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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 computationThu, 20 Dec 2012 15:08:23 -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/20/t1356034161fy2kja92n1c8ldt.htm/, Retrieved Thu, 31 Oct 2024 23:11:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203087, Retrieved Thu, 31 Oct 2024 23:11:20 +0000
QR Codes:

Original text written by user:deel 4 same data
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2011-12-21 12:20:37] [247ecd499838c320214983d11495b1c2]
-   P   [ARIMA Backward Selection] [] [2011-12-23 00:41:00] [247ecd499838c320214983d11495b1c2]
-           [ARIMA Backward Selection] [ARIMA Backward Se...] [2012-12-20 20:08:23] [8c1e1aad2e0aebe6ad8d0bf075616208] [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 time15 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 15 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203087&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203087&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203087&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 time15 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3sar1sar2sma1
Estimates ( 1 )0.0980.22760.0655-0.0891-0.0526-0.6512
(p-val)(0.0672 )(0 )(0.2171 )(0.4288 )(0.5606 )(0 )
Estimates ( 2 )0.09530.23230.0652-0.0430-0.6971
(p-val)(0.0735 )(0 )(0.2186 )(0.5812 )(NA )(0 )
Estimates ( 3 )0.09380.2350.065500-0.7207
(p-val)(0.0777 )(0 )(0.2165 )(NA )(NA )(0 )
Estimates ( 4 )0.10940.2417000-0.724
(p-val)(0.0348 )(0 )(NA )(NA )(NA )(0 )
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 & ar3 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 0.098 & 0.2276 & 0.0655 & -0.0891 & -0.0526 & -0.6512 \tabularnewline
(p-val) & (0.0672 ) & (0 ) & (0.2171 ) & (0.4288 ) & (0.5606 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.0953 & 0.2323 & 0.0652 & -0.043 & 0 & -0.6971 \tabularnewline
(p-val) & (0.0735 ) & (0 ) & (0.2186 ) & (0.5812 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.0938 & 0.235 & 0.0655 & 0 & 0 & -0.7207 \tabularnewline
(p-val) & (0.0777 ) & (0 ) & (0.2165 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0.1094 & 0.2417 & 0 & 0 & 0 & -0.724 \tabularnewline
(p-val) & (0.0348 ) & (0 ) & (NA ) & (NA ) & (NA ) & (0 ) \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=203087&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][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.098[/C][C]0.2276[/C][C]0.0655[/C][C]-0.0891[/C][C]-0.0526[/C][C]-0.6512[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0672 )[/C][C](0 )[/C][C](0.2171 )[/C][C](0.4288 )[/C][C](0.5606 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.0953[/C][C]0.2323[/C][C]0.0652[/C][C]-0.043[/C][C]0[/C][C]-0.6971[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0735 )[/C][C](0 )[/C][C](0.2186 )[/C][C](0.5812 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.0938[/C][C]0.235[/C][C]0.0655[/C][C]0[/C][C]0[/C][C]-0.7207[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0777 )[/C][C](0 )[/C][C](0.2165 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.1094[/C][C]0.2417[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.724[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0348 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/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=203087&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203087&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
Iterationar1ar2ar3sar1sar2sma1
Estimates ( 1 )0.0980.22760.0655-0.0891-0.0526-0.6512
(p-val)(0.0672 )(0 )(0.2171 )(0.4288 )(0.5606 )(0 )
Estimates ( 2 )0.09530.23230.0652-0.0430-0.6971
(p-val)(0.0735 )(0 )(0.2186 )(0.5812 )(NA )(0 )
Estimates ( 3 )0.09380.2350.065500-0.7207
(p-val)(0.0777 )(0 )(0.2165 )(NA )(NA )(0 )
Estimates ( 4 )0.10940.2417000-0.724
(p-val)(0.0348 )(0 )(NA )(NA )(NA )(0 )
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
-0.0447137390230427
-0.068778362173901
0.200064905344157
0.369647506184097
1.52302520143469
-0.382348235575581
0.442481123302953
-0.548688225400163
-0.326734158696823
1.30468259726711
-1.3499961122333
-0.389444330854926
0.182875958172545
-0.827655945543168
-0.571203436128746
-0.727773843868161
-0.408616832728847
-0.0704102119463752
-0.812911802065448
-0.878889742265605
0.689887044964178
-0.730700715164829
0.835146028467026
-0.138182610095487
-1.62611373700985
-1.10034892357678
0.456775029302124
0.0327527399388272
0.101998161993637
0.314319979422889
-0.280297381566065
0.300458423917035
0.864927195047558
0.113438692607626
0.0734383808683579
-1.19439085824408
-0.151865082808314
-0.0210499034840303
-0.343328268139467
0.574181408860685
0.477814069872239
-0.345526543718989
0.0869606031474299
0.590525693132867
-0.490002114337156
-0.422664091888079
-0.0950390599117852
-0.100980417804073
0.509364851377814
-1.00698273663314
0.32882830855559
0.863393027878096
-0.476121982707797
-0.442131907077511
-0.0557930322146298
0.327255220315304
0.866297964346517
0.446075301346585
0.800999595390282
0.913381844758492
1.21106666248245
0.432277274667487
0.307401636283489
-0.163324594043776
-0.171057239378714
-1.03961279566805
0.0264096948412303
0.590971944182521
0.097670677054715
-0.913730369936987
-0.330725944996714
-0.39195787582803
-0.33595034452303
-0.460376742093759
-0.0292544642563706
0.496164472480486
-0.733628064837655
-0.0817196760109188
-0.515669889873024
0.578909014902339
-0.353325630092767
0.636523095994961
0.0383900142934602
-0.05717954972781
-0.858265740430048
-0.0909909671169302
0.755512537455174
-0.518648217936032
0.994492422667557
0.406664942162714
-0.616393295045222
-0.995857996261535
-0.203823892252728
0.285780542685931
0.992848968316651
-0.0326397207468738
-0.57212454025559
-0.552548081664224
-0.240243601603045
0.322411611687398
0.63031194684375
0.601435260424315
-0.733286674293114
-0.160048874289102
0.395985413152027
0.525887497510421
0.819053315388188
0.175417251536123
0.722306904374839
1.20239659491249
0.181074936222386
0.0276801516692858
-0.448687018044392
-0.252681576296468
0.188300610510945
-0.140216623112251
-1.04008096616512
-0.165183747591579
-0.938098572061686
0.684172588835534
-0.420125533725554
-0.354904957071316
-0.4402095680533
-1.11887539966437
0.0686400022043326
0.616722079681329
0.0574427529225043
0.267702495589702
0.130908769970717
0.588947022236224
0.00572931435708336
-0.884969238670104
-0.428900391013364
-0.717442437909778
1.42772319395733
-0.361531025868097
-0.321237196333656
1.06722596915131
-0.332899505579197
0.26755493532206
-0.558802728167511
0.942244603297444
0.107679924170957
0.814030268383651
-0.0649701591623913
0.345683590782759
-0.473259366239089
-0.240987317958988
0.0553112760130569
0.177992065057002
-0.284191254343871
-0.425390201231931
-0.207811605240561
-0.175665136353111
-0.705543674058894
-0.0650415677691152
-0.234295874318436
-0.418030927809276
0.0635320706909568
0.132842596804431
0.794190648939422
-0.73377919048655
-0.499463118892945
1.0823913949472
-0.195664376868674
-0.575471991412053
0.555335920251613
-0.271927109253379
0.319592469889687
0.481050873165319
-0.81262021317062
-0.0584292465299886
0.319300019076322
0.340162410381212
-0.380473820282668
-0.388108344689168
0.1685144205457
0.19661884652462
0.26596991387649
-0.564467948412353
-0.0741103952017459
-0.245462478711435
-0.000631389767717157
0.21369186850472
-0.514624433562774
1.10591298637288
-1.14042349320031
0.295885098368021
0.192716129404743
0.0823072358236444
-0.731740593269919
0.0993108813730623
-0.291266196318867
0.526798104732335
-0.626284045784558
0.508876657405255
-0.280279966198115
0.627929970101604
-0.540421906008141
-0.193912943095855
-0.0379846560049439
-0.0737620940200367
-0.242637925283929
-0.43061966286576
-0.266578730393379
-0.450848285320423
0.543203273712881
0.292437550526524
0.629960879633487
0.386023446038045
-0.464131363980343
-0.162091265117137
-0.109311710481473
0.20677906778099
-0.37094182476461
0.20588715574871
-0.0868138860576414
-0.00924483533251914
-0.035028252015474
0.0295215511211097
-0.325457742777693
1.5287688391028
0.227700386364204
-0.575536684551323
0.594316510035283
0.37515803465652
-0.979807120225475
-0.738198925523941
-0.295608030285202
0.782269508648891
-0.284789999159792
-0.508090777505369
-0.101173612551281
1.69597904328625
0.086829700334653
-0.935437972650999
0.0840202441044248
-0.0643789061728054
-0.198880993996696
-0.384724066124665
0.0966832596098639
0.0394759573842357
0.24174601300767
0.371825825061598
-0.398580802115616
0.45354385188004
0.616055635170869
-0.182235403952182
0.705125508379164
-0.285630485701543
-0.940748069114885
-0.014961905002704
1.03118125958687
0.812714622723828
0.243127768612531
0.127520356958432
-0.118773508976652
0.201560302973906
0.562828109567238
0.0391841113595466
0.360470642466829
0.00772967872452958
0.520052218813871
0.139321006052018
-0.101107123566241
-0.484393929597704
-0.0577542791946067
-0.217551105155161
-0.118845039993276
-0.403654241306614
0.601786011528683
0.320911327979352
-0.378816314728272
-0.500309884634342
0.315580203318693
-0.0462215702820841
-0.00959682701864157
-0.380509115912296
0.161104299620352
-0.215810334992719
-0.231483682772306
-0.287379168849087
0.264765750682409
0.147438035699739
-0.17225831298373
-0.141335815257429
-0.845249892948913
-0.07116998703821
-0.0997707750594097
0.312584621295038
-0.18488831144209
0.142210938319076
-0.258926987546677
-0.183942477676906
0.0508125243226326
-0.00280282672347421
0.264522266026299
-0.669526688925139
0.598261632565563
0.315603413028144
0.542034140419243
-0.123599550236359
-0.45576812630854
-0.1808289556203
0.423548877510037
0.194698872251254
0.318093988550252
-0.167334117885655
0.882620358576855
0.0779770145823035
0.822929220036745
0.82078582440982
1.77156595829527
-0.557596462788049
0.184472787533444
-0.192694836126969
0.102324610915626
-1.12766298471575
-0.0361697366624325
0.110558331268631
-0.216038204587198
0.0312628043758275
-0.555202236199007
-0.105295179383841
-0.417645222751738
-0.363087876677658
-0.259927749492462
-0.030401184504028
-0.425424354754701
0.278579063448424
0.570200202631144
0.317954382336276
-0.606007242696958
0.054328479815075
0.133601531670096
-0.213625062404201
-0.677702387621696
0.454032416538173
-0.267527781164567
-0.842082469327236
0.0427494961667498
0.274541114949432
-0.425880299551981
0.414783000696576
-0.322934639291387
0.0091548270495709
-0.135962089356316
-0.916593232971699
0.147517728153363
-0.278725545439982
0.29705481550847
-0.249411715064457
0.281147963945179
-0.633173983199397
0.83947288177533
-0.335288304565954
-0.0537324995837137
-0.226994780779326
0.000315597805152081
0.516500742069267

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0447137390230427 \tabularnewline
-0.068778362173901 \tabularnewline
0.200064905344157 \tabularnewline
0.369647506184097 \tabularnewline
1.52302520143469 \tabularnewline
-0.382348235575581 \tabularnewline
0.442481123302953 \tabularnewline
-0.548688225400163 \tabularnewline
-0.326734158696823 \tabularnewline
1.30468259726711 \tabularnewline
-1.3499961122333 \tabularnewline
-0.389444330854926 \tabularnewline
0.182875958172545 \tabularnewline
-0.827655945543168 \tabularnewline
-0.571203436128746 \tabularnewline
-0.727773843868161 \tabularnewline
-0.408616832728847 \tabularnewline
-0.0704102119463752 \tabularnewline
-0.812911802065448 \tabularnewline
-0.878889742265605 \tabularnewline
0.689887044964178 \tabularnewline
-0.730700715164829 \tabularnewline
0.835146028467026 \tabularnewline
-0.138182610095487 \tabularnewline
-1.62611373700985 \tabularnewline
-1.10034892357678 \tabularnewline
0.456775029302124 \tabularnewline
0.0327527399388272 \tabularnewline
0.101998161993637 \tabularnewline
0.314319979422889 \tabularnewline
-0.280297381566065 \tabularnewline
0.300458423917035 \tabularnewline
0.864927195047558 \tabularnewline
0.113438692607626 \tabularnewline
0.0734383808683579 \tabularnewline
-1.19439085824408 \tabularnewline
-0.151865082808314 \tabularnewline
-0.0210499034840303 \tabularnewline
-0.343328268139467 \tabularnewline
0.574181408860685 \tabularnewline
0.477814069872239 \tabularnewline
-0.345526543718989 \tabularnewline
0.0869606031474299 \tabularnewline
0.590525693132867 \tabularnewline
-0.490002114337156 \tabularnewline
-0.422664091888079 \tabularnewline
-0.0950390599117852 \tabularnewline
-0.100980417804073 \tabularnewline
0.509364851377814 \tabularnewline
-1.00698273663314 \tabularnewline
0.32882830855559 \tabularnewline
0.863393027878096 \tabularnewline
-0.476121982707797 \tabularnewline
-0.442131907077511 \tabularnewline
-0.0557930322146298 \tabularnewline
0.327255220315304 \tabularnewline
0.866297964346517 \tabularnewline
0.446075301346585 \tabularnewline
0.800999595390282 \tabularnewline
0.913381844758492 \tabularnewline
1.21106666248245 \tabularnewline
0.432277274667487 \tabularnewline
0.307401636283489 \tabularnewline
-0.163324594043776 \tabularnewline
-0.171057239378714 \tabularnewline
-1.03961279566805 \tabularnewline
0.0264096948412303 \tabularnewline
0.590971944182521 \tabularnewline
0.097670677054715 \tabularnewline
-0.913730369936987 \tabularnewline
-0.330725944996714 \tabularnewline
-0.39195787582803 \tabularnewline
-0.33595034452303 \tabularnewline
-0.460376742093759 \tabularnewline
-0.0292544642563706 \tabularnewline
0.496164472480486 \tabularnewline
-0.733628064837655 \tabularnewline
-0.0817196760109188 \tabularnewline
-0.515669889873024 \tabularnewline
0.578909014902339 \tabularnewline
-0.353325630092767 \tabularnewline
0.636523095994961 \tabularnewline
0.0383900142934602 \tabularnewline
-0.05717954972781 \tabularnewline
-0.858265740430048 \tabularnewline
-0.0909909671169302 \tabularnewline
0.755512537455174 \tabularnewline
-0.518648217936032 \tabularnewline
0.994492422667557 \tabularnewline
0.406664942162714 \tabularnewline
-0.616393295045222 \tabularnewline
-0.995857996261535 \tabularnewline
-0.203823892252728 \tabularnewline
0.285780542685931 \tabularnewline
0.992848968316651 \tabularnewline
-0.0326397207468738 \tabularnewline
-0.57212454025559 \tabularnewline
-0.552548081664224 \tabularnewline
-0.240243601603045 \tabularnewline
0.322411611687398 \tabularnewline
0.63031194684375 \tabularnewline
0.601435260424315 \tabularnewline
-0.733286674293114 \tabularnewline
-0.160048874289102 \tabularnewline
0.395985413152027 \tabularnewline
0.525887497510421 \tabularnewline
0.819053315388188 \tabularnewline
0.175417251536123 \tabularnewline
0.722306904374839 \tabularnewline
1.20239659491249 \tabularnewline
0.181074936222386 \tabularnewline
0.0276801516692858 \tabularnewline
-0.448687018044392 \tabularnewline
-0.252681576296468 \tabularnewline
0.188300610510945 \tabularnewline
-0.140216623112251 \tabularnewline
-1.04008096616512 \tabularnewline
-0.165183747591579 \tabularnewline
-0.938098572061686 \tabularnewline
0.684172588835534 \tabularnewline
-0.420125533725554 \tabularnewline
-0.354904957071316 \tabularnewline
-0.4402095680533 \tabularnewline
-1.11887539966437 \tabularnewline
0.0686400022043326 \tabularnewline
0.616722079681329 \tabularnewline
0.0574427529225043 \tabularnewline
0.267702495589702 \tabularnewline
0.130908769970717 \tabularnewline
0.588947022236224 \tabularnewline
0.00572931435708336 \tabularnewline
-0.884969238670104 \tabularnewline
-0.428900391013364 \tabularnewline
-0.717442437909778 \tabularnewline
1.42772319395733 \tabularnewline
-0.361531025868097 \tabularnewline
-0.321237196333656 \tabularnewline
1.06722596915131 \tabularnewline
-0.332899505579197 \tabularnewline
0.26755493532206 \tabularnewline
-0.558802728167511 \tabularnewline
0.942244603297444 \tabularnewline
0.107679924170957 \tabularnewline
0.814030268383651 \tabularnewline
-0.0649701591623913 \tabularnewline
0.345683590782759 \tabularnewline
-0.473259366239089 \tabularnewline
-0.240987317958988 \tabularnewline
0.0553112760130569 \tabularnewline
0.177992065057002 \tabularnewline
-0.284191254343871 \tabularnewline
-0.425390201231931 \tabularnewline
-0.207811605240561 \tabularnewline
-0.175665136353111 \tabularnewline
-0.705543674058894 \tabularnewline
-0.0650415677691152 \tabularnewline
-0.234295874318436 \tabularnewline
-0.418030927809276 \tabularnewline
0.0635320706909568 \tabularnewline
0.132842596804431 \tabularnewline
0.794190648939422 \tabularnewline
-0.73377919048655 \tabularnewline
-0.499463118892945 \tabularnewline
1.0823913949472 \tabularnewline
-0.195664376868674 \tabularnewline
-0.575471991412053 \tabularnewline
0.555335920251613 \tabularnewline
-0.271927109253379 \tabularnewline
0.319592469889687 \tabularnewline
0.481050873165319 \tabularnewline
-0.81262021317062 \tabularnewline
-0.0584292465299886 \tabularnewline
0.319300019076322 \tabularnewline
0.340162410381212 \tabularnewline
-0.380473820282668 \tabularnewline
-0.388108344689168 \tabularnewline
0.1685144205457 \tabularnewline
0.19661884652462 \tabularnewline
0.26596991387649 \tabularnewline
-0.564467948412353 \tabularnewline
-0.0741103952017459 \tabularnewline
-0.245462478711435 \tabularnewline
-0.000631389767717157 \tabularnewline
0.21369186850472 \tabularnewline
-0.514624433562774 \tabularnewline
1.10591298637288 \tabularnewline
-1.14042349320031 \tabularnewline
0.295885098368021 \tabularnewline
0.192716129404743 \tabularnewline
0.0823072358236444 \tabularnewline
-0.731740593269919 \tabularnewline
0.0993108813730623 \tabularnewline
-0.291266196318867 \tabularnewline
0.526798104732335 \tabularnewline
-0.626284045784558 \tabularnewline
0.508876657405255 \tabularnewline
-0.280279966198115 \tabularnewline
0.627929970101604 \tabularnewline
-0.540421906008141 \tabularnewline
-0.193912943095855 \tabularnewline
-0.0379846560049439 \tabularnewline
-0.0737620940200367 \tabularnewline
-0.242637925283929 \tabularnewline
-0.43061966286576 \tabularnewline
-0.266578730393379 \tabularnewline
-0.450848285320423 \tabularnewline
0.543203273712881 \tabularnewline
0.292437550526524 \tabularnewline
0.629960879633487 \tabularnewline
0.386023446038045 \tabularnewline
-0.464131363980343 \tabularnewline
-0.162091265117137 \tabularnewline
-0.109311710481473 \tabularnewline
0.20677906778099 \tabularnewline
-0.37094182476461 \tabularnewline
0.20588715574871 \tabularnewline
-0.0868138860576414 \tabularnewline
-0.00924483533251914 \tabularnewline
-0.035028252015474 \tabularnewline
0.0295215511211097 \tabularnewline
-0.325457742777693 \tabularnewline
1.5287688391028 \tabularnewline
0.227700386364204 \tabularnewline
-0.575536684551323 \tabularnewline
0.594316510035283 \tabularnewline
0.37515803465652 \tabularnewline
-0.979807120225475 \tabularnewline
-0.738198925523941 \tabularnewline
-0.295608030285202 \tabularnewline
0.782269508648891 \tabularnewline
-0.284789999159792 \tabularnewline
-0.508090777505369 \tabularnewline
-0.101173612551281 \tabularnewline
1.69597904328625 \tabularnewline
0.086829700334653 \tabularnewline
-0.935437972650999 \tabularnewline
0.0840202441044248 \tabularnewline
-0.0643789061728054 \tabularnewline
-0.198880993996696 \tabularnewline
-0.384724066124665 \tabularnewline
0.0966832596098639 \tabularnewline
0.0394759573842357 \tabularnewline
0.24174601300767 \tabularnewline
0.371825825061598 \tabularnewline
-0.398580802115616 \tabularnewline
0.45354385188004 \tabularnewline
0.616055635170869 \tabularnewline
-0.182235403952182 \tabularnewline
0.705125508379164 \tabularnewline
-0.285630485701543 \tabularnewline
-0.940748069114885 \tabularnewline
-0.014961905002704 \tabularnewline
1.03118125958687 \tabularnewline
0.812714622723828 \tabularnewline
0.243127768612531 \tabularnewline
0.127520356958432 \tabularnewline
-0.118773508976652 \tabularnewline
0.201560302973906 \tabularnewline
0.562828109567238 \tabularnewline
0.0391841113595466 \tabularnewline
0.360470642466829 \tabularnewline
0.00772967872452958 \tabularnewline
0.520052218813871 \tabularnewline
0.139321006052018 \tabularnewline
-0.101107123566241 \tabularnewline
-0.484393929597704 \tabularnewline
-0.0577542791946067 \tabularnewline
-0.217551105155161 \tabularnewline
-0.118845039993276 \tabularnewline
-0.403654241306614 \tabularnewline
0.601786011528683 \tabularnewline
0.320911327979352 \tabularnewline
-0.378816314728272 \tabularnewline
-0.500309884634342 \tabularnewline
0.315580203318693 \tabularnewline
-0.0462215702820841 \tabularnewline
-0.00959682701864157 \tabularnewline
-0.380509115912296 \tabularnewline
0.161104299620352 \tabularnewline
-0.215810334992719 \tabularnewline
-0.231483682772306 \tabularnewline
-0.287379168849087 \tabularnewline
0.264765750682409 \tabularnewline
0.147438035699739 \tabularnewline
-0.17225831298373 \tabularnewline
-0.141335815257429 \tabularnewline
-0.845249892948913 \tabularnewline
-0.07116998703821 \tabularnewline
-0.0997707750594097 \tabularnewline
0.312584621295038 \tabularnewline
-0.18488831144209 \tabularnewline
0.142210938319076 \tabularnewline
-0.258926987546677 \tabularnewline
-0.183942477676906 \tabularnewline
0.0508125243226326 \tabularnewline
-0.00280282672347421 \tabularnewline
0.264522266026299 \tabularnewline
-0.669526688925139 \tabularnewline
0.598261632565563 \tabularnewline
0.315603413028144 \tabularnewline
0.542034140419243 \tabularnewline
-0.123599550236359 \tabularnewline
-0.45576812630854 \tabularnewline
-0.1808289556203 \tabularnewline
0.423548877510037 \tabularnewline
0.194698872251254 \tabularnewline
0.318093988550252 \tabularnewline
-0.167334117885655 \tabularnewline
0.882620358576855 \tabularnewline
0.0779770145823035 \tabularnewline
0.822929220036745 \tabularnewline
0.82078582440982 \tabularnewline
1.77156595829527 \tabularnewline
-0.557596462788049 \tabularnewline
0.184472787533444 \tabularnewline
-0.192694836126969 \tabularnewline
0.102324610915626 \tabularnewline
-1.12766298471575 \tabularnewline
-0.0361697366624325 \tabularnewline
0.110558331268631 \tabularnewline
-0.216038204587198 \tabularnewline
0.0312628043758275 \tabularnewline
-0.555202236199007 \tabularnewline
-0.105295179383841 \tabularnewline
-0.417645222751738 \tabularnewline
-0.363087876677658 \tabularnewline
-0.259927749492462 \tabularnewline
-0.030401184504028 \tabularnewline
-0.425424354754701 \tabularnewline
0.278579063448424 \tabularnewline
0.570200202631144 \tabularnewline
0.317954382336276 \tabularnewline
-0.606007242696958 \tabularnewline
0.054328479815075 \tabularnewline
0.133601531670096 \tabularnewline
-0.213625062404201 \tabularnewline
-0.677702387621696 \tabularnewline
0.454032416538173 \tabularnewline
-0.267527781164567 \tabularnewline
-0.842082469327236 \tabularnewline
0.0427494961667498 \tabularnewline
0.274541114949432 \tabularnewline
-0.425880299551981 \tabularnewline
0.414783000696576 \tabularnewline
-0.322934639291387 \tabularnewline
0.0091548270495709 \tabularnewline
-0.135962089356316 \tabularnewline
-0.916593232971699 \tabularnewline
0.147517728153363 \tabularnewline
-0.278725545439982 \tabularnewline
0.29705481550847 \tabularnewline
-0.249411715064457 \tabularnewline
0.281147963945179 \tabularnewline
-0.633173983199397 \tabularnewline
0.83947288177533 \tabularnewline
-0.335288304565954 \tabularnewline
-0.0537324995837137 \tabularnewline
-0.226994780779326 \tabularnewline
0.000315597805152081 \tabularnewline
0.516500742069267 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203087&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0447137390230427[/C][/ROW]
[ROW][C]-0.068778362173901[/C][/ROW]
[ROW][C]0.200064905344157[/C][/ROW]
[ROW][C]0.369647506184097[/C][/ROW]
[ROW][C]1.52302520143469[/C][/ROW]
[ROW][C]-0.382348235575581[/C][/ROW]
[ROW][C]0.442481123302953[/C][/ROW]
[ROW][C]-0.548688225400163[/C][/ROW]
[ROW][C]-0.326734158696823[/C][/ROW]
[ROW][C]1.30468259726711[/C][/ROW]
[ROW][C]-1.3499961122333[/C][/ROW]
[ROW][C]-0.389444330854926[/C][/ROW]
[ROW][C]0.182875958172545[/C][/ROW]
[ROW][C]-0.827655945543168[/C][/ROW]
[ROW][C]-0.571203436128746[/C][/ROW]
[ROW][C]-0.727773843868161[/C][/ROW]
[ROW][C]-0.408616832728847[/C][/ROW]
[ROW][C]-0.0704102119463752[/C][/ROW]
[ROW][C]-0.812911802065448[/C][/ROW]
[ROW][C]-0.878889742265605[/C][/ROW]
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[ROW][C]-0.633173983199397[/C][/ROW]
[ROW][C]0.83947288177533[/C][/ROW]
[ROW][C]-0.335288304565954[/C][/ROW]
[ROW][C]-0.0537324995837137[/C][/ROW]
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[ROW][C]0.000315597805152081[/C][/ROW]
[ROW][C]0.516500742069267[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203087&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203087&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
-0.0447137390230427
-0.068778362173901
0.200064905344157
0.369647506184097
1.52302520143469
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0.442481123302953
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1.30468259726711
-1.3499961122333
-0.389444330854926
0.182875958172545
-0.827655945543168
-0.571203436128746
-0.727773843868161
-0.408616832728847
-0.0704102119463752
-0.812911802065448
-0.878889742265605
0.689887044964178
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0.835146028467026
-0.138182610095487
-1.62611373700985
-1.10034892357678
0.456775029302124
0.0327527399388272
0.101998161993637
0.314319979422889
-0.280297381566065
0.300458423917035
0.864927195047558
0.113438692607626
0.0734383808683579
-1.19439085824408
-0.151865082808314
-0.0210499034840303
-0.343328268139467
0.574181408860685
0.477814069872239
-0.345526543718989
0.0869606031474299
0.590525693132867
-0.490002114337156
-0.422664091888079
-0.0950390599117852
-0.100980417804073
0.509364851377814
-1.00698273663314
0.32882830855559
0.863393027878096
-0.476121982707797
-0.442131907077511
-0.0557930322146298
0.327255220315304
0.866297964346517
0.446075301346585
0.800999595390282
0.913381844758492
1.21106666248245
0.432277274667487
0.307401636283489
-0.163324594043776
-0.171057239378714
-1.03961279566805
0.0264096948412303
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Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; 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')