]867.887547070964[/C][/ROW]
[ROW][C]-2295.84619358662[/C][/ROW]
[ROW][C]33867.5488585595[/C][/ROW]
[ROW][C]7369.76445574945[/C][/ROW]
[ROW][C]1370.93557555042[/C][/ROW]
[ROW][C]24009.3717195274[/C][/ROW]
[ROW][C]18469.7429258204[/C][/ROW]
[ROW][C]266312.986248155[/C][/ROW]
[ROW][C]-126608.293437306[/C][/ROW]
[ROW][C]-19593.6070169109[/C][/ROW]
[ROW][C]67778.2702973566[/C][/ROW]
[ROW][C]24260.014517325[/C][/ROW]
[ROW][C]-74158.8346092945[/C][/ROW]
[ROW][C]-15958.7239614546[/C][/ROW]
[ROW][C]-52862.9722682513[/C][/ROW]
[ROW][C]25327.7283414855[/C][/ROW]
[ROW][C]95505.2851060353[/C][/ROW]
[ROW][C]-187570.884577506[/C][/ROW]
[ROW][C]102586.775494468[/C][/ROW]
[ROW][C]-178238.665967173[/C][/ROW]
[ROW][C]137821.196929789[/C][/ROW]
[ROW][C]167317.925427787[/C][/ROW]
[ROW][C]109838.367612594[/C][/ROW]
[ROW][C]106478.992617829[/C][/ROW]
[ROW][C]135519.036425288[/C][/ROW]
[ROW][C]81352.4540733087[/C][/ROW]
[ROW][C]31758.3164444665[/C][/ROW]
[ROW][C]-18190.5315768208[/C][/ROW]
[ROW][C]-23689.0334021426[/C][/ROW]
[ROW][C]-108848.180796023[/C][/ROW]
[ROW][C]-96792.0586664734[/C][/ROW]
[ROW][C]-150755.117628151[/C][/ROW]
[ROW][C]29784.4276677356[/C][/ROW]
[ROW][C]39197.7255802089[/C][/ROW]
[ROW][C]-1720.68110411517[/C][/ROW]
[ROW][C]-28854.2930598759[/C][/ROW]
[ROW][C]-8532.05461747034[/C][/ROW]
[ROW][C]-49323.2568998833[/C][/ROW]
[ROW][C]57901.599770025[/C][/ROW]
[ROW][C]58527.9023719264[/C][/ROW]
[ROW][C]-173920.235895278[/C][/ROW]
[ROW][C]68411.680375476[/C][/ROW]
[ROW][C]-24212.3962278838[/C][/ROW]
[ROW][C]-115053.72190265[/C][/ROW]
[ROW][C]159175.212384398[/C][/ROW]
[ROW][C]54913.9927717611[/C][/ROW]
[ROW][C]14384.1744027395[/C][/ROW]
[ROW][C]12677.6146878707[/C][/ROW]
[ROW][C]904.308884151444[/C][/ROW]
[ROW][C]28609.8531579105[/C][/ROW]
[ROW][C]-45872.4145855542[/C][/ROW]
[ROW][C]-77728.5897658589[/C][/ROW]
[ROW][C]-31474.4287629373[/C][/ROW]
[ROW][C]-55102.0137628162[/C][/ROW]
[ROW][C]-53168.4578773642[/C][/ROW]
[ROW][C]-56825.4928809449[/C][/ROW]
[ROW][C]49331.2286471484[/C][/ROW]
[ROW][C]-23345.5465756268[/C][/ROW]
[ROW][C]-16932.5737647195[/C][/ROW]
[ROW][C]-34819.0357488006[/C][/ROW]
[ROW][C]-33689.0122477512[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204503&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204503&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
867.887547070964
-2295.84619358662
33867.5488585595
7369.76445574945
1370.93557555042
24009.3717195274
18469.7429258204
266312.986248155
-126608.293437306
-19593.6070169109
67778.2702973566
24260.014517325
-74158.8346092945
-15958.7239614546
-52862.9722682513
25327.7283414855
95505.2851060353
-187570.884577506
102586.775494468
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Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; 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')