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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 computationFri, 21 Dec 2012 11:50:06 -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/21/t1356108617ia0ngkaadvdr6mc.htm/, Retrieved Fri, 29 Mar 2024 05:19:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203939, Retrieved Fri, 29 Mar 2024 05:19:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [] [2008-12-14 11:54:22] [d2d412c7f4d35ffbf5ee5ee89db327d4]
- RMP   [ARIMA Backward Selection] [] [2011-12-06 20:20:50] [b98453cac15ba1066b407e146608df68]
-   PD      [ARIMA Backward Selection] [] [2012-12-21 16:50:06] [e2ccb4f6662abf6b355cbcf28b97e404] [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 time16 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 16 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203939&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]16 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203939&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203939&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 time16 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sma1
Estimates ( 1 )0.54740.1798-0.0292-0.4593-0.0469-0.6957
(p-val)(0.0529 )(0.0074 )(0.7568 )(0.0969 )(0.5479 )(0 )
Estimates ( 2 )0.47060.18360-0.3842-0.0462-0.6958
(p-val)(0.0074 )(0.0062 )(NA )(0.0293 )(0.5533 )(0 )
Estimates ( 3 )0.46170.18820-0.37670-0.7209
(p-val)(0.0078 )(0.0044 )(NA )(0.0307 )(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 & ar3 & ma1 & sar1 & sma1 \tabularnewline
Estimates ( 1 ) & 0.5474 & 0.1798 & -0.0292 & -0.4593 & -0.0469 & -0.6957 \tabularnewline
(p-val) & (0.0529 ) & (0.0074 ) & (0.7568 ) & (0.0969 ) & (0.5479 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.4706 & 0.1836 & 0 & -0.3842 & -0.0462 & -0.6958 \tabularnewline
(p-val) & (0.0074 ) & (0.0062 ) & (NA ) & (0.0293 ) & (0.5533 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.4617 & 0.1882 & 0 & -0.3767 & 0 & -0.7209 \tabularnewline
(p-val) & (0.0078 ) & (0.0044 ) & (NA ) & (0.0307 ) & (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=203939&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]sar1[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.5474[/C][C]0.1798[/C][C]-0.0292[/C][C]-0.4593[/C][C]-0.0469[/C][C]-0.6957[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0529 )[/C][C](0.0074 )[/C][C](0.7568 )[/C][C](0.0969 )[/C][C](0.5479 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4706[/C][C]0.1836[/C][C]0[/C][C]-0.3842[/C][C]-0.0462[/C][C]-0.6958[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0074 )[/C][C](0.0062 )[/C][C](NA )[/C][C](0.0293 )[/C][C](0.5533 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4617[/C][C]0.1882[/C][C]0[/C][C]-0.3767[/C][C]0[/C][C]-0.7209[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0078 )[/C][C](0.0044 )[/C][C](NA )[/C][C](0.0307 )[/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=203939&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203939&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
Iterationar1ar2ar3ma1sar1sma1
Estimates ( 1 )0.54740.1798-0.0292-0.4593-0.0469-0.6957
(p-val)(0.0529 )(0.0074 )(0.7568 )(0.0969 )(0.5479 )(0 )
Estimates ( 2 )0.47060.18360-0.3842-0.0462-0.6958
(p-val)(0.0074 )(0.0062 )(NA )(0.0293 )(0.5533 )(0 )
Estimates ( 3 )0.46170.18820-0.37670-0.7209
(p-val)(0.0078 )(0.0044 )(NA )(0.0307 )(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
-0.0447135254057231
-0.0681917505478084
0.197918269607118
0.364977510288305
1.51337996933159
-0.359461693683337
0.457275231643101
-0.576200433829823
-0.358061445786517
1.26000911969509
-1.34266139991407
-0.353289571497946
0.161395641494175
-0.85782300297493
-0.555144912298359
-0.727027913044961
-0.369042261655499
-0.0578971497992717
-0.769036818576891
-0.853703554581848
0.700854566672244
-0.676058766832693
0.868578036694034
-0.10884588975301
-1.57887204236819
-1.12268950514398
0.39692385190415
0.0148495697973627
0.140259353790028
0.369865091064737
-0.279776101924426
0.304546129475365
0.892466302715145
0.0893800831442478
0.135269394231309
-1.20736758947938
-0.230372958786898
-0.0877971490509319
-0.33323296780618
0.60196627511901
0.48979033192109
-0.300400372794546
0.101351037939174
0.596346950582894
-0.476838868063972
-0.418379377485807
-0.117113003673828
-0.147180754345273
0.532293994236285
-0.976528360213732
0.331179682756605
0.869619341823574
-0.452578488121829
-0.42138949463861
-0.0683600581129025
0.326102644103438
0.848949464327804
0.455827855730794
0.823473300311693
0.933736135720844
1.23343911512994
0.408339888885158
0.287976014212968
-0.211203400023637
-0.267468886934765
-1.11645429924972
-0.0317247398904611
0.547972212916532
0.120802505225608
-0.868321571501203
-0.311155680962737
-0.380689102119501
-0.305618643654202
-0.409940938228271
-0.00967793776263202
0.495426344923855
-0.704347377621387
-0.0634698792084991
-0.513898813333212
0.589511460763565
-0.349423192928886
0.641305316979721
0.0343658031299827
-0.0527521706610295
-0.880138087903553
-0.110617811300362
0.725017567873349
-0.50521028991854
1.015061557493
0.432165488939549
-0.605966485103154
-0.999657590805216
-0.273898494723043
0.276038635782484
1.00336898138124
0.0129978236302341
-0.560227111581804
-0.549704221644982
-0.264710628774072
0.279628858544352
0.678638849882419
0.661069134033749
-0.704450399639775
-0.201260460274452
0.350125398121578
0.512074040088484
0.855206906549666
0.194069541456855
0.723929039883086
1.18131067430518
0.142881527616885
0.0179257999324864
-0.497956664934098
-0.299719686680144
0.135788266531753
-0.170331262015486
-1.03309848477463
-0.173819270986569
-0.963024089117263
0.698379739279439
-0.367089110910285
-0.263848476436372
-0.418819019840622
-1.12067684130113
0.0500585205773671
0.608903578527248
0.134690293435901
0.330041347787177
0.127838692569612
0.581319132516197
-0.038707943536881
-0.870097194938441
-0.462113796849836
-0.775456514875537
1.39586946762297
-0.373271731449701
-0.268080176510487
1.08656232048461
-0.325879955921929
0.30661854788457
-0.552774192453423
0.92873551861249
0.0930554068879337
0.793725485236928
-0.0655196055781843
0.31938973725902
-0.466224915112164
-0.262872803122968
0.0101377300966978
0.166709542388761
-0.27948869702557
-0.416437753013764
-0.221155653982262
-0.180257931963139
-0.698512272287555
-0.030966150632744
-0.218027303452022
-0.373584442457798
0.052951427314271
0.16272689702871
0.827781138510899
-0.724325872700048
-0.470004756981797
1.04570491743371
-0.192863384714994
-0.571180242416833
0.531291140789517
-0.305718227359467
0.338050048474922
0.476242122330785
-0.81389847635506
-0.0347526256916431
0.307275272687164
0.298265453953272
-0.356205547514144
-0.356604247457693
0.16191437451988
0.157622107243432
0.301928819166272
-0.561981939800824
-0.0543444359644914
-0.242625453729012
-0.0291188126122587
0.228470615616776
-0.510675391591978
1.12387561916997
-1.12598799910629
0.290323760058927
0.190186117613125
0.0853119358914609
-0.708038983963899
0.0821216895899207
-0.307060856617881
0.525053351110559
-0.601608778527341
0.538181815538459
-0.306512070731953
0.654542044064886
-0.537374859392079
-0.186945154314925
-0.0415286501896635
-0.0882630415428836
-0.256492309570866
-0.41305922709641
-0.270151685278353
-0.435615700218057
0.546649355951026
0.323884533274196
0.661932000134874
0.402774353219053
-0.428179650529226
-0.185363732952745
-0.146275810216944
0.177901302311226
-0.370503387950712
0.205238677067021
-0.0894107718996266
-0.0207802658241128
-0.00240155924277281
0.0272372593388978
-0.30401211751709
1.50787083654869
0.259049823633271
-0.546357645795726
0.581469268528577
0.330213798462058
-0.983366580775167
-0.736262986208755
-0.338744406461673
0.760537197604814
-0.261709569948611
-0.476048053943411
-0.110367750063647
1.69080994427218
0.149871963217245
-0.894443959355815
0.0854707859389192
-0.11791601547532
-0.215892867114871
-0.394092028282406
0.0924263195771732
0.0585916699118239
0.253245871086333
0.370568028643833
-0.386523945873816
0.447110885622225
0.623154288482199
-0.184203126128336
0.705478214339162
-0.317030724633314
-0.927475247660233
-0.0393758868190344
1.00274756958954
0.812619294578388
0.298293415694118
0.152551035780658
-0.150639291567325
0.110741928054486
0.552786542794449
0.0434953176436756
0.36320976255479
-0.0227970084706981
0.504138678148357
0.138779289305853
-0.0853035308417627
-0.496484911315902
-0.0852251911409686
-0.247830972313436
-0.120849214187909
-0.451770225463026
0.612150462832891
0.350625301752197
-0.359290488889771
-0.48443652903107
0.339430852270146
-0.0417944819768526
-0.0100068969566531
-0.403912575239062
0.151831207953882
-0.229701003816198
-0.217361643531106
-0.332188937105688
0.264417994027136
0.176869376138034
-0.176677800263279
-0.13567036715417
-0.827944200296052
-0.0722734399204132
-0.117607845832746
0.314635053724412
-0.154386181086533
0.163002765901451
-0.244739215669361
-0.204481562449259
0.0360259309576058
0.00417168114426594
0.266111866388443
-0.651003114649842
0.589994534676577
0.312322955620027
0.55266992874864
-0.0962287489408765
-0.467083613141822
-0.198196785314411
0.395575343631519
0.175735234004809
0.315653622056974
-0.16114907850581
0.882040919470631
0.0637848764427299
0.858633851994301
0.822761597215925
1.77266886690859
-0.56698109822252
0.153261675817821
-0.27923293044209
0.0495720504717388
-1.16973330350604
-0.0821094023297278
0.0681021355630733
-0.201544972117822
0.0764370043961384
-0.526360142929291
-0.080413992290807
-0.390603734038536
-0.366201530340293
-0.236325627413761
-0.0197748907207431
-0.400593230734885
0.273305313443572
0.571670833577946
0.355107022563815
-0.598053247795437
0.0680124314430932
0.0825157705041395
-0.236688909377818
-0.720993035857936
0.445855527021637
-0.278184399175569
-0.813695706192547
0.0382223634028161
0.287981462997023
-0.397401294088736
0.453441800201652
-0.336993631715909
0.0350016901300973
-0.129074066212103
-0.92948724921499
0.112538313206619
-0.266214839380579
0.318919353798613
-0.235823743806211
0.312056037492047
-0.607348899940245
0.821439552875318
-0.330769416907474
-0.0368470896545833
-0.223411512848023
-0.0162393530813711
0.494328753928909

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0447135254057231 \tabularnewline
-0.0681917505478084 \tabularnewline
0.197918269607118 \tabularnewline
0.364977510288305 \tabularnewline
1.51337996933159 \tabularnewline
-0.359461693683337 \tabularnewline
0.457275231643101 \tabularnewline
-0.576200433829823 \tabularnewline
-0.358061445786517 \tabularnewline
1.26000911969509 \tabularnewline
-1.34266139991407 \tabularnewline
-0.353289571497946 \tabularnewline
0.161395641494175 \tabularnewline
-0.85782300297493 \tabularnewline
-0.555144912298359 \tabularnewline
-0.727027913044961 \tabularnewline
-0.369042261655499 \tabularnewline
-0.0578971497992717 \tabularnewline
-0.769036818576891 \tabularnewline
-0.853703554581848 \tabularnewline
0.700854566672244 \tabularnewline
-0.676058766832693 \tabularnewline
0.868578036694034 \tabularnewline
-0.10884588975301 \tabularnewline
-1.57887204236819 \tabularnewline
-1.12268950514398 \tabularnewline
0.39692385190415 \tabularnewline
0.0148495697973627 \tabularnewline
0.140259353790028 \tabularnewline
0.369865091064737 \tabularnewline
-0.279776101924426 \tabularnewline
0.304546129475365 \tabularnewline
0.892466302715145 \tabularnewline
0.0893800831442478 \tabularnewline
0.135269394231309 \tabularnewline
-1.20736758947938 \tabularnewline
-0.230372958786898 \tabularnewline
-0.0877971490509319 \tabularnewline
-0.33323296780618 \tabularnewline
0.60196627511901 \tabularnewline
0.48979033192109 \tabularnewline
-0.300400372794546 \tabularnewline
0.101351037939174 \tabularnewline
0.596346950582894 \tabularnewline
-0.476838868063972 \tabularnewline
-0.418379377485807 \tabularnewline
-0.117113003673828 \tabularnewline
-0.147180754345273 \tabularnewline
0.532293994236285 \tabularnewline
-0.976528360213732 \tabularnewline
0.331179682756605 \tabularnewline
0.869619341823574 \tabularnewline
-0.452578488121829 \tabularnewline
-0.42138949463861 \tabularnewline
-0.0683600581129025 \tabularnewline
0.326102644103438 \tabularnewline
0.848949464327804 \tabularnewline
0.455827855730794 \tabularnewline
0.823473300311693 \tabularnewline
0.933736135720844 \tabularnewline
1.23343911512994 \tabularnewline
0.408339888885158 \tabularnewline
0.287976014212968 \tabularnewline
-0.211203400023637 \tabularnewline
-0.267468886934765 \tabularnewline
-1.11645429924972 \tabularnewline
-0.0317247398904611 \tabularnewline
0.547972212916532 \tabularnewline
0.120802505225608 \tabularnewline
-0.868321571501203 \tabularnewline
-0.311155680962737 \tabularnewline
-0.380689102119501 \tabularnewline
-0.305618643654202 \tabularnewline
-0.409940938228271 \tabularnewline
-0.00967793776263202 \tabularnewline
0.495426344923855 \tabularnewline
-0.704347377621387 \tabularnewline
-0.0634698792084991 \tabularnewline
-0.513898813333212 \tabularnewline
0.589511460763565 \tabularnewline
-0.349423192928886 \tabularnewline
0.641305316979721 \tabularnewline
0.0343658031299827 \tabularnewline
-0.0527521706610295 \tabularnewline
-0.880138087903553 \tabularnewline
-0.110617811300362 \tabularnewline
0.725017567873349 \tabularnewline
-0.50521028991854 \tabularnewline
1.015061557493 \tabularnewline
0.432165488939549 \tabularnewline
-0.605966485103154 \tabularnewline
-0.999657590805216 \tabularnewline
-0.273898494723043 \tabularnewline
0.276038635782484 \tabularnewline
1.00336898138124 \tabularnewline
0.0129978236302341 \tabularnewline
-0.560227111581804 \tabularnewline
-0.549704221644982 \tabularnewline
-0.264710628774072 \tabularnewline
0.279628858544352 \tabularnewline
0.678638849882419 \tabularnewline
0.661069134033749 \tabularnewline
-0.704450399639775 \tabularnewline
-0.201260460274452 \tabularnewline
0.350125398121578 \tabularnewline
0.512074040088484 \tabularnewline
0.855206906549666 \tabularnewline
0.194069541456855 \tabularnewline
0.723929039883086 \tabularnewline
1.18131067430518 \tabularnewline
0.142881527616885 \tabularnewline
0.0179257999324864 \tabularnewline
-0.497956664934098 \tabularnewline
-0.299719686680144 \tabularnewline
0.135788266531753 \tabularnewline
-0.170331262015486 \tabularnewline
-1.03309848477463 \tabularnewline
-0.173819270986569 \tabularnewline
-0.963024089117263 \tabularnewline
0.698379739279439 \tabularnewline
-0.367089110910285 \tabularnewline
-0.263848476436372 \tabularnewline
-0.418819019840622 \tabularnewline
-1.12067684130113 \tabularnewline
0.0500585205773671 \tabularnewline
0.608903578527248 \tabularnewline
0.134690293435901 \tabularnewline
0.330041347787177 \tabularnewline
0.127838692569612 \tabularnewline
0.581319132516197 \tabularnewline
-0.038707943536881 \tabularnewline
-0.870097194938441 \tabularnewline
-0.462113796849836 \tabularnewline
-0.775456514875537 \tabularnewline
1.39586946762297 \tabularnewline
-0.373271731449701 \tabularnewline
-0.268080176510487 \tabularnewline
1.08656232048461 \tabularnewline
-0.325879955921929 \tabularnewline
0.30661854788457 \tabularnewline
-0.552774192453423 \tabularnewline
0.92873551861249 \tabularnewline
0.0930554068879337 \tabularnewline
0.793725485236928 \tabularnewline
-0.0655196055781843 \tabularnewline
0.31938973725902 \tabularnewline
-0.466224915112164 \tabularnewline
-0.262872803122968 \tabularnewline
0.0101377300966978 \tabularnewline
0.166709542388761 \tabularnewline
-0.27948869702557 \tabularnewline
-0.416437753013764 \tabularnewline
-0.221155653982262 \tabularnewline
-0.180257931963139 \tabularnewline
-0.698512272287555 \tabularnewline
-0.030966150632744 \tabularnewline
-0.218027303452022 \tabularnewline
-0.373584442457798 \tabularnewline
0.052951427314271 \tabularnewline
0.16272689702871 \tabularnewline
0.827781138510899 \tabularnewline
-0.724325872700048 \tabularnewline
-0.470004756981797 \tabularnewline
1.04570491743371 \tabularnewline
-0.192863384714994 \tabularnewline
-0.571180242416833 \tabularnewline
0.531291140789517 \tabularnewline
-0.305718227359467 \tabularnewline
0.338050048474922 \tabularnewline
0.476242122330785 \tabularnewline
-0.81389847635506 \tabularnewline
-0.0347526256916431 \tabularnewline
0.307275272687164 \tabularnewline
0.298265453953272 \tabularnewline
-0.356205547514144 \tabularnewline
-0.356604247457693 \tabularnewline
0.16191437451988 \tabularnewline
0.157622107243432 \tabularnewline
0.301928819166272 \tabularnewline
-0.561981939800824 \tabularnewline
-0.0543444359644914 \tabularnewline
-0.242625453729012 \tabularnewline
-0.0291188126122587 \tabularnewline
0.228470615616776 \tabularnewline
-0.510675391591978 \tabularnewline
1.12387561916997 \tabularnewline
-1.12598799910629 \tabularnewline
0.290323760058927 \tabularnewline
0.190186117613125 \tabularnewline
0.0853119358914609 \tabularnewline
-0.708038983963899 \tabularnewline
0.0821216895899207 \tabularnewline
-0.307060856617881 \tabularnewline
0.525053351110559 \tabularnewline
-0.601608778527341 \tabularnewline
0.538181815538459 \tabularnewline
-0.306512070731953 \tabularnewline
0.654542044064886 \tabularnewline
-0.537374859392079 \tabularnewline
-0.186945154314925 \tabularnewline
-0.0415286501896635 \tabularnewline
-0.0882630415428836 \tabularnewline
-0.256492309570866 \tabularnewline
-0.41305922709641 \tabularnewline
-0.270151685278353 \tabularnewline
-0.435615700218057 \tabularnewline
0.546649355951026 \tabularnewline
0.323884533274196 \tabularnewline
0.661932000134874 \tabularnewline
0.402774353219053 \tabularnewline
-0.428179650529226 \tabularnewline
-0.185363732952745 \tabularnewline
-0.146275810216944 \tabularnewline
0.177901302311226 \tabularnewline
-0.370503387950712 \tabularnewline
0.205238677067021 \tabularnewline
-0.0894107718996266 \tabularnewline
-0.0207802658241128 \tabularnewline
-0.00240155924277281 \tabularnewline
0.0272372593388978 \tabularnewline
-0.30401211751709 \tabularnewline
1.50787083654869 \tabularnewline
0.259049823633271 \tabularnewline
-0.546357645795726 \tabularnewline
0.581469268528577 \tabularnewline
0.330213798462058 \tabularnewline
-0.983366580775167 \tabularnewline
-0.736262986208755 \tabularnewline
-0.338744406461673 \tabularnewline
0.760537197604814 \tabularnewline
-0.261709569948611 \tabularnewline
-0.476048053943411 \tabularnewline
-0.110367750063647 \tabularnewline
1.69080994427218 \tabularnewline
0.149871963217245 \tabularnewline
-0.894443959355815 \tabularnewline
0.0854707859389192 \tabularnewline
-0.11791601547532 \tabularnewline
-0.215892867114871 \tabularnewline
-0.394092028282406 \tabularnewline
0.0924263195771732 \tabularnewline
0.0585916699118239 \tabularnewline
0.253245871086333 \tabularnewline
0.370568028643833 \tabularnewline
-0.386523945873816 \tabularnewline
0.447110885622225 \tabularnewline
0.623154288482199 \tabularnewline
-0.184203126128336 \tabularnewline
0.705478214339162 \tabularnewline
-0.317030724633314 \tabularnewline
-0.927475247660233 \tabularnewline
-0.0393758868190344 \tabularnewline
1.00274756958954 \tabularnewline
0.812619294578388 \tabularnewline
0.298293415694118 \tabularnewline
0.152551035780658 \tabularnewline
-0.150639291567325 \tabularnewline
0.110741928054486 \tabularnewline
0.552786542794449 \tabularnewline
0.0434953176436756 \tabularnewline
0.36320976255479 \tabularnewline
-0.0227970084706981 \tabularnewline
0.504138678148357 \tabularnewline
0.138779289305853 \tabularnewline
-0.0853035308417627 \tabularnewline
-0.496484911315902 \tabularnewline
-0.0852251911409686 \tabularnewline
-0.247830972313436 \tabularnewline
-0.120849214187909 \tabularnewline
-0.451770225463026 \tabularnewline
0.612150462832891 \tabularnewline
0.350625301752197 \tabularnewline
-0.359290488889771 \tabularnewline
-0.48443652903107 \tabularnewline
0.339430852270146 \tabularnewline
-0.0417944819768526 \tabularnewline
-0.0100068969566531 \tabularnewline
-0.403912575239062 \tabularnewline
0.151831207953882 \tabularnewline
-0.229701003816198 \tabularnewline
-0.217361643531106 \tabularnewline
-0.332188937105688 \tabularnewline
0.264417994027136 \tabularnewline
0.176869376138034 \tabularnewline
-0.176677800263279 \tabularnewline
-0.13567036715417 \tabularnewline
-0.827944200296052 \tabularnewline
-0.0722734399204132 \tabularnewline
-0.117607845832746 \tabularnewline
0.314635053724412 \tabularnewline
-0.154386181086533 \tabularnewline
0.163002765901451 \tabularnewline
-0.244739215669361 \tabularnewline
-0.204481562449259 \tabularnewline
0.0360259309576058 \tabularnewline
0.00417168114426594 \tabularnewline
0.266111866388443 \tabularnewline
-0.651003114649842 \tabularnewline
0.589994534676577 \tabularnewline
0.312322955620027 \tabularnewline
0.55266992874864 \tabularnewline
-0.0962287489408765 \tabularnewline
-0.467083613141822 \tabularnewline
-0.198196785314411 \tabularnewline
0.395575343631519 \tabularnewline
0.175735234004809 \tabularnewline
0.315653622056974 \tabularnewline
-0.16114907850581 \tabularnewline
0.882040919470631 \tabularnewline
0.0637848764427299 \tabularnewline
0.858633851994301 \tabularnewline
0.822761597215925 \tabularnewline
1.77266886690859 \tabularnewline
-0.56698109822252 \tabularnewline
0.153261675817821 \tabularnewline
-0.27923293044209 \tabularnewline
0.0495720504717388 \tabularnewline
-1.16973330350604 \tabularnewline
-0.0821094023297278 \tabularnewline
0.0681021355630733 \tabularnewline
-0.201544972117822 \tabularnewline
0.0764370043961384 \tabularnewline
-0.526360142929291 \tabularnewline
-0.080413992290807 \tabularnewline
-0.390603734038536 \tabularnewline
-0.366201530340293 \tabularnewline
-0.236325627413761 \tabularnewline
-0.0197748907207431 \tabularnewline
-0.400593230734885 \tabularnewline
0.273305313443572 \tabularnewline
0.571670833577946 \tabularnewline
0.355107022563815 \tabularnewline
-0.598053247795437 \tabularnewline
0.0680124314430932 \tabularnewline
0.0825157705041395 \tabularnewline
-0.236688909377818 \tabularnewline
-0.720993035857936 \tabularnewline
0.445855527021637 \tabularnewline
-0.278184399175569 \tabularnewline
-0.813695706192547 \tabularnewline
0.0382223634028161 \tabularnewline
0.287981462997023 \tabularnewline
-0.397401294088736 \tabularnewline
0.453441800201652 \tabularnewline
-0.336993631715909 \tabularnewline
0.0350016901300973 \tabularnewline
-0.129074066212103 \tabularnewline
-0.92948724921499 \tabularnewline
0.112538313206619 \tabularnewline
-0.266214839380579 \tabularnewline
0.318919353798613 \tabularnewline
-0.235823743806211 \tabularnewline
0.312056037492047 \tabularnewline
-0.607348899940245 \tabularnewline
0.821439552875318 \tabularnewline
-0.330769416907474 \tabularnewline
-0.0368470896545833 \tabularnewline
-0.223411512848023 \tabularnewline
-0.0162393530813711 \tabularnewline
0.494328753928909 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203939&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0447135254057231[/C][/ROW]
[ROW][C]-0.0681917505478084[/C][/ROW]
[ROW][C]0.197918269607118[/C][/ROW]
[ROW][C]0.364977510288305[/C][/ROW]
[ROW][C]1.51337996933159[/C][/ROW]
[ROW][C]-0.359461693683337[/C][/ROW]
[ROW][C]0.457275231643101[/C][/ROW]
[ROW][C]-0.576200433829823[/C][/ROW]
[ROW][C]-0.358061445786517[/C][/ROW]
[ROW][C]1.26000911969509[/C][/ROW]
[ROW][C]-1.34266139991407[/C][/ROW]
[ROW][C]-0.353289571497946[/C][/ROW]
[ROW][C]0.161395641494175[/C][/ROW]
[ROW][C]-0.85782300297493[/C][/ROW]
[ROW][C]-0.555144912298359[/C][/ROW]
[ROW][C]-0.727027913044961[/C][/ROW]
[ROW][C]-0.369042261655499[/C][/ROW]
[ROW][C]-0.0578971497992717[/C][/ROW]
[ROW][C]-0.769036818576891[/C][/ROW]
[ROW][C]-0.853703554581848[/C][/ROW]
[ROW][C]0.700854566672244[/C][/ROW]
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[ROW][C]-0.607348899940245[/C][/ROW]
[ROW][C]0.821439552875318[/C][/ROW]
[ROW][C]-0.330769416907474[/C][/ROW]
[ROW][C]-0.0368470896545833[/C][/ROW]
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[ROW][C]-0.0162393530813711[/C][/ROW]
[ROW][C]0.494328753928909[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203939&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203939&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
-0.0447135254057231
-0.0681917505478084
0.197918269607118
0.364977510288305
1.51337996933159
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0.457275231643101
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1.26000911969509
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0.161395641494175
-0.85782300297493
-0.555144912298359
-0.727027913044961
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-0.0578971497992717
-0.769036818576891
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0.700854566672244
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0.868578036694034
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-1.12268950514398
0.39692385190415
0.0148495697973627
0.140259353790028
0.369865091064737
-0.279776101924426
0.304546129475365
0.892466302715145
0.0893800831442478
0.135269394231309
-1.20736758947938
-0.230372958786898
-0.0877971490509319
-0.33323296780618
0.60196627511901
0.48979033192109
-0.300400372794546
0.101351037939174
0.596346950582894
-0.476838868063972
-0.418379377485807
-0.117113003673828
-0.147180754345273
0.532293994236285
-0.976528360213732
0.331179682756605
0.869619341823574
-0.452578488121829
-0.42138949463861
-0.0683600581129025
0.326102644103438
0.848949464327804
0.455827855730794
0.823473300311693
0.933736135720844
1.23343911512994
0.408339888885158
0.287976014212968
-0.211203400023637
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-1.11645429924972
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0.547972212916532
0.120802505225608
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Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; 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')