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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationTue, 04 Dec 2012 17:37:08 -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/04/t13546606642833y4ib0latf1w.htm/, Retrieved Fri, 29 Mar 2024 07:46:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196694, Retrieved Fri, 29 Mar 2024 07:46:29 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115
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   [(Partial) Autocorrelation Function] [Unemployment] [2010-11-29 09:05:21] [b98453cac15ba1066b407e146608df68]
- R       [(Partial) Autocorrelation Function] [Autocorrelatie Un...] [2012-12-04 20:09:16] [f8950b13b9b6c1e097d81f3c7491f9a1]
-   P       [(Partial) Autocorrelation Function] [d=1 en Timelags =...] [2012-12-04 20:17:51] [f8950b13b9b6c1e097d81f3c7491f9a1]
- RMP         [Spectral Analysis] [Spectral analysis...] [2012-12-04 20:36:51] [f8950b13b9b6c1e097d81f3c7491f9a1]
- R P           [Spectral Analysis] [Spectral analysis...] [2012-12-04 20:40:07] [f8950b13b9b6c1e097d81f3c7491f9a1]
- RMP               [ARIMA Backward Selection] [Arima backward se...] [2012-12-04 22:37:08] [09cdab5d933081235930b6410ef38881] [Current]
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Dataseries X:
235.1
280.7
264.6
240.7
201.4
240.8
241.1
223.8
206.1
174.7
203.3
220.5
299.5
347.4
338.3
327.7
351.6
396.6
438.8
395.6
363.5
378.8
357
369
464.8
479.1
431.3
366.5
326.3
355.1
331.6
261.3
249
205.5
235.6
240.9
264.9
253.8
232.3
193.8
177
213.2
207.2
180.6
188.6
175.4
199
179.6
225.8
234
200.2
183.6
178.2
203.2
208.5
191.8
172.8
148
159.4
154.5
213.2
196.4
182.8
176.4
153.6
173.2
171
151.2
161.9
157.2
201.7
236.4
356.1
398.3
403.7
384.6
365.8
368.1
367.9
347
343.3
292.9
311.5
300.9
366.9
356.9
329.7
316.2
269
289.3
266.2
253.6
233.8
228.4
253.6
260.1
306.6
309.2
309.5
271
279.9
317.9
298.4
246.7
227.3
209.1
259.9
266
320.6
308.5
282.2
262.7
263.5
313.1
284.3
252.6
250.3
246.5
312.7
333.2
446.4
511.6
515.5
506.4
483.2
522.3
509.8
460.7
405.8
375
378.5
406.8
467.8
469.8
429.8
355.8
332.7
378
360.5
334.7
319.5
323.1
363.6
352.1
411.9
388.6
416.4
360.7
338
417.2
388.4
371.1
331.5
353.7
396.7
447
533.5
565.4
542.3
488.7
467.1
531.3
496.1
444
403.4
386.3
394.1
404.1
462.1
448.1
432.3
386.3
395.2
421.9
382.9
384.2
345.5
323.4
372.6
376
462.7
487
444.2
399.3
394.9
455.4
414
375.5
347
339.4
385.8
378.8
451.8
446.1
422.5
383.1
352.8
445.3
367.5
355.1
326.2
319.8
331.8
340.9
394.1
417.2
369.9
349.2
321.4
405.7
342.9
316.5
284.2
270.9
288.8
278.8
324.4
310.9
299
273
279.3
359.2
305
282.1
250.3
246.5
257.9
266.5
315.9
318.4
295.4
266.4
245.8
362.8
324.9
294.2
289.5
295.2
290.3
272
307.4
328.7
292.9
249.1
230.4
361.5
321.7
277.2
260.7
251
257.6
241.8
287.5
292.3
274.7
254.2
230
339
318.2
287
295.8
284
271
262.7
340.6
379.4
373.3
355.2
338.4
466.9
451
422
429.2
425.9
460.7
463.6
541.4
544.2
517.5
469.4
439.4
549
533
506.1
484
457
481.5
469.5
544.7
541.2
521.5
469.7
434.4
542.6
517.3
485.7
465.8
447
426.6
411.6
467.5
484.5
451.2
417.4
379.9
484.7
455
420.8
416.5
376.3
405.6
405.8
500.8
514
475.5
430.1
414.4
538
526
488.5
520.2
504.4
568.5
610.6
818
830.9
835.9
782
762.3
856.9
820.9
769.6
752.2
724.4
723.1
719.5
817.4
803.3
752.5
689
630.4
765.5
757.7
732.2
702.6
683.3
709.5
702.2
784.8
810.9
755.6
656.8
615.1
745.3
694.1
675.7
643.7
622.1
634.6
588
689.7
673.9
647.9
568.8
545.7
632.6
643.8
593.1
579.7
546
562.9
572.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time17 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 17 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196694&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196694&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196694&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 time17 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ma1sar1sar2sma1
Estimates ( 1 )0.48670.1754-0.3974-0.1005-0.0616-0.6417
(p-val)(0.0053 )(0.0103 )(0.0223 )(0.3733 )(0.4942 )(0 )
Estimates ( 2 )0.47060.1836-0.3842-0.04620-0.6958
(p-val)(0.0074 )(0.0062 )(0.0293 )(0.5533 )(NA )(0 )
Estimates ( 3 )0.46170.1882-0.376700-0.7209
(p-val)(0.0078 )(0.0044 )(0.0307 )(NA )(NA )(0 )
Estimates ( 4 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(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 ) & 0.4867 & 0.1754 & -0.3974 & -0.1005 & -0.0616 & -0.6417 \tabularnewline
(p-val) & (0.0053 ) & (0.0103 ) & (0.0223 ) & (0.3733 ) & (0.4942 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.4706 & 0.1836 & -0.3842 & -0.0462 & 0 & -0.6958 \tabularnewline
(p-val) & (0.0074 ) & (0.0062 ) & (0.0293 ) & (0.5533 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.4617 & 0.1882 & -0.3767 & 0 & 0 & -0.7209 \tabularnewline
(p-val) & (0.0078 ) & (0.0044 ) & (0.0307 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (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=196694&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]0.4867[/C][C]0.1754[/C][C]-0.3974[/C][C]-0.1005[/C][C]-0.0616[/C][C]-0.6417[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0053 )[/C][C](0.0103 )[/C][C](0.0223 )[/C][C](0.3733 )[/C][C](0.4942 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4706[/C][C]0.1836[/C][C]-0.3842[/C][C]-0.0462[/C][C]0[/C][C]-0.6958[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0074 )[/C][C](0.0062 )[/C][C](0.0293 )[/C][C](0.5533 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4617[/C][C]0.1882[/C][C]-0.3767[/C][C]0[/C][C]0[/C][C]-0.7209[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0078 )[/C][C](0.0044 )[/C][C](0.0307 )[/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][/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 ( 5 )[/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 ( 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=196694&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196694&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 )0.48670.1754-0.3974-0.1005-0.0616-0.6417
(p-val)(0.0053 )(0.0103 )(0.0223 )(0.3733 )(0.4942 )(0 )
Estimates ( 2 )0.47060.1836-0.3842-0.04620-0.6958
(p-val)(0.0074 )(0.0062 )(0.0293 )(0.5533 )(NA )(0 )
Estimates ( 3 )0.46170.1882-0.376700-0.7209
(p-val)(0.0078 )(0.0044 )(0.0307 )(NA )(NA )(0 )
Estimates ( 4 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(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
-0.0447135254057248
-0.068191750570018
0.197918269606767
0.364977510480364
1.51337997001491
-0.359461692692278
0.457275231502941
-0.576200434425975
-0.358061446762809
1.26000911919094
-1.3426613995655
-0.353289572272576
0.161395641006039
-0.857823002225894
-0.555144912714677
-0.727027913427185
-0.369042263720964
-0.057897148849369
-0.769036817946528
-0.853703552871702
0.700854566929713
-0.676058766898924
0.8685780389433
-0.108845887798573
-1.57887204058037
-1.12268950420273
0.396923851965569
0.0148495722533744
0.140259357209249
0.369865091448743
-0.27977609909265
0.304546130220721
0.892466300941603
0.0893800864394611
0.135269391227267
-1.20736758943123
-0.230372957020729
-0.0877971494845146
-0.333232968913545
0.601966274149009
0.489790332779987
-0.30040037273032
0.101351037465765
0.596346948276175
-0.476838868118948
-0.418379377958274
-0.117113004328417
-0.147180753398651
0.53229399247261
-0.976528361947971
0.33117968278846
0.869619340713942
-0.45257848693113
-0.421389493954976
-0.0683600596148124
0.326102642776714
0.848949466716148
0.455827856984584
0.823473300848379
0.933736133640458
1.23343911305542
0.408339887976362
0.287976012716416
-0.211203401074363
-0.26746888677941
-1.11645430061279
-0.0317247419611393
0.547972212615497
0.120802505203706
-0.868321572115532
-0.311155682279801
-0.380689104886608
-0.305618644605495
-0.409940941522515
-0.00967793736508503
0.495426347825795
-0.704347376462402
-0.0634698794656698
-0.513898813561395
0.589511461499308
-0.349423190287194
0.641305318304652
0.0343658055867815
-0.0527521693413458
-0.88013808526258
-0.110617813036417
0.725017568433121
-0.505210288357341
1.0150615573079
0.43216548655901
-0.605966484589926
-0.999657589859428
-0.273898494235233
0.276038633377334
1.00336898214727
0.0129978244196988
-0.560227109760074
-0.549704223972516
-0.264710629286852
0.279628860388416
0.678638847868503
0.661069132524911
-0.704450399948789
-0.201260457770014
0.350125397880165
0.51207404046824
0.855206906792167
0.194069541636632
0.723929038782105
1.18131067298568
0.142881529978045
0.0179257992173649
-0.497956665394179
-0.29971968896939
0.135788263432812
-0.170331262881628
-1.03309848562878
-0.173819271274051
-0.96302408721706
0.69837973868929
-0.367089112370319
-0.263848478594962
-0.418819018967518
-1.12067684039214
0.0500585225821296
0.608903580253748
0.134690291624678
0.330041348089375
0.127838694231447
0.581319134621242
-0.0387079391384443
-0.870097195066174
-0.46211379738008
-0.775456514275368
1.39586946814998
-0.373271729166201
-0.268080175766345
1.08656232000804
-0.325879956916868
0.306618546870411
-0.552774194646825
0.928735518538036
0.0930554074196139
0.793725487523887
-0.0655196058831758
0.319389737335432
-0.466224916530026
-0.26287280601473
0.0101377297889321
0.166709543071387
-0.279488697769329
-0.416437753159013
-0.221155655098779
-0.180257930613951
-0.698512272071116
-0.0309661520108812
-0.218027304661754
-0.373584443853832
0.0529514307570937
0.162726895411381
0.827781138825207
-0.724325868294362
-0.470004758449234
1.04570491786558
-0.192863385796906
-0.571180238732665
0.531291140851591
-0.305718225764298
0.338050047369129
0.47624212229548
-0.813898475121248
-0.0347526285331439
0.307275271758755
0.298265457183836
-0.356205548558446
-0.356604248588957
0.161914372355164
0.157622109396232
0.301928817825696
-0.561981938576204
-0.0543444377712256
-0.242625455291228
-0.0291188115730895
0.228470614637006
-0.510675389572873
1.12387561905997
-1.12598800027546
0.29032376190433
0.190186115737645
0.0853119361597817
-0.708038983594444
0.0821216891754381
-0.307060856747843
0.525053351581741
-0.601608779320242
0.538181814497383
-0.306512068299638
0.654542044531521
-0.53737486074489
-0.186945154129269
-0.0415286514173413
-0.0882630411827104
-0.256492308353941
-0.413059228553879
-0.270151685538235
-0.435615700806758
0.546649355345503
0.323884533777379
0.661932001163851
0.402774356850292
-0.428179654071949
-0.185363732351798
-0.146275810966406
0.177901302434422
-0.37050338889839
0.205238676307464
-0.0894107725670443
-0.020780264582518
-0.00240156148496601
0.027237260428761
-0.304012118322446
1.50787083979102
0.259049820904403
-0.546357644977852
0.581469267535052
0.330213798270331
-0.983366581900927
-0.736262988873595
-0.3387444087161
0.760537196901047
-0.261709569152106
-0.476048052063922
-0.11036774898827
1.69080994703978
0.149871960917807
-0.89444395874598
0.0854707841839252
-0.117916015644284
-0.215892868582292
-0.394092028680007
0.0924263184543216
0.058591669486815
0.253245871408703
0.370568030327609
-0.386523945370544
0.447110890424461
0.623154286603536
-0.18420312666525
0.705478215065575
-0.317030723430716
-0.927475251460118
-0.039375889939744
1.00274756764144
0.81261929695723
0.29829341575836
0.152551035337065
-0.150639292038429
0.110741934834994
0.552786540926509
0.0434953147112301
0.363209762786509
-0.0227970081427034
0.504138675352424
0.138779286905376
-0.0853035323190891
-0.496484911108282
-0.085225191991771
-0.247830972188592
-0.120849215406307
-0.451770219371036
0.612150463129442
0.350625299958072
-0.359290486177318
-0.484436529581368
0.339430847526538
-0.04179448345411
-0.0100068949516317
-0.40391257230065
0.151831208519986
-0.229701002820787
-0.217361644443429
-0.332188932423211
0.264417995396671
0.176869374863164
-0.176677797319851
-0.135670367156522
-0.827944202014328
-0.0722734412054292
-0.117607844899363
0.3146350548744
-0.154386180190955
0.163002766297209
-0.244739216220372
-0.204481560160278
0.0360259332914365
0.00417168096220324
0.266111867403148
-0.651003115556459
0.589994534374599
0.3123229553376
0.552669929926551
-0.0962287491235329
-0.46708361270574
-0.198196786667396
0.395575342061826
0.175735235024807
0.315653624555208
-0.161149078162954
0.882040919260029
0.0637848758882274
0.85863384948623
0.822761596570881
1.77266886701177
-0.56698109715146
0.153261674779983
-0.279232931851406
0.0495720476014342
-1.1697333044563
-0.082109402254228
0.0681021351452311
-0.201544971565849
0.0764370021920877
-0.526360143076404
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-0.366201529954764
-0.236325628463171
-0.0197748908005201
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0.273305314699106
0.571670836112908
0.355107023195257
-0.598053244582319
0.0680124296295634
0.0825157723798942
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-0.720993030836092
0.445855525389991
-0.278184399046103
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0.0382223632234146
0.287981460707054
-0.397401292522107
0.453441800228948
-0.336993629724672
0.0350016894417679
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-0.929487247871639
0.112538315342205
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0.318919353458226
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0.312056036916161
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0.821439554981428
-0.330769415699755
-0.0368470899315831
-0.223411513405448
-0.0162393529216278
0.494328754593185

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0447135254057248 \tabularnewline
-0.068191750570018 \tabularnewline
0.197918269606767 \tabularnewline
0.364977510480364 \tabularnewline
1.51337997001491 \tabularnewline
-0.359461692692278 \tabularnewline
0.457275231502941 \tabularnewline
-0.576200434425975 \tabularnewline
-0.358061446762809 \tabularnewline
1.26000911919094 \tabularnewline
-1.3426613995655 \tabularnewline
-0.353289572272576 \tabularnewline
0.161395641006039 \tabularnewline
-0.857823002225894 \tabularnewline
-0.555144912714677 \tabularnewline
-0.727027913427185 \tabularnewline
-0.369042263720964 \tabularnewline
-0.057897148849369 \tabularnewline
-0.769036817946528 \tabularnewline
-0.853703552871702 \tabularnewline
0.700854566929713 \tabularnewline
-0.676058766898924 \tabularnewline
0.8685780389433 \tabularnewline
-0.108845887798573 \tabularnewline
-1.57887204058037 \tabularnewline
-1.12268950420273 \tabularnewline
0.396923851965569 \tabularnewline
0.0148495722533744 \tabularnewline
0.140259357209249 \tabularnewline
0.369865091448743 \tabularnewline
-0.27977609909265 \tabularnewline
0.304546130220721 \tabularnewline
0.892466300941603 \tabularnewline
0.0893800864394611 \tabularnewline
0.135269391227267 \tabularnewline
-1.20736758943123 \tabularnewline
-0.230372957020729 \tabularnewline
-0.0877971494845146 \tabularnewline
-0.333232968913545 \tabularnewline
0.601966274149009 \tabularnewline
0.489790332779987 \tabularnewline
-0.30040037273032 \tabularnewline
0.101351037465765 \tabularnewline
0.596346948276175 \tabularnewline
-0.476838868118948 \tabularnewline
-0.418379377958274 \tabularnewline
-0.117113004328417 \tabularnewline
-0.147180753398651 \tabularnewline
0.53229399247261 \tabularnewline
-0.976528361947971 \tabularnewline
0.33117968278846 \tabularnewline
0.869619340713942 \tabularnewline
-0.45257848693113 \tabularnewline
-0.421389493954976 \tabularnewline
-0.0683600596148124 \tabularnewline
0.326102642776714 \tabularnewline
0.848949466716148 \tabularnewline
0.455827856984584 \tabularnewline
0.823473300848379 \tabularnewline
0.933736133640458 \tabularnewline
1.23343911305542 \tabularnewline
0.408339887976362 \tabularnewline
0.287976012716416 \tabularnewline
-0.211203401074363 \tabularnewline
-0.26746888677941 \tabularnewline
-1.11645430061279 \tabularnewline
-0.0317247419611393 \tabularnewline
0.547972212615497 \tabularnewline
0.120802505203706 \tabularnewline
-0.868321572115532 \tabularnewline
-0.311155682279801 \tabularnewline
-0.380689104886608 \tabularnewline
-0.305618644605495 \tabularnewline
-0.409940941522515 \tabularnewline
-0.00967793736508503 \tabularnewline
0.495426347825795 \tabularnewline
-0.704347376462402 \tabularnewline
-0.0634698794656698 \tabularnewline
-0.513898813561395 \tabularnewline
0.589511461499308 \tabularnewline
-0.349423190287194 \tabularnewline
0.641305318304652 \tabularnewline
0.0343658055867815 \tabularnewline
-0.0527521693413458 \tabularnewline
-0.88013808526258 \tabularnewline
-0.110617813036417 \tabularnewline
0.725017568433121 \tabularnewline
-0.505210288357341 \tabularnewline
1.0150615573079 \tabularnewline
0.43216548655901 \tabularnewline
-0.605966484589926 \tabularnewline
-0.999657589859428 \tabularnewline
-0.273898494235233 \tabularnewline
0.276038633377334 \tabularnewline
1.00336898214727 \tabularnewline
0.0129978244196988 \tabularnewline
-0.560227109760074 \tabularnewline
-0.549704223972516 \tabularnewline
-0.264710629286852 \tabularnewline
0.279628860388416 \tabularnewline
0.678638847868503 \tabularnewline
0.661069132524911 \tabularnewline
-0.704450399948789 \tabularnewline
-0.201260457770014 \tabularnewline
0.350125397880165 \tabularnewline
0.51207404046824 \tabularnewline
0.855206906792167 \tabularnewline
0.194069541636632 \tabularnewline
0.723929038782105 \tabularnewline
1.18131067298568 \tabularnewline
0.142881529978045 \tabularnewline
0.0179257992173649 \tabularnewline
-0.497956665394179 \tabularnewline
-0.29971968896939 \tabularnewline
0.135788263432812 \tabularnewline
-0.170331262881628 \tabularnewline
-1.03309848562878 \tabularnewline
-0.173819271274051 \tabularnewline
-0.96302408721706 \tabularnewline
0.69837973868929 \tabularnewline
-0.367089112370319 \tabularnewline
-0.263848478594962 \tabularnewline
-0.418819018967518 \tabularnewline
-1.12067684039214 \tabularnewline
0.0500585225821296 \tabularnewline
0.608903580253748 \tabularnewline
0.134690291624678 \tabularnewline
0.330041348089375 \tabularnewline
0.127838694231447 \tabularnewline
0.581319134621242 \tabularnewline
-0.0387079391384443 \tabularnewline
-0.870097195066174 \tabularnewline
-0.46211379738008 \tabularnewline
-0.775456514275368 \tabularnewline
1.39586946814998 \tabularnewline
-0.373271729166201 \tabularnewline
-0.268080175766345 \tabularnewline
1.08656232000804 \tabularnewline
-0.325879956916868 \tabularnewline
0.306618546870411 \tabularnewline
-0.552774194646825 \tabularnewline
0.928735518538036 \tabularnewline
0.0930554074196139 \tabularnewline
0.793725487523887 \tabularnewline
-0.0655196058831758 \tabularnewline
0.319389737335432 \tabularnewline
-0.466224916530026 \tabularnewline
-0.26287280601473 \tabularnewline
0.0101377297889321 \tabularnewline
0.166709543071387 \tabularnewline
-0.279488697769329 \tabularnewline
-0.416437753159013 \tabularnewline
-0.221155655098779 \tabularnewline
-0.180257930613951 \tabularnewline
-0.698512272071116 \tabularnewline
-0.0309661520108812 \tabularnewline
-0.218027304661754 \tabularnewline
-0.373584443853832 \tabularnewline
0.0529514307570937 \tabularnewline
0.162726895411381 \tabularnewline
0.827781138825207 \tabularnewline
-0.724325868294362 \tabularnewline
-0.470004758449234 \tabularnewline
1.04570491786558 \tabularnewline
-0.192863385796906 \tabularnewline
-0.571180238732665 \tabularnewline
0.531291140851591 \tabularnewline
-0.305718225764298 \tabularnewline
0.338050047369129 \tabularnewline
0.47624212229548 \tabularnewline
-0.813898475121248 \tabularnewline
-0.0347526285331439 \tabularnewline
0.307275271758755 \tabularnewline
0.298265457183836 \tabularnewline
-0.356205548558446 \tabularnewline
-0.356604248588957 \tabularnewline
0.161914372355164 \tabularnewline
0.157622109396232 \tabularnewline
0.301928817825696 \tabularnewline
-0.561981938576204 \tabularnewline
-0.0543444377712256 \tabularnewline
-0.242625455291228 \tabularnewline
-0.0291188115730895 \tabularnewline
0.228470614637006 \tabularnewline
-0.510675389572873 \tabularnewline
1.12387561905997 \tabularnewline
-1.12598800027546 \tabularnewline
0.29032376190433 \tabularnewline
0.190186115737645 \tabularnewline
0.0853119361597817 \tabularnewline
-0.708038983594444 \tabularnewline
0.0821216891754381 \tabularnewline
-0.307060856747843 \tabularnewline
0.525053351581741 \tabularnewline
-0.601608779320242 \tabularnewline
0.538181814497383 \tabularnewline
-0.306512068299638 \tabularnewline
0.654542044531521 \tabularnewline
-0.53737486074489 \tabularnewline
-0.186945154129269 \tabularnewline
-0.0415286514173413 \tabularnewline
-0.0882630411827104 \tabularnewline
-0.256492308353941 \tabularnewline
-0.413059228553879 \tabularnewline
-0.270151685538235 \tabularnewline
-0.435615700806758 \tabularnewline
0.546649355345503 \tabularnewline
0.323884533777379 \tabularnewline
0.661932001163851 \tabularnewline
0.402774356850292 \tabularnewline
-0.428179654071949 \tabularnewline
-0.185363732351798 \tabularnewline
-0.146275810966406 \tabularnewline
0.177901302434422 \tabularnewline
-0.37050338889839 \tabularnewline
0.205238676307464 \tabularnewline
-0.0894107725670443 \tabularnewline
-0.020780264582518 \tabularnewline
-0.00240156148496601 \tabularnewline
0.027237260428761 \tabularnewline
-0.304012118322446 \tabularnewline
1.50787083979102 \tabularnewline
0.259049820904403 \tabularnewline
-0.546357644977852 \tabularnewline
0.581469267535052 \tabularnewline
0.330213798270331 \tabularnewline
-0.983366581900927 \tabularnewline
-0.736262988873595 \tabularnewline
-0.3387444087161 \tabularnewline
0.760537196901047 \tabularnewline
-0.261709569152106 \tabularnewline
-0.476048052063922 \tabularnewline
-0.11036774898827 \tabularnewline
1.69080994703978 \tabularnewline
0.149871960917807 \tabularnewline
-0.89444395874598 \tabularnewline
0.0854707841839252 \tabularnewline
-0.117916015644284 \tabularnewline
-0.215892868582292 \tabularnewline
-0.394092028680007 \tabularnewline
0.0924263184543216 \tabularnewline
0.058591669486815 \tabularnewline
0.253245871408703 \tabularnewline
0.370568030327609 \tabularnewline
-0.386523945370544 \tabularnewline
0.447110890424461 \tabularnewline
0.623154286603536 \tabularnewline
-0.18420312666525 \tabularnewline
0.705478215065575 \tabularnewline
-0.317030723430716 \tabularnewline
-0.927475251460118 \tabularnewline
-0.039375889939744 \tabularnewline
1.00274756764144 \tabularnewline
0.81261929695723 \tabularnewline
0.29829341575836 \tabularnewline
0.152551035337065 \tabularnewline
-0.150639292038429 \tabularnewline
0.110741934834994 \tabularnewline
0.552786540926509 \tabularnewline
0.0434953147112301 \tabularnewline
0.363209762786509 \tabularnewline
-0.0227970081427034 \tabularnewline
0.504138675352424 \tabularnewline
0.138779286905376 \tabularnewline
-0.0853035323190891 \tabularnewline
-0.496484911108282 \tabularnewline
-0.085225191991771 \tabularnewline
-0.247830972188592 \tabularnewline
-0.120849215406307 \tabularnewline
-0.451770219371036 \tabularnewline
0.612150463129442 \tabularnewline
0.350625299958072 \tabularnewline
-0.359290486177318 \tabularnewline
-0.484436529581368 \tabularnewline
0.339430847526538 \tabularnewline
-0.04179448345411 \tabularnewline
-0.0100068949516317 \tabularnewline
-0.40391257230065 \tabularnewline
0.151831208519986 \tabularnewline
-0.229701002820787 \tabularnewline
-0.217361644443429 \tabularnewline
-0.332188932423211 \tabularnewline
0.264417995396671 \tabularnewline
0.176869374863164 \tabularnewline
-0.176677797319851 \tabularnewline
-0.135670367156522 \tabularnewline
-0.827944202014328 \tabularnewline
-0.0722734412054292 \tabularnewline
-0.117607844899363 \tabularnewline
0.3146350548744 \tabularnewline
-0.154386180190955 \tabularnewline
0.163002766297209 \tabularnewline
-0.244739216220372 \tabularnewline
-0.204481560160278 \tabularnewline
0.0360259332914365 \tabularnewline
0.00417168096220324 \tabularnewline
0.266111867403148 \tabularnewline
-0.651003115556459 \tabularnewline
0.589994534374599 \tabularnewline
0.3123229553376 \tabularnewline
0.552669929926551 \tabularnewline
-0.0962287491235329 \tabularnewline
-0.46708361270574 \tabularnewline
-0.198196786667396 \tabularnewline
0.395575342061826 \tabularnewline
0.175735235024807 \tabularnewline
0.315653624555208 \tabularnewline
-0.161149078162954 \tabularnewline
0.882040919260029 \tabularnewline
0.0637848758882274 \tabularnewline
0.85863384948623 \tabularnewline
0.822761596570881 \tabularnewline
1.77266886701177 \tabularnewline
-0.56698109715146 \tabularnewline
0.153261674779983 \tabularnewline
-0.279232931851406 \tabularnewline
0.0495720476014342 \tabularnewline
-1.1697333044563 \tabularnewline
-0.082109402254228 \tabularnewline
0.0681021351452311 \tabularnewline
-0.201544971565849 \tabularnewline
0.0764370021920877 \tabularnewline
-0.526360143076404 \tabularnewline
-0.0804139924411478 \tabularnewline
-0.390603733294242 \tabularnewline
-0.366201529954764 \tabularnewline
-0.236325628463171 \tabularnewline
-0.0197748908005201 \tabularnewline
-0.40059323025076 \tabularnewline
0.273305314699106 \tabularnewline
0.571670836112908 \tabularnewline
0.355107023195257 \tabularnewline
-0.598053244582319 \tabularnewline
0.0680124296295634 \tabularnewline
0.0825157723798942 \tabularnewline
-0.236688907197677 \tabularnewline
-0.720993030836092 \tabularnewline
0.445855525389991 \tabularnewline
-0.278184399046103 \tabularnewline
-0.813695706906145 \tabularnewline
0.0382223632234146 \tabularnewline
0.287981460707054 \tabularnewline
-0.397401292522107 \tabularnewline
0.453441800228948 \tabularnewline
-0.336993629724672 \tabularnewline
0.0350016894417679 \tabularnewline
-0.129074066176102 \tabularnewline
-0.929487247871639 \tabularnewline
0.112538315342205 \tabularnewline
-0.266214841056696 \tabularnewline
0.318919353458226 \tabularnewline
-0.235823743490795 \tabularnewline
0.312056036916161 \tabularnewline
-0.607348900500754 \tabularnewline
0.821439554981428 \tabularnewline
-0.330769415699755 \tabularnewline
-0.0368470899315831 \tabularnewline
-0.223411513405448 \tabularnewline
-0.0162393529216278 \tabularnewline
0.494328754593185 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196694&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0447135254057248[/C][/ROW]
[ROW][C]-0.068191750570018[/C][/ROW]
[ROW][C]0.197918269606767[/C][/ROW]
[ROW][C]0.364977510480364[/C][/ROW]
[ROW][C]1.51337997001491[/C][/ROW]
[ROW][C]-0.359461692692278[/C][/ROW]
[ROW][C]0.457275231502941[/C][/ROW]
[ROW][C]-0.576200434425975[/C][/ROW]
[ROW][C]-0.358061446762809[/C][/ROW]
[ROW][C]1.26000911919094[/C][/ROW]
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[ROW][C]0.0350016894417679[/C][/ROW]
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[ROW][C]0.318919353458226[/C][/ROW]
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[ROW][C]-0.607348900500754[/C][/ROW]
[ROW][C]0.821439554981428[/C][/ROW]
[ROW][C]-0.330769415699755[/C][/ROW]
[ROW][C]-0.0368470899315831[/C][/ROW]
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[ROW][C]-0.0162393529216278[/C][/ROW]
[ROW][C]0.494328754593185[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196694&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196694&T=2

As an alternative you can also use a QR Code:  

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

Estimated ARIMA Residuals
Value
-0.0447135254057248
-0.068191750570018
0.197918269606767
0.364977510480364
1.51337997001491
-0.359461692692278
0.457275231502941
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-0.358061446762809
1.26000911919094
-1.3426613995655
-0.353289572272576
0.161395641006039
-0.857823002225894
-0.555144912714677
-0.727027913427185
-0.369042263720964
-0.057897148849369
-0.769036817946528
-0.853703552871702
0.700854566929713
-0.676058766898924
0.8685780389433
-0.108845887798573
-1.57887204058037
-1.12268950420273
0.396923851965569
0.0148495722533744
0.140259357209249
0.369865091448743
-0.27977609909265
0.304546130220721
0.892466300941603
0.0893800864394611
0.135269391227267
-1.20736758943123
-0.230372957020729
-0.0877971494845146
-0.333232968913545
0.601966274149009
0.489790332779987
-0.30040037273032
0.101351037465765
0.596346948276175
-0.476838868118948
-0.418379377958274
-0.117113004328417
-0.147180753398651
0.53229399247261
-0.976528361947971
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Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; 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')