Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationWed, 15 Dec 2010 09:56:47 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/15/t1292406878q9q0joi7rykx7fr.htm/, Retrieved Thu, 31 Oct 2024 23:53:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110318, Retrieved Thu, 31 Oct 2024 23:53:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact188
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 17:44:33] [b98453cac15ba1066b407e146608df68]
-   PD  [Kendall tau Correlation Matrix] [WS10, Pearson Cor...] [2010-12-10 12:56:18] [d946de7cca328fbcf207448a112523ab]
- RMPD      [ARIMA Backward Selection] [] [2010-12-15 09:56:47] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
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Dataseries X:
12008
9169
8788
8417
8247
8197
8236
8253
7733
8366
8626
8863
10102
8463
9114
8563
8872
8301
8301
8278
7736
7973
8268
9476
11100
8962
9173
8738
8459
8078
8411
8291
7810
8616
8312
9692
9911
8915
9452
9112
8472
8230
8384
8625
8221
8649
8625
10443
10357
8586
8892
8329
8101
7922
8120
7838
7735
8406
8209
9451
10041
9411
10405
8467
8464
8102
7627
7513
7510
8291
8064
9383
9706
8579
9474
8318
8213
8059
9111
7708
7680
8014
8007
8718
9486
9113
9025
8476
7952
7759
7835
7600
7651
8319
8812
8630




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110318&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110318&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110318&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3sar1sar2
Estimates ( 1 )0.32980.1383-0.0882-0.54350.0423
(p-val)(0.0071 )(0.2905 )(0.4758 )(1e-04 )(0.7612 )
Estimates ( 2 )0.32560.1311-0.081-0.5670
(p-val)(0.0078 )(0.3115 )(0.5068 )(0 )(NA )
Estimates ( 3 )0.31020.11150-0.58080
(p-val)(0.0103 )(0.3781 )(NA )(0 )(NA )
Estimates ( 4 )0.352900-0.54720
(p-val)(0.0017 )(NA )(NA )(0 )(NA )
Estimates ( 5 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & sar1 & sar2 \tabularnewline
Estimates ( 1 ) & 0.3298 & 0.1383 & -0.0882 & -0.5435 & 0.0423 \tabularnewline
(p-val) & (0.0071 ) & (0.2905 ) & (0.4758 ) & (1e-04 ) & (0.7612 ) \tabularnewline
Estimates ( 2 ) & 0.3256 & 0.1311 & -0.081 & -0.567 & 0 \tabularnewline
(p-val) & (0.0078 ) & (0.3115 ) & (0.5068 ) & (0 ) & (NA ) \tabularnewline
Estimates ( 3 ) & 0.3102 & 0.1115 & 0 & -0.5808 & 0 \tabularnewline
(p-val) & (0.0103 ) & (0.3781 ) & (NA ) & (0 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0.3529 & 0 & 0 & -0.5472 & 0 \tabularnewline
(p-val) & (0.0017 ) & (NA ) & (NA ) & (0 ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110318&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]sar1[/C][C]sar2[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.3298[/C][C]0.1383[/C][C]-0.0882[/C][C]-0.5435[/C][C]0.0423[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0071 )[/C][C](0.2905 )[/C][C](0.4758 )[/C][C](1e-04 )[/C][C](0.7612 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.3256[/C][C]0.1311[/C][C]-0.081[/C][C]-0.567[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0078 )[/C][C](0.3115 )[/C][C](0.5068 )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.3102[/C][C]0.1115[/C][C]0[/C][C]-0.5808[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0103 )[/C][C](0.3781 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.3529[/C][C]0[/C][C]0[/C][C]-0.5472[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0017 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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][/ROW]
[ROW][C](p-val)[/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=110318&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110318&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
Iterationar1ar2ar3sar1sar2
Estimates ( 1 )0.32980.1383-0.0882-0.54350.0423
(p-val)(0.0071 )(0.2905 )(0.4758 )(1e-04 )(0.7612 )
Estimates ( 2 )0.32560.1311-0.081-0.5670
(p-val)(0.0078 )(0.3115 )(0.5068 )(0 )(NA )
Estimates ( 3 )0.31020.11150-0.58080
(p-val)(0.0103 )(0.3781 )(NA )(0 )(NA )
Estimates ( 4 )0.352900-0.54720
(p-val)(0.0017 )(NA )(NA )(0 )(NA )
Estimates ( 5 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
8.8629889142453
-1445.13455655095
-33.2747365245238
615.880126976873
99.0027695036187
439.187775414182
-92.3995488180432
-41.5615586654959
-27.2854590027427
-52.0756899865351
-401.395806957037
-360.313291645036
375.543639463325
-108.301465402274
107.345616173314
232.896221192468
172.836639421585
-158.296694647917
-176.063273541900
203.767836627323
-0.177175783677740
50.7253097631324
388.184867430597
-301.028290278266
576.611329268055
-768.53364452147
368.04018846536
305.914505814839
351.377473887573
-409.353036325201
39.8060052812007
55.2168180974277
327.599891663243
343.913314418697
227.523777443996
161.835599370348
726.093458493706
-554.199650021294
-378.194042713216
-260.156656568419
-402.594167500245
-143.551488617743
-43.8680074485274
-170.983473187638
-481.750281530075
-32.1432011688639
-80.9759318144916
-137.195140757511
-458.207049173627
141.576685687149
713.58994723106
997.467276277857
-755.914260475392
113.303683360820
-9.31526003681665
-663.130045950883
-581.71081962024
-192.565790477052
-11.5420104122259
-250.574106396029
-495.645260722154
-275.590337444877
-120.150878787761
115.025936161301
-13.2805726796232
-12.9858723438974
81.6837627732584
1183.06165131420
-372.144539891836
-96.2035067918605
-356.685179810464
-38.9547577175035
-622.341500938177
-180.275415161010
257.963711195815
-959.226721960298
372.812473040851
-318.552420695921
-206.760275748944
-267.928904903987
169.960641465167
114.294461605642
121.903709487507
719.409599123777
-729.745651277842

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
8.8629889142453 \tabularnewline
-1445.13455655095 \tabularnewline
-33.2747365245238 \tabularnewline
615.880126976873 \tabularnewline
99.0027695036187 \tabularnewline
439.187775414182 \tabularnewline
-92.3995488180432 \tabularnewline
-41.5615586654959 \tabularnewline
-27.2854590027427 \tabularnewline
-52.0756899865351 \tabularnewline
-401.395806957037 \tabularnewline
-360.313291645036 \tabularnewline
375.543639463325 \tabularnewline
-108.301465402274 \tabularnewline
107.345616173314 \tabularnewline
232.896221192468 \tabularnewline
172.836639421585 \tabularnewline
-158.296694647917 \tabularnewline
-176.063273541900 \tabularnewline
203.767836627323 \tabularnewline
-0.177175783677740 \tabularnewline
50.7253097631324 \tabularnewline
388.184867430597 \tabularnewline
-301.028290278266 \tabularnewline
576.611329268055 \tabularnewline
-768.53364452147 \tabularnewline
368.04018846536 \tabularnewline
305.914505814839 \tabularnewline
351.377473887573 \tabularnewline
-409.353036325201 \tabularnewline
39.8060052812007 \tabularnewline
55.2168180974277 \tabularnewline
327.599891663243 \tabularnewline
343.913314418697 \tabularnewline
227.523777443996 \tabularnewline
161.835599370348 \tabularnewline
726.093458493706 \tabularnewline
-554.199650021294 \tabularnewline
-378.194042713216 \tabularnewline
-260.156656568419 \tabularnewline
-402.594167500245 \tabularnewline
-143.551488617743 \tabularnewline
-43.8680074485274 \tabularnewline
-170.983473187638 \tabularnewline
-481.750281530075 \tabularnewline
-32.1432011688639 \tabularnewline
-80.9759318144916 \tabularnewline
-137.195140757511 \tabularnewline
-458.207049173627 \tabularnewline
141.576685687149 \tabularnewline
713.58994723106 \tabularnewline
997.467276277857 \tabularnewline
-755.914260475392 \tabularnewline
113.303683360820 \tabularnewline
-9.31526003681665 \tabularnewline
-663.130045950883 \tabularnewline
-581.71081962024 \tabularnewline
-192.565790477052 \tabularnewline
-11.5420104122259 \tabularnewline
-250.574106396029 \tabularnewline
-495.645260722154 \tabularnewline
-275.590337444877 \tabularnewline
-120.150878787761 \tabularnewline
115.025936161301 \tabularnewline
-13.2805726796232 \tabularnewline
-12.9858723438974 \tabularnewline
81.6837627732584 \tabularnewline
1183.06165131420 \tabularnewline
-372.144539891836 \tabularnewline
-96.2035067918605 \tabularnewline
-356.685179810464 \tabularnewline
-38.9547577175035 \tabularnewline
-622.341500938177 \tabularnewline
-180.275415161010 \tabularnewline
257.963711195815 \tabularnewline
-959.226721960298 \tabularnewline
372.812473040851 \tabularnewline
-318.552420695921 \tabularnewline
-206.760275748944 \tabularnewline
-267.928904903987 \tabularnewline
169.960641465167 \tabularnewline
114.294461605642 \tabularnewline
121.903709487507 \tabularnewline
719.409599123777 \tabularnewline
-729.745651277842 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110318&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]8.8629889142453[/C][/ROW]
[ROW][C]-1445.13455655095[/C][/ROW]
[ROW][C]-33.2747365245238[/C][/ROW]
[ROW][C]615.880126976873[/C][/ROW]
[ROW][C]99.0027695036187[/C][/ROW]
[ROW][C]439.187775414182[/C][/ROW]
[ROW][C]-92.3995488180432[/C][/ROW]
[ROW][C]-41.5615586654959[/C][/ROW]
[ROW][C]-27.2854590027427[/C][/ROW]
[ROW][C]-52.0756899865351[/C][/ROW]
[ROW][C]-401.395806957037[/C][/ROW]
[ROW][C]-360.313291645036[/C][/ROW]
[ROW][C]375.543639463325[/C][/ROW]
[ROW][C]-108.301465402274[/C][/ROW]
[ROW][C]107.345616173314[/C][/ROW]
[ROW][C]232.896221192468[/C][/ROW]
[ROW][C]172.836639421585[/C][/ROW]
[ROW][C]-158.296694647917[/C][/ROW]
[ROW][C]-176.063273541900[/C][/ROW]
[ROW][C]203.767836627323[/C][/ROW]
[ROW][C]-0.177175783677740[/C][/ROW]
[ROW][C]50.7253097631324[/C][/ROW]
[ROW][C]388.184867430597[/C][/ROW]
[ROW][C]-301.028290278266[/C][/ROW]
[ROW][C]576.611329268055[/C][/ROW]
[ROW][C]-768.53364452147[/C][/ROW]
[ROW][C]368.04018846536[/C][/ROW]
[ROW][C]305.914505814839[/C][/ROW]
[ROW][C]351.377473887573[/C][/ROW]
[ROW][C]-409.353036325201[/C][/ROW]
[ROW][C]39.8060052812007[/C][/ROW]
[ROW][C]55.2168180974277[/C][/ROW]
[ROW][C]327.599891663243[/C][/ROW]
[ROW][C]343.913314418697[/C][/ROW]
[ROW][C]227.523777443996[/C][/ROW]
[ROW][C]161.835599370348[/C][/ROW]
[ROW][C]726.093458493706[/C][/ROW]
[ROW][C]-554.199650021294[/C][/ROW]
[ROW][C]-378.194042713216[/C][/ROW]
[ROW][C]-260.156656568419[/C][/ROW]
[ROW][C]-402.594167500245[/C][/ROW]
[ROW][C]-143.551488617743[/C][/ROW]
[ROW][C]-43.8680074485274[/C][/ROW]
[ROW][C]-170.983473187638[/C][/ROW]
[ROW][C]-481.750281530075[/C][/ROW]
[ROW][C]-32.1432011688639[/C][/ROW]
[ROW][C]-80.9759318144916[/C][/ROW]
[ROW][C]-137.195140757511[/C][/ROW]
[ROW][C]-458.207049173627[/C][/ROW]
[ROW][C]141.576685687149[/C][/ROW]
[ROW][C]713.58994723106[/C][/ROW]
[ROW][C]997.467276277857[/C][/ROW]
[ROW][C]-755.914260475392[/C][/ROW]
[ROW][C]113.303683360820[/C][/ROW]
[ROW][C]-9.31526003681665[/C][/ROW]
[ROW][C]-663.130045950883[/C][/ROW]
[ROW][C]-581.71081962024[/C][/ROW]
[ROW][C]-192.565790477052[/C][/ROW]
[ROW][C]-11.5420104122259[/C][/ROW]
[ROW][C]-250.574106396029[/C][/ROW]
[ROW][C]-495.645260722154[/C][/ROW]
[ROW][C]-275.590337444877[/C][/ROW]
[ROW][C]-120.150878787761[/C][/ROW]
[ROW][C]115.025936161301[/C][/ROW]
[ROW][C]-13.2805726796232[/C][/ROW]
[ROW][C]-12.9858723438974[/C][/ROW]
[ROW][C]81.6837627732584[/C][/ROW]
[ROW][C]1183.06165131420[/C][/ROW]
[ROW][C]-372.144539891836[/C][/ROW]
[ROW][C]-96.2035067918605[/C][/ROW]
[ROW][C]-356.685179810464[/C][/ROW]
[ROW][C]-38.9547577175035[/C][/ROW]
[ROW][C]-622.341500938177[/C][/ROW]
[ROW][C]-180.275415161010[/C][/ROW]
[ROW][C]257.963711195815[/C][/ROW]
[ROW][C]-959.226721960298[/C][/ROW]
[ROW][C]372.812473040851[/C][/ROW]
[ROW][C]-318.552420695921[/C][/ROW]
[ROW][C]-206.760275748944[/C][/ROW]
[ROW][C]-267.928904903987[/C][/ROW]
[ROW][C]169.960641465167[/C][/ROW]
[ROW][C]114.294461605642[/C][/ROW]
[ROW][C]121.903709487507[/C][/ROW]
[ROW][C]719.409599123777[/C][/ROW]
[ROW][C]-729.745651277842[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110318&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110318&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
8.8629889142453
-1445.13455655095
-33.2747365245238
615.880126976873
99.0027695036187
439.187775414182
-92.3995488180432
-41.5615586654959
-27.2854590027427
-52.0756899865351
-401.395806957037
-360.313291645036
375.543639463325
-108.301465402274
107.345616173314
232.896221192468
172.836639421585
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Parameters (Session):
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 2 ; 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
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')