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Description of Statistical Computation

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 computationSun, 19 Dec 2010 14:59:42 +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/19/t1292770684pc2qy6hbann2jmy.htm/, Retrieved Sun, 05 May 2024 08:10:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112462, Retrieved Sun, 05 May 2024 08:10:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

Tree of Dependent Computations

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   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-   PD    [ARIMA Backward Selection] [ws9 tabel] [2009-12-04 14:44:54] [626f1d98f4a7f05bcb9f17666b672c60]
- R PD      [ARIMA Backward Selection] [Paper ARS] [2009-12-12 12:11:40] [626f1d98f4a7f05bcb9f17666b672c60]
-    D          [ARIMA Backward Selection] [ARIMA backward se...] [2010-12-19 14:59:42] [a960f182d9e6e851e9aaba5921cd26a4] [Current]
- R               [ARIMA Backward Selection] [ARIMA backward se...] [2010-12-19 16:10:01] [8d09066a9d3795298da6860e7d4a4400]
-   P               [ARIMA Backward Selection] [ARIMA backward se...] [2010-12-24 10:51:31] [18a20458ff88c9ba38344d123a9464bc]
- RM D            [ARIMA Forecasting] [ARIMA Forecasting] [2010-12-19 16:30:13] [8d09066a9d3795298da6860e7d4a4400]
-   P               [ARIMA Forecasting] [ARIMA Forecasting] [2010-12-24 10:55:57] [18a20458ff88c9ba38344d123a9464bc]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
206010
198112
194519
185705
180173
176142
203401
221902
197378
185001
176356
180449
180144
173666
165688
161570
156145
153730
182698
200765
176512
166618
158644
159585
163095
159044
155511
153745
150569
150605
179612
194690
189917
184128
175335
179566
181140
177876
175041
169292
166070
166972
206348
215706
202108
195411
193111
195198
198770
194163
190420
189733
186029
191531
232571
243477
227247
217859
208679
213188
216234
213586
209465
204045
200237
203666
241476
260307
243324
244460
233575
237217
235243
230354
227184
221678
217142
219452
256446
265845
248624
241114
229245
231805
219277
219313
212610
214771
211142
211457
240048
240636
230580
208795
197922
194596
194581
185686
178106
172608
167302
168053
202300
202388
182516
173476
166444
171297
169701
164182
161914
159612
151001
158114
186530
187069
174330
169362
166827
178037
186413
189226
191563
188906
186005
195309
223532
226899
214126
206903
204442
220375
214320
212588
205816
202196
195722
198563
229139
229527
211868
203555
195770

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time70 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 & 70 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112462&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]70 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=112462&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112462&T=0

As an alternative you can also use a QR Code:  
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 time70 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1ma2ma3sar1sar2sma1
Estimates ( 1 )-0.78910.70650.76950.8123-0.4845-0.59330.0323-0.1202-0.5329
(p-val)(1e-04 )(0 )(5e-04 )(0.0016 )(0.0096 )(0.0285 )(0.8871 )(0.3765 )(0.0158 )
Estimates ( 2 )-0.85760.81130.72010.9113-0.6111-0.52240-0.1585-0.4932
(p-val)(0 )(0 )(1e-04 )(4e-04 )(9e-04 )(0.0282 )(NA )(0.1308 )(0 )
Estimates ( 3 )-0.90880.67810.91031.0259-0.4375-0.788700-0.5393
(p-val)(0 )(0 )(0 )(0 )(0.0142 )(0 )(NA )(NA )(0 )
Estimates ( 4 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & ma2 & ma3 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.7891 & 0.7065 & 0.7695 & 0.8123 & -0.4845 & -0.5933 & 0.0323 & -0.1202 & -0.5329 \tabularnewline
(p-val) & (1e-04 ) & (0 ) & (5e-04 ) & (0.0016 ) & (0.0096 ) & (0.0285 ) & (0.8871 ) & (0.3765 ) & (0.0158 ) \tabularnewline
Estimates ( 2 ) & -0.8576 & 0.8113 & 0.7201 & 0.9113 & -0.6111 & -0.5224 & 0 & -0.1585 & -0.4932 \tabularnewline
(p-val) & (0 ) & (0 ) & (1e-04 ) & (4e-04 ) & (9e-04 ) & (0.0282 ) & (NA ) & (0.1308 ) & (0 ) \tabularnewline
Estimates ( 3 ) & -0.9088 & 0.6781 & 0.9103 & 1.0259 & -0.4375 & -0.7887 & 0 & 0 & -0.5393 \tabularnewline
(p-val) & (0 ) & (0 ) & (0 ) & (0 ) & (0.0142 ) & (0 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 14 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 15 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 16 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 17 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112462&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]ma2[/C][C]ma3[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.7891[/C][C]0.7065[/C][C]0.7695[/C][C]0.8123[/C][C]-0.4845[/C][C]-0.5933[/C][C]0.0323[/C][C]-0.1202[/C][C]-0.5329[/C][/ROW]
[ROW][C](p-val)[/C][C](1e-04 )[/C][C](0 )[/C][C](5e-04 )[/C][C](0.0016 )[/C][C](0.0096 )[/C][C](0.0285 )[/C][C](0.8871 )[/C][C](0.3765 )[/C][C](0.0158 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.8576[/C][C]0.8113[/C][C]0.7201[/C][C]0.9113[/C][C]-0.6111[/C][C]-0.5224[/C][C]0[/C][C]-0.1585[/C][C]-0.4932[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](1e-04 )[/C][C](4e-04 )[/C][C](9e-04 )[/C][C](0.0282 )[/C][C](NA )[/C][C](0.1308 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.9088[/C][C]0.6781[/C][C]0.9103[/C][C]1.0259[/C][C]-0.4375[/C][C]-0.7887[/C][C]0[/C][C]0[/C][C]-0.5393[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](0.0142 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][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][/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][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][/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][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][/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][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][/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][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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/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][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112462&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112462&T=1

As an alternative you can also use a QR Code:  
QR Code


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

ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1ma2ma3sar1sar2sma1
Estimates ( 1 )-0.78910.70650.76950.8123-0.4845-0.59330.0323-0.1202-0.5329
(p-val)(1e-04 )(0 )(5e-04 )(0.0016 )(0.0096 )(0.0285 )(0.8871 )(0.3765 )(0.0158 )
Estimates ( 2 )-0.85760.81130.72010.9113-0.6111-0.52240-0.1585-0.4932
(p-val)(0 )(0 )(1e-04 )(4e-04 )(9e-04 )(0.0282 )(NA )(0.1308 )(0 )
Estimates ( 3 )-0.90880.67810.91031.0259-0.4375-0.788700-0.5393
(p-val)(0 )(0 )(0 )(0 )(0.0142 )(0 )(NA )(NA )(0 )
Estimates ( 4 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )






Estimated ARIMA Residuals
Value
-769.911091064411
1171.29374933207
-3576.19909367338
3609.02574272962
1095.79925841030
493.570996581135
1699.03808604918
-836.851790569599
-273.33274927449
2168.44641653895
126.280531276651
-2912.49908022849
2528.279326198
3062.62746113468
1838.54408855953
3170.78275493723
1031.28777198572
1693.88505468331
-534.931294103998
-3998.67897183693
16976.4897466883
4820.76156299178
-4679.06008524019
597.739006023639
-2477.86336887463
243.500170055299
538.858643878335
-3200.23746695186
324.380492584170
1527.48820190478
10152.01213867
-8059.36170074455
-2418.70020158621
1731.69804803241
5707.52053038529
-2457.74088247615
1308.43067614726
-1090.40510831372
239.326738599514
3112.88202848046
735.340966954667
3676.49659050106
7161.95345823981
-5271.52366899354
-615.490931163876
-2953.67362051989
-3959.28228467967
960.006524980228
1416.51784934988
634.824635154175
720.397470155339
-4470.20401491884
712.17396667502
632.714436638679
2421.35179425981
5025.05128520032
-1844.60128865064
7934.36477809165
-2477.42538503866
-2659.07717306397
-4568.25217166251
-2153.24058135901
1086.69403201461
-348.12195537322
-1194.07385286245
975.591846310268
-133.959575019721
-5399.0547322856
-2095.61029664976
-2266.49549333173
-3160.81015683610
1344.92759353098
-11955.0464889042
6424.5906825952
-765.813368053706
7754.3401036934
1287.97876816604
-1575.27353258201
-9483.7660556782
-8373.24725033994
6676.87062136193
-10402.8203505712
-1759.94290062492
-1095.85530323959
5697.04768411452
-3295.26426878776
-2174.62933880444
-1673.19503942257
51.227339272433
894.207408879431
3271.20830644572
-6741.80637755126
-4763.83829373788
5149.95899638157
6359.49322347478
4069.35367233416
861.463305490183
-1076.12541623963
4778.74718653818
660.011275435734
-3644.177055773
4021.36674834023
-5651.22701044024
-6165.26778707089
6483.38099027895
3857.60047521209
6052.5227072953
6726.9391985371
10097.3691486225
4513.37356055597
2970.10060073751
-4046.51950522375
636.105225891238
2404.07792977331
-4585.66701848662
-2297.29019666499
231.391439225268
-214.577648690509
3258.0934402565
8146.9294080479
-10154.0931655095
-2913.35302191135
-4751.01639953772
-1109.69856965135
-1853.91213961220
-2165.27796141261
294.375444732118
-938.131242252736
-3698.26252171547
2449.84996466576
-2776.86349372163

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-769.911091064411 \tabularnewline
1171.29374933207 \tabularnewline
-3576.19909367338 \tabularnewline
3609.02574272962 \tabularnewline
1095.79925841030 \tabularnewline
493.570996581135 \tabularnewline
1699.03808604918 \tabularnewline
-836.851790569599 \tabularnewline
-273.33274927449 \tabularnewline
2168.44641653895 \tabularnewline
126.280531276651 \tabularnewline
-2912.49908022849 \tabularnewline
2528.279326198 \tabularnewline
3062.62746113468 \tabularnewline
1838.54408855953 \tabularnewline
3170.78275493723 \tabularnewline
1031.28777198572 \tabularnewline
1693.88505468331 \tabularnewline
-534.931294103998 \tabularnewline
-3998.67897183693 \tabularnewline
16976.4897466883 \tabularnewline
4820.76156299178 \tabularnewline
-4679.06008524019 \tabularnewline
597.739006023639 \tabularnewline
-2477.86336887463 \tabularnewline
243.500170055299 \tabularnewline
538.858643878335 \tabularnewline
-3200.23746695186 \tabularnewline
324.380492584170 \tabularnewline
1527.48820190478 \tabularnewline
10152.01213867 \tabularnewline
-8059.36170074455 \tabularnewline
-2418.70020158621 \tabularnewline
1731.69804803241 \tabularnewline
5707.52053038529 \tabularnewline
-2457.74088247615 \tabularnewline
1308.43067614726 \tabularnewline
-1090.40510831372 \tabularnewline
239.326738599514 \tabularnewline
3112.88202848046 \tabularnewline
735.340966954667 \tabularnewline
3676.49659050106 \tabularnewline
7161.95345823981 \tabularnewline
-5271.52366899354 \tabularnewline
-615.490931163876 \tabularnewline
-2953.67362051989 \tabularnewline
-3959.28228467967 \tabularnewline
960.006524980228 \tabularnewline
1416.51784934988 \tabularnewline
634.824635154175 \tabularnewline
720.397470155339 \tabularnewline
-4470.20401491884 \tabularnewline
712.17396667502 \tabularnewline
632.714436638679 \tabularnewline
2421.35179425981 \tabularnewline
5025.05128520032 \tabularnewline
-1844.60128865064 \tabularnewline
7934.36477809165 \tabularnewline
-2477.42538503866 \tabularnewline
-2659.07717306397 \tabularnewline
-4568.25217166251 \tabularnewline
-2153.24058135901 \tabularnewline
1086.69403201461 \tabularnewline
-348.12195537322 \tabularnewline
-1194.07385286245 \tabularnewline
975.591846310268 \tabularnewline
-133.959575019721 \tabularnewline
-5399.0547322856 \tabularnewline
-2095.61029664976 \tabularnewline
-2266.49549333173 \tabularnewline
-3160.81015683610 \tabularnewline
1344.92759353098 \tabularnewline
-11955.0464889042 \tabularnewline
6424.5906825952 \tabularnewline
-765.813368053706 \tabularnewline
7754.3401036934 \tabularnewline
1287.97876816604 \tabularnewline
-1575.27353258201 \tabularnewline
-9483.7660556782 \tabularnewline
-8373.24725033994 \tabularnewline
6676.87062136193 \tabularnewline
-10402.8203505712 \tabularnewline
-1759.94290062492 \tabularnewline
-1095.85530323959 \tabularnewline
5697.04768411452 \tabularnewline
-3295.26426878776 \tabularnewline
-2174.62933880444 \tabularnewline
-1673.19503942257 \tabularnewline
51.227339272433 \tabularnewline
894.207408879431 \tabularnewline
3271.20830644572 \tabularnewline
-6741.80637755126 \tabularnewline
-4763.83829373788 \tabularnewline
5149.95899638157 \tabularnewline
6359.49322347478 \tabularnewline
4069.35367233416 \tabularnewline
861.463305490183 \tabularnewline
-1076.12541623963 \tabularnewline
4778.74718653818 \tabularnewline
660.011275435734 \tabularnewline
-3644.177055773 \tabularnewline
4021.36674834023 \tabularnewline
-5651.22701044024 \tabularnewline
-6165.26778707089 \tabularnewline
6483.38099027895 \tabularnewline
3857.60047521209 \tabularnewline
6052.5227072953 \tabularnewline
6726.9391985371 \tabularnewline
10097.3691486225 \tabularnewline
4513.37356055597 \tabularnewline
2970.10060073751 \tabularnewline
-4046.51950522375 \tabularnewline
636.105225891238 \tabularnewline
2404.07792977331 \tabularnewline
-4585.66701848662 \tabularnewline
-2297.29019666499 \tabularnewline
231.391439225268 \tabularnewline
-214.577648690509 \tabularnewline
3258.0934402565 \tabularnewline
8146.9294080479 \tabularnewline
-10154.0931655095 \tabularnewline
-2913.35302191135 \tabularnewline
-4751.01639953772 \tabularnewline
-1109.69856965135 \tabularnewline
-1853.91213961220 \tabularnewline
-2165.27796141261 \tabularnewline
294.375444732118 \tabularnewline
-938.131242252736 \tabularnewline
-3698.26252171547 \tabularnewline
2449.84996466576 \tabularnewline
-2776.86349372163 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112462&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-769.911091064411[/C][/ROW]
[ROW][C]1171.29374933207[/C][/ROW]
[ROW][C]-3576.19909367338[/C][/ROW]
[ROW][C]3609.02574272962[/C][/ROW]
[ROW][C]1095.79925841030[/C][/ROW]
[ROW][C]493.570996581135[/C][/ROW]
[ROW][C]1699.03808604918[/C][/ROW]
[ROW][C]-836.851790569599[/C][/ROW]
[ROW][C]-273.33274927449[/C][/ROW]
[ROW][C]2168.44641653895[/C][/ROW]
[ROW][C]126.280531276651[/C][/ROW]
[ROW][C]-2912.49908022849[/C][/ROW]
[ROW][C]2528.279326198[/C][/ROW]
[ROW][C]3062.62746113468[/C][/ROW]
[ROW][C]1838.54408855953[/C][/ROW]
[ROW][C]3170.78275493723[/C][/ROW]
[ROW][C]1031.28777198572[/C][/ROW]
[ROW][C]1693.88505468331[/C][/ROW]
[ROW][C]-534.931294103998[/C][/ROW]
[ROW][C]-3998.67897183693[/C][/ROW]
[ROW][C]16976.4897466883[/C][/ROW]
[ROW][C]4820.76156299178[/C][/ROW]
[ROW][C]-4679.06008524019[/C][/ROW]
[ROW][C]597.739006023639[/C][/ROW]
[ROW][C]-2477.86336887463[/C][/ROW]
[ROW][C]243.500170055299[/C][/ROW]
[ROW][C]538.858643878335[/C][/ROW]
[ROW][C]-3200.23746695186[/C][/ROW]
[ROW][C]324.380492584170[/C][/ROW]
[ROW][C]1527.48820190478[/C][/ROW]
[ROW][C]10152.01213867[/C][/ROW]
[ROW][C]-8059.36170074455[/C][/ROW]
[ROW][C]-2418.70020158621[/C][/ROW]
[ROW][C]1731.69804803241[/C][/ROW]
[ROW][C]5707.52053038529[/C][/ROW]
[ROW][C]-2457.74088247615[/C][/ROW]
[ROW][C]1308.43067614726[/C][/ROW]
[ROW][C]-1090.40510831372[/C][/ROW]
[ROW][C]239.326738599514[/C][/ROW]
[ROW][C]3112.88202848046[/C][/ROW]
[ROW][C]735.340966954667[/C][/ROW]
[ROW][C]3676.49659050106[/C][/ROW]
[ROW][C]7161.95345823981[/C][/ROW]
[ROW][C]-5271.52366899354[/C][/ROW]
[ROW][C]-615.490931163876[/C][/ROW]
[ROW][C]-2953.67362051989[/C][/ROW]
[ROW][C]-3959.28228467967[/C][/ROW]
[ROW][C]960.006524980228[/C][/ROW]
[ROW][C]1416.51784934988[/C][/ROW]
[ROW][C]634.824635154175[/C][/ROW]
[ROW][C]720.397470155339[/C][/ROW]
[ROW][C]-4470.20401491884[/C][/ROW]
[ROW][C]712.17396667502[/C][/ROW]
[ROW][C]632.714436638679[/C][/ROW]
[ROW][C]2421.35179425981[/C][/ROW]
[ROW][C]5025.05128520032[/C][/ROW]
[ROW][C]-1844.60128865064[/C][/ROW]
[ROW][C]7934.36477809165[/C][/ROW]
[ROW][C]-2477.42538503866[/C][/ROW]
[ROW][C]-2659.07717306397[/C][/ROW]
[ROW][C]-4568.25217166251[/C][/ROW]
[ROW][C]-2153.24058135901[/C][/ROW]
[ROW][C]1086.69403201461[/C][/ROW]
[ROW][C]-348.12195537322[/C][/ROW]
[ROW][C]-1194.07385286245[/C][/ROW]
[ROW][C]975.591846310268[/C][/ROW]
[ROW][C]-133.959575019721[/C][/ROW]
[ROW][C]-5399.0547322856[/C][/ROW]
[ROW][C]-2095.61029664976[/C][/ROW]
[ROW][C]-2266.49549333173[/C][/ROW]
[ROW][C]-3160.81015683610[/C][/ROW]
[ROW][C]1344.92759353098[/C][/ROW]
[ROW][C]-11955.0464889042[/C][/ROW]
[ROW][C]6424.5906825952[/C][/ROW]
[ROW][C]-765.813368053706[/C][/ROW]
[ROW][C]7754.3401036934[/C][/ROW]
[ROW][C]1287.97876816604[/C][/ROW]
[ROW][C]-1575.27353258201[/C][/ROW]
[ROW][C]-9483.7660556782[/C][/ROW]
[ROW][C]-8373.24725033994[/C][/ROW]
[ROW][C]6676.87062136193[/C][/ROW]
[ROW][C]-10402.8203505712[/C][/ROW]
[ROW][C]-1759.94290062492[/C][/ROW]
[ROW][C]-1095.85530323959[/C][/ROW]
[ROW][C]5697.04768411452[/C][/ROW]
[ROW][C]-3295.26426878776[/C][/ROW]
[ROW][C]-2174.62933880444[/C][/ROW]
[ROW][C]-1673.19503942257[/C][/ROW]
[ROW][C]51.227339272433[/C][/ROW]
[ROW][C]894.207408879431[/C][/ROW]
[ROW][C]3271.20830644572[/C][/ROW]
[ROW][C]-6741.80637755126[/C][/ROW]
[ROW][C]-4763.83829373788[/C][/ROW]
[ROW][C]5149.95899638157[/C][/ROW]
[ROW][C]6359.49322347478[/C][/ROW]
[ROW][C]4069.35367233416[/C][/ROW]
[ROW][C]861.463305490183[/C][/ROW]
[ROW][C]-1076.12541623963[/C][/ROW]
[ROW][C]4778.74718653818[/C][/ROW]
[ROW][C]660.011275435734[/C][/ROW]
[ROW][C]-3644.177055773[/C][/ROW]
[ROW][C]4021.36674834023[/C][/ROW]
[ROW][C]-5651.22701044024[/C][/ROW]
[ROW][C]-6165.26778707089[/C][/ROW]
[ROW][C]6483.38099027895[/C][/ROW]
[ROW][C]3857.60047521209[/C][/ROW]
[ROW][C]6052.5227072953[/C][/ROW]
[ROW][C]6726.9391985371[/C][/ROW]
[ROW][C]10097.3691486225[/C][/ROW]
[ROW][C]4513.37356055597[/C][/ROW]
[ROW][C]2970.10060073751[/C][/ROW]
[ROW][C]-4046.51950522375[/C][/ROW]
[ROW][C]636.105225891238[/C][/ROW]
[ROW][C]2404.07792977331[/C][/ROW]
[ROW][C]-4585.66701848662[/C][/ROW]
[ROW][C]-2297.29019666499[/C][/ROW]
[ROW][C]231.391439225268[/C][/ROW]
[ROW][C]-214.577648690509[/C][/ROW]
[ROW][C]3258.0934402565[/C][/ROW]
[ROW][C]8146.9294080479[/C][/ROW]
[ROW][C]-10154.0931655095[/C][/ROW]
[ROW][C]-2913.35302191135[/C][/ROW]
[ROW][C]-4751.01639953772[/C][/ROW]
[ROW][C]-1109.69856965135[/C][/ROW]
[ROW][C]-1853.91213961220[/C][/ROW]
[ROW][C]-2165.27796141261[/C][/ROW]
[ROW][C]294.375444732118[/C][/ROW]
[ROW][C]-938.131242252736[/C][/ROW]
[ROW][C]-3698.26252171547[/C][/ROW]
[ROW][C]2449.84996466576[/C][/ROW]
[ROW][C]-2776.86349372163[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112462&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112462&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Estimated ARIMA Residuals
Value
-769.911091064411
1171.29374933207
-3576.19909367338
3609.02574272962
1095.79925841030
493.570996581135
1699.03808604918
-836.851790569599
-273.33274927449
2168.44641653895
126.280531276651
-2912.49908022849
2528.279326198
3062.62746113468
1838.54408855953
3170.78275493723
1031.28777198572
1693.88505468331
-534.931294103998
-3998.67897183693
16976.4897466883
4820.76156299178
-4679.06008524019
597.739006023639
-2477.86336887463
243.500170055299
538.858643878335
-3200.23746695186
324.380492584170
1527.48820190478
10152.01213867
-8059.36170074455
-2418.70020158621
1731.69804803241
5707.52053038529
-2457.74088247615
1308.43067614726
-1090.40510831372
239.326738599514
3112.88202848046
735.340966954667
3676.49659050106
7161.95345823981
-5271.52366899354
-615.490931163876
-2953.67362051989
-3959.28228467967
960.006524980228
1416.51784934988
634.824635154175
720.397470155339
-4470.20401491884
712.17396667502
632.714436638679
2421.35179425981
5025.05128520032
-1844.60128865064
7934.36477809165
-2477.42538503866
-2659.07717306397
-4568.25217166251
-2153.24058135901
1086.69403201461
-348.12195537322
-1194.07385286245
975.591846310268
-133.959575019721
-5399.0547322856
-2095.61029664976
-2266.49549333173
-3160.81015683610
1344.92759353098
-11955.0464889042
6424.5906825952
-765.813368053706
7754.3401036934
1287.97876816604
-1575.27353258201
-9483.7660556782
-8373.24725033994
6676.87062136193
-10402.8203505712
-1759.94290062492
-1095.85530323959
5697.04768411452
-3295.26426878776
-2174.62933880444
-1673.19503942257
51.227339272433
894.207408879431
3271.20830644572
-6741.80637755126
-4763.83829373788
5149.95899638157
6359.49322347478
4069.35367233416
861.463305490183
-1076.12541623963
4778.74718653818
660.011275435734
-3644.177055773
4021.36674834023
-5651.22701044024
-6165.26778707089
6483.38099027895
3857.60047521209
6052.5227072953
6726.9391985371
10097.3691486225
4513.37356055597
2970.10060073751
-4046.51950522375
636.105225891238
2404.07792977331
-4585.66701848662
-2297.29019666499
231.391439225268
-214.577648690509
3258.0934402565
8146.9294080479
-10154.0931655095
-2913.35302191135
-4751.01639953772
-1109.69856965135
-1853.91213961220
-2165.27796141261
294.375444732118
-938.131242252736
-3698.26252171547
2449.84996466576
-2776.86349372163


Figures (Output of Computation)

Input Parameters & R Code

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 <- 3
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')