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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 08:06:06 -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/t1356095280awc3b9khaorhma1.htm/, Retrieved Fri, 29 Mar 2024 14:22:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203604, Retrieved Fri, 29 Mar 2024 14:22:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact56
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [] [2012-12-21 13:06:06] [ce0248448cbe5ad845933386ae480935] [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 time9 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 & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203604&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]9 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=203604&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203604&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 time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ma1sma1
Estimates ( 1 )0.46190.1882-0.3768-0.721
(p-val)(0.0077 )(0.0044 )(0.0306 )(0 )
Estimates ( 2 )0.10940.24170-0.724
(p-val)(0.0348 )(0 )(NA )(0 )
Estimates ( 3 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANA
(p-val)(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ma1 & sma1 \tabularnewline
Estimates ( 1 ) & 0.4619 & 0.1882 & -0.3768 & -0.721 \tabularnewline
(p-val) & (0.0077 ) & (0.0044 ) & (0.0306 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.1094 & 0.2417 & 0 & -0.724 \tabularnewline
(p-val) & (0.0348 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 3 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203604&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]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.4619[/C][C]0.1882[/C][C]-0.3768[/C][C]-0.721[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0077 )[/C][C](0.0044 )[/C][C](0.0306 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1094[/C][C]0.2417[/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](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/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][/ROW]
[ROW][C]Estimates ( 4 )[/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][/ROW]
[ROW][C]Estimates ( 5 )[/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][/ROW]
[ROW][C]Estimates ( 6 )[/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][/ROW]
[ROW][C]Estimates ( 7 )[/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][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203604&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203604&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
Iterationar1ar2ma1sma1
Estimates ( 1 )0.46190.1882-0.3768-0.721
(p-val)(0.0077 )(0.0044 )(0.0306 )(0 )
Estimates ( 2 )0.10940.24170-0.724
(p-val)(0.0348 )(0 )(NA )(0 )
Estimates ( 3 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANA
(p-val)(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
-0.0447135975564015
-0.068851077389192
0.19975885994943
0.369237718927883
1.52891008716395
-0.362461380458287
0.456332847475961
-0.580190969979705
-0.364333253660248
1.27450372436959
-1.35440590062028
-0.363598699194249
0.165785243750222
-0.850601443886277
-0.559434619476288
-0.731897931714867
-0.402813853637869
-0.0477724404679542
-0.776849659844251
-0.839353539146016
0.70897112622612
-0.69968966146469
0.895086299414392
-0.0956342505965127
-1.57974817944645
-1.0972358836624
0.417039930315061
0.0439997587924109
0.167496313026295
0.365238294769197
-0.251725162839035
0.318837141580976
0.86573415749751
0.124189182294537
0.0903329569178606
-1.20515699935903
-0.183214652104578
-0.063273993119216
-0.348733260268386
0.59347410057631
0.492799631360409
-0.314844126750646
0.100870214135639
0.573039262494829
-0.494356777582491
-0.421225792025915
-0.119085785135963
-0.117012806045119
0.520332372968346
-0.993117054371473
0.33798358899519
0.85311714464704
-0.457610015448462
-0.413607276824214
-0.0766437074878558
0.306427995129002
0.875079155084385
0.468637235479884
0.823142613350431
0.915708767009251
1.20366940089563
0.416221225201744
0.273455918705473
-0.224500131619378
-0.243846206234801
-1.10646962372921
-0.0326091583785065
0.549057326731057
0.103171731988667
-0.884283825633521
-0.333679108830001
-0.414771401947834
-0.335151161309839
-0.445011431268229
-0.0128575814715765
0.519556851120394
-0.69698077493146
-0.0498598984938788
-0.514252272196781
0.583668607531954
-0.333319122170617
0.662837243948878
0.0561917854880375
-0.041152497735812
-0.853189729557153
-0.113091473746239
0.734507295745328
-0.50641355320503
1.02342654951866
0.411021443268426
-0.600246448228955
-0.99896682661693
-0.252766413008753
0.252299552234367
1.00571064251044
0.0102246939947914
-0.537293108635791
-0.563639189076632
-0.275112656990782
0.307454958018172
0.642645434743438
0.633466903585246
-0.703923157889356
-0.161817894215063
0.360736548150943
0.511094850347629
0.835857700200639
0.189891892600526
0.722949816590086
1.18106818534133
0.164913915264453
0.0100164918248854
-0.50276182086032
-0.318345598314152
0.134347355642333
-0.168654846711318
-1.04340733949278
-0.178322274835372
-0.957227812085651
0.690930421824966
-0.395368902686234
-0.311809593084826
-0.415378680777951
-1.1144429489918
0.0777377605289671
0.626822819735826
0.105795349188376
0.325709218844843
0.15435436526943
0.598011484541181
0.0114304228401893
-0.884708349528124
-0.452863224576263
-0.758032197836134
1.41250623643531
-0.340443066035576
-0.272356293091256
1.07383377870599
-0.341684610148132
0.288713148318133
-0.570178375467876
0.920864591771103
0.0990618277792076
0.823573067114276
-0.0609090690118148
0.332837291098084
-0.497230844802295
-0.274357415890976
0.021132006856406
0.156660834815392
-0.280694998417876
-0.422116334829533
-0.216952569168654
-0.182930099751707
-0.698085771341028
-0.055519222900064
-0.227526605979338
-0.395395304282079
0.087790466841846
0.150835187390292
0.824188997689988
-0.699442817902407
-0.48070340371998
1.06253284964567
-0.201920241081153
-0.543002645536571
0.549311528954242
-0.291324700646986
0.33062507296194
0.482571358168004
-0.80852116233046
-0.0584059794301274
0.29128914268057
0.335777312261192
-0.360563114757611
-0.38394667279532
0.151464453516804
0.186525703129928
0.2793241297717
-0.549542473080587
-0.0743318544245414
-0.260815505175626
-0.00535042577469104
0.220801673208287
-0.502188957913164
1.11771421710809
-1.13397642838515
0.310238692404703
0.176860485751251
0.0813948602184539
-0.711425327957502
0.0913318649965922
-0.304666277507114
0.53176251242292
-0.609982158534
0.522866800848758
-0.277808945039933
0.636558927528419
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-0.234326630232533
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0.552249715754605
0.313444851225879
0.667294124561339
0.415519848557979
-0.453800038035257
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0.207246918668066
-0.0896899164201706
-0.00469030876669471
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0.0294783221899896
-0.322250688756535
1.52890777947118
0.237391408950256
-0.544594140716878
0.581191326819688
0.33066895059344
-0.986825040953978
-0.754572168836486
-0.344428455761885
0.759526017838667
-0.260033466575689
-0.467803398721052
-0.0940196345490337
1.68862806152533
0.115040238710846
-0.88988033460933
0.0676016712778089
-0.11720265688258
-0.209507138112201
-0.382410506399521
0.091055970867866
0.0417472945933687
0.256954006503187
0.389318400645662
-0.384469280220314
0.458532144139405
0.604655486005763
-0.177248281800628
0.709930417197372
-0.303394376059225
-0.953853005657786
-0.048299122668885
0.991525873334455
0.825956140421231
0.284996455251105
0.139735056932413
-0.144410002936183
0.166306721614458
0.530924855796225
0.0237506524681535
0.353800138884694
-0.0112804035238849
0.499744152029792
0.12193057979977
-0.114215564176263
-0.50428076136487
-0.0904212283111114
-0.243343899338681
-0.125828500021664
-0.400377281112512
0.604748667254737
0.332080862775385
-0.350557374664389
-0.488561676867702
0.29515474521128
-0.0565870545443949
0.00478840222779325
-0.371090500197721
0.158928091484615
-0.2166489709501
-0.22406799712593
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-0.128481389444627
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0.328071235215951
-0.154768162022171
0.165799027663996
-0.247712618260516
-0.178864229664554
0.051092530366185
-0.00189317099383931
0.275780381123414
-0.658673634517877
0.601320873752928
0.310561088990036
0.558385655357484
-0.106050295870301
-0.461152112770098
-0.205809008257585
0.39237861509895
0.190088475750179
0.332604769074611
-0.160478793744549
0.876919262045452
0.069807115348101
0.8243014746347
0.81179831568855
1.76116717161172
-0.560099455670421
0.157428460686782
-0.271266442117145
0.0298150076274938
-1.16844648188344
-0.0760071792925438
0.0762442163813859
-0.213133097363578
0.0554242326635321
-0.54510602198665
-0.0970187838929338
-0.414561795356839
-0.354164227684292
-0.247488454636249
-0.0173177708953856
-0.401574770640281
0.302548944683364
0.590591902515323
0.350161204896005
-0.574360262885138
0.0527131225430806
0.110062420335209
-0.218477278227609
-0.671792343991023
0.443222994915277
-0.272979855092307
-0.823082143575851
0.0464802606853471
0.266124406707183
-0.400171438861263
0.443833899853294
-0.312784943153084
0.0242214760106831
-0.130728880695374
-0.915920746662384
0.146465382814139
-0.285944694135481
0.316850276006811
-0.22309923793668
0.301762330775462
-0.618747380276495
0.845445889852936
-0.330790134887514
-0.0371750227443185
-0.226828455263526
-0.0121392879717632
0.516891082519025

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0447135975564015 \tabularnewline
-0.068851077389192 \tabularnewline
0.19975885994943 \tabularnewline
0.369237718927883 \tabularnewline
1.52891008716395 \tabularnewline
-0.362461380458287 \tabularnewline
0.456332847475961 \tabularnewline
-0.580190969979705 \tabularnewline
-0.364333253660248 \tabularnewline
1.27450372436959 \tabularnewline
-1.35440590062028 \tabularnewline
-0.363598699194249 \tabularnewline
0.165785243750222 \tabularnewline
-0.850601443886277 \tabularnewline
-0.559434619476288 \tabularnewline
-0.731897931714867 \tabularnewline
-0.402813853637869 \tabularnewline
-0.0477724404679542 \tabularnewline
-0.776849659844251 \tabularnewline
-0.839353539146016 \tabularnewline
0.70897112622612 \tabularnewline
-0.69968966146469 \tabularnewline
0.895086299414392 \tabularnewline
-0.0956342505965127 \tabularnewline
-1.57974817944645 \tabularnewline
-1.0972358836624 \tabularnewline
0.417039930315061 \tabularnewline
0.0439997587924109 \tabularnewline
0.167496313026295 \tabularnewline
0.365238294769197 \tabularnewline
-0.251725162839035 \tabularnewline
0.318837141580976 \tabularnewline
0.86573415749751 \tabularnewline
0.124189182294537 \tabularnewline
0.0903329569178606 \tabularnewline
-1.20515699935903 \tabularnewline
-0.183214652104578 \tabularnewline
-0.063273993119216 \tabularnewline
-0.348733260268386 \tabularnewline
0.59347410057631 \tabularnewline
0.492799631360409 \tabularnewline
-0.314844126750646 \tabularnewline
0.100870214135639 \tabularnewline
0.573039262494829 \tabularnewline
-0.494356777582491 \tabularnewline
-0.421225792025915 \tabularnewline
-0.119085785135963 \tabularnewline
-0.117012806045119 \tabularnewline
0.520332372968346 \tabularnewline
-0.993117054371473 \tabularnewline
0.33798358899519 \tabularnewline
0.85311714464704 \tabularnewline
-0.457610015448462 \tabularnewline
-0.413607276824214 \tabularnewline
-0.0766437074878558 \tabularnewline
0.306427995129002 \tabularnewline
0.875079155084385 \tabularnewline
0.468637235479884 \tabularnewline
0.823142613350431 \tabularnewline
0.915708767009251 \tabularnewline
1.20366940089563 \tabularnewline
0.416221225201744 \tabularnewline
0.273455918705473 \tabularnewline
-0.224500131619378 \tabularnewline
-0.243846206234801 \tabularnewline
-1.10646962372921 \tabularnewline
-0.0326091583785065 \tabularnewline
0.549057326731057 \tabularnewline
0.103171731988667 \tabularnewline
-0.884283825633521 \tabularnewline
-0.333679108830001 \tabularnewline
-0.414771401947834 \tabularnewline
-0.335151161309839 \tabularnewline
-0.445011431268229 \tabularnewline
-0.0128575814715765 \tabularnewline
0.519556851120394 \tabularnewline
-0.69698077493146 \tabularnewline
-0.0498598984938788 \tabularnewline
-0.514252272196781 \tabularnewline
0.583668607531954 \tabularnewline
-0.333319122170617 \tabularnewline
0.662837243948878 \tabularnewline
0.0561917854880375 \tabularnewline
-0.041152497735812 \tabularnewline
-0.853189729557153 \tabularnewline
-0.113091473746239 \tabularnewline
0.734507295745328 \tabularnewline
-0.50641355320503 \tabularnewline
1.02342654951866 \tabularnewline
0.411021443268426 \tabularnewline
-0.600246448228955 \tabularnewline
-0.99896682661693 \tabularnewline
-0.252766413008753 \tabularnewline
0.252299552234367 \tabularnewline
1.00571064251044 \tabularnewline
0.0102246939947914 \tabularnewline
-0.537293108635791 \tabularnewline
-0.563639189076632 \tabularnewline
-0.275112656990782 \tabularnewline
0.307454958018172 \tabularnewline
0.642645434743438 \tabularnewline
0.633466903585246 \tabularnewline
-0.703923157889356 \tabularnewline
-0.161817894215063 \tabularnewline
0.360736548150943 \tabularnewline
0.511094850347629 \tabularnewline
0.835857700200639 \tabularnewline
0.189891892600526 \tabularnewline
0.722949816590086 \tabularnewline
1.18106818534133 \tabularnewline
0.164913915264453 \tabularnewline
0.0100164918248854 \tabularnewline
-0.50276182086032 \tabularnewline
-0.318345598314152 \tabularnewline
0.134347355642333 \tabularnewline
-0.168654846711318 \tabularnewline
-1.04340733949278 \tabularnewline
-0.178322274835372 \tabularnewline
-0.957227812085651 \tabularnewline
0.690930421824966 \tabularnewline
-0.395368902686234 \tabularnewline
-0.311809593084826 \tabularnewline
-0.415378680777951 \tabularnewline
-1.1144429489918 \tabularnewline
0.0777377605289671 \tabularnewline
0.626822819735826 \tabularnewline
0.105795349188376 \tabularnewline
0.325709218844843 \tabularnewline
0.15435436526943 \tabularnewline
0.598011484541181 \tabularnewline
0.0114304228401893 \tabularnewline
-0.884708349528124 \tabularnewline
-0.452863224576263 \tabularnewline
-0.758032197836134 \tabularnewline
1.41250623643531 \tabularnewline
-0.340443066035576 \tabularnewline
-0.272356293091256 \tabularnewline
1.07383377870599 \tabularnewline
-0.341684610148132 \tabularnewline
0.288713148318133 \tabularnewline
-0.570178375467876 \tabularnewline
0.920864591771103 \tabularnewline
0.0990618277792076 \tabularnewline
0.823573067114276 \tabularnewline
-0.0609090690118148 \tabularnewline
0.332837291098084 \tabularnewline
-0.497230844802295 \tabularnewline
-0.274357415890976 \tabularnewline
0.021132006856406 \tabularnewline
0.156660834815392 \tabularnewline
-0.280694998417876 \tabularnewline
-0.422116334829533 \tabularnewline
-0.216952569168654 \tabularnewline
-0.182930099751707 \tabularnewline
-0.698085771341028 \tabularnewline
-0.055519222900064 \tabularnewline
-0.227526605979338 \tabularnewline
-0.395395304282079 \tabularnewline
0.087790466841846 \tabularnewline
0.150835187390292 \tabularnewline
0.824188997689988 \tabularnewline
-0.699442817902407 \tabularnewline
-0.48070340371998 \tabularnewline
1.06253284964567 \tabularnewline
-0.201920241081153 \tabularnewline
-0.543002645536571 \tabularnewline
0.549311528954242 \tabularnewline
-0.291324700646986 \tabularnewline
0.33062507296194 \tabularnewline
0.482571358168004 \tabularnewline
-0.80852116233046 \tabularnewline
-0.0584059794301274 \tabularnewline
0.29128914268057 \tabularnewline
0.335777312261192 \tabularnewline
-0.360563114757611 \tabularnewline
-0.38394667279532 \tabularnewline
0.151464453516804 \tabularnewline
0.186525703129928 \tabularnewline
0.2793241297717 \tabularnewline
-0.549542473080587 \tabularnewline
-0.0743318544245414 \tabularnewline
-0.260815505175626 \tabularnewline
-0.00535042577469104 \tabularnewline
0.220801673208287 \tabularnewline
-0.502188957913164 \tabularnewline
1.11771421710809 \tabularnewline
-1.13397642838515 \tabularnewline
0.310238692404703 \tabularnewline
0.176860485751251 \tabularnewline
0.0813948602184539 \tabularnewline
-0.711425327957502 \tabularnewline
0.0913318649965922 \tabularnewline
-0.304666277507114 \tabularnewline
0.53176251242292 \tabularnewline
-0.609982158534 \tabularnewline
0.522866800848758 \tabularnewline
-0.277808945039933 \tabularnewline
0.636558927528419 \tabularnewline
-0.531300720466899 \tabularnewline
-0.191649864679672 \tabularnewline
-0.0507802811785204 \tabularnewline
-0.0853233870119887 \tabularnewline
-0.234326630232533 \tabularnewline
-0.426080253503842 \tabularnewline
-0.266141480294914 \tabularnewline
-0.44939188007748 \tabularnewline
0.552249715754605 \tabularnewline
0.313444851225879 \tabularnewline
0.667294124561339 \tabularnewline
0.415519848557979 \tabularnewline
-0.453800038035257 \tabularnewline
-0.173880806260092 \tabularnewline
-0.145531266812513 \tabularnewline
0.184780482334863 \tabularnewline
-0.372914391733875 \tabularnewline
0.207246918668066 \tabularnewline
-0.0896899164201706 \tabularnewline
-0.00469030876669471 \tabularnewline
-0.0300931075121586 \tabularnewline
0.0294783221899896 \tabularnewline
-0.322250688756535 \tabularnewline
1.52890777947118 \tabularnewline
0.237391408950256 \tabularnewline
-0.544594140716878 \tabularnewline
0.581191326819688 \tabularnewline
0.33066895059344 \tabularnewline
-0.986825040953978 \tabularnewline
-0.754572168836486 \tabularnewline
-0.344428455761885 \tabularnewline
0.759526017838667 \tabularnewline
-0.260033466575689 \tabularnewline
-0.467803398721052 \tabularnewline
-0.0940196345490337 \tabularnewline
1.68862806152533 \tabularnewline
0.115040238710846 \tabularnewline
-0.88988033460933 \tabularnewline
0.0676016712778089 \tabularnewline
-0.11720265688258 \tabularnewline
-0.209507138112201 \tabularnewline
-0.382410506399521 \tabularnewline
0.091055970867866 \tabularnewline
0.0417472945933687 \tabularnewline
0.256954006503187 \tabularnewline
0.389318400645662 \tabularnewline
-0.384469280220314 \tabularnewline
0.458532144139405 \tabularnewline
0.604655486005763 \tabularnewline
-0.177248281800628 \tabularnewline
0.709930417197372 \tabularnewline
-0.303394376059225 \tabularnewline
-0.953853005657786 \tabularnewline
-0.048299122668885 \tabularnewline
0.991525873334455 \tabularnewline
0.825956140421231 \tabularnewline
0.284996455251105 \tabularnewline
0.139735056932413 \tabularnewline
-0.144410002936183 \tabularnewline
0.166306721614458 \tabularnewline
0.530924855796225 \tabularnewline
0.0237506524681535 \tabularnewline
0.353800138884694 \tabularnewline
-0.0112804035238849 \tabularnewline
0.499744152029792 \tabularnewline
0.12193057979977 \tabularnewline
-0.114215564176263 \tabularnewline
-0.50428076136487 \tabularnewline
-0.0904212283111114 \tabularnewline
-0.243343899338681 \tabularnewline
-0.125828500021664 \tabularnewline
-0.400377281112512 \tabularnewline
0.604748667254737 \tabularnewline
0.332080862775385 \tabularnewline
-0.350557374664389 \tabularnewline
-0.488561676867702 \tabularnewline
0.29515474521128 \tabularnewline
-0.0565870545443949 \tabularnewline
0.00478840222779325 \tabularnewline
-0.371090500197721 \tabularnewline
0.158928091484615 \tabularnewline
-0.2166489709501 \tabularnewline
-0.22406799712593 \tabularnewline
-0.282626424604171 \tabularnewline
0.264514942349082 \tabularnewline
0.157522198804002 \tabularnewline
-0.151106223288107 \tabularnewline
-0.128481389444627 \tabularnewline
-0.848383716206 \tabularnewline
-0.0795563847003291 \tabularnewline
-0.107817656783217 \tabularnewline
0.328071235215951 \tabularnewline
-0.154768162022171 \tabularnewline
0.165799027663996 \tabularnewline
-0.247712618260516 \tabularnewline
-0.178864229664554 \tabularnewline
0.051092530366185 \tabularnewline
-0.00189317099383931 \tabularnewline
0.275780381123414 \tabularnewline
-0.658673634517877 \tabularnewline
0.601320873752928 \tabularnewline
0.310561088990036 \tabularnewline
0.558385655357484 \tabularnewline
-0.106050295870301 \tabularnewline
-0.461152112770098 \tabularnewline
-0.205809008257585 \tabularnewline
0.39237861509895 \tabularnewline
0.190088475750179 \tabularnewline
0.332604769074611 \tabularnewline
-0.160478793744549 \tabularnewline
0.876919262045452 \tabularnewline
0.069807115348101 \tabularnewline
0.8243014746347 \tabularnewline
0.81179831568855 \tabularnewline
1.76116717161172 \tabularnewline
-0.560099455670421 \tabularnewline
0.157428460686782 \tabularnewline
-0.271266442117145 \tabularnewline
0.0298150076274938 \tabularnewline
-1.16844648188344 \tabularnewline
-0.0760071792925438 \tabularnewline
0.0762442163813859 \tabularnewline
-0.213133097363578 \tabularnewline
0.0554242326635321 \tabularnewline
-0.54510602198665 \tabularnewline
-0.0970187838929338 \tabularnewline
-0.414561795356839 \tabularnewline
-0.354164227684292 \tabularnewline
-0.247488454636249 \tabularnewline
-0.0173177708953856 \tabularnewline
-0.401574770640281 \tabularnewline
0.302548944683364 \tabularnewline
0.590591902515323 \tabularnewline
0.350161204896005 \tabularnewline
-0.574360262885138 \tabularnewline
0.0527131225430806 \tabularnewline
0.110062420335209 \tabularnewline
-0.218477278227609 \tabularnewline
-0.671792343991023 \tabularnewline
0.443222994915277 \tabularnewline
-0.272979855092307 \tabularnewline
-0.823082143575851 \tabularnewline
0.0464802606853471 \tabularnewline
0.266124406707183 \tabularnewline
-0.400171438861263 \tabularnewline
0.443833899853294 \tabularnewline
-0.312784943153084 \tabularnewline
0.0242214760106831 \tabularnewline
-0.130728880695374 \tabularnewline
-0.915920746662384 \tabularnewline
0.146465382814139 \tabularnewline
-0.285944694135481 \tabularnewline
0.316850276006811 \tabularnewline
-0.22309923793668 \tabularnewline
0.301762330775462 \tabularnewline
-0.618747380276495 \tabularnewline
0.845445889852936 \tabularnewline
-0.330790134887514 \tabularnewline
-0.0371750227443185 \tabularnewline
-0.226828455263526 \tabularnewline
-0.0121392879717632 \tabularnewline
0.516891082519025 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203604&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0447135975564015[/C][/ROW]
[ROW][C]-0.068851077389192[/C][/ROW]
[ROW][C]0.19975885994943[/C][/ROW]
[ROW][C]0.369237718927883[/C][/ROW]
[ROW][C]1.52891008716395[/C][/ROW]
[ROW][C]-0.362461380458287[/C][/ROW]
[ROW][C]0.456332847475961[/C][/ROW]
[ROW][C]-0.580190969979705[/C][/ROW]
[ROW][C]-0.364333253660248[/C][/ROW]
[ROW][C]1.27450372436959[/C][/ROW]
[ROW][C]-1.35440590062028[/C][/ROW]
[ROW][C]-0.363598699194249[/C][/ROW]
[ROW][C]0.165785243750222[/C][/ROW]
[ROW][C]-0.850601443886277[/C][/ROW]
[ROW][C]-0.559434619476288[/C][/ROW]
[ROW][C]-0.731897931714867[/C][/ROW]
[ROW][C]-0.402813853637869[/C][/ROW]
[ROW][C]-0.0477724404679542[/C][/ROW]
[ROW][C]-0.776849659844251[/C][/ROW]
[ROW][C]-0.839353539146016[/C][/ROW]
[ROW][C]0.70897112622612[/C][/ROW]
[ROW][C]-0.69968966146469[/C][/ROW]
[ROW][C]0.895086299414392[/C][/ROW]
[ROW][C]-0.0956342505965127[/C][/ROW]
[ROW][C]-1.57974817944645[/C][/ROW]
[ROW][C]-1.0972358836624[/C][/ROW]
[ROW][C]0.417039930315061[/C][/ROW]
[ROW][C]0.0439997587924109[/C][/ROW]
[ROW][C]0.167496313026295[/C][/ROW]
[ROW][C]0.365238294769197[/C][/ROW]
[ROW][C]-0.251725162839035[/C][/ROW]
[ROW][C]0.318837141580976[/C][/ROW]
[ROW][C]0.86573415749751[/C][/ROW]
[ROW][C]0.124189182294537[/C][/ROW]
[ROW][C]0.0903329569178606[/C][/ROW]
[ROW][C]-1.20515699935903[/C][/ROW]
[ROW][C]-0.183214652104578[/C][/ROW]
[ROW][C]-0.063273993119216[/C][/ROW]
[ROW][C]-0.348733260268386[/C][/ROW]
[ROW][C]0.59347410057631[/C][/ROW]
[ROW][C]0.492799631360409[/C][/ROW]
[ROW][C]-0.314844126750646[/C][/ROW]
[ROW][C]0.100870214135639[/C][/ROW]
[ROW][C]0.573039262494829[/C][/ROW]
[ROW][C]-0.494356777582491[/C][/ROW]
[ROW][C]-0.421225792025915[/C][/ROW]
[ROW][C]-0.119085785135963[/C][/ROW]
[ROW][C]-0.117012806045119[/C][/ROW]
[ROW][C]0.520332372968346[/C][/ROW]
[ROW][C]-0.993117054371473[/C][/ROW]
[ROW][C]0.33798358899519[/C][/ROW]
[ROW][C]0.85311714464704[/C][/ROW]
[ROW][C]-0.457610015448462[/C][/ROW]
[ROW][C]-0.413607276824214[/C][/ROW]
[ROW][C]-0.0766437074878558[/C][/ROW]
[ROW][C]0.306427995129002[/C][/ROW]
[ROW][C]0.875079155084385[/C][/ROW]
[ROW][C]0.468637235479884[/C][/ROW]
[ROW][C]0.823142613350431[/C][/ROW]
[ROW][C]0.915708767009251[/C][/ROW]
[ROW][C]1.20366940089563[/C][/ROW]
[ROW][C]0.416221225201744[/C][/ROW]
[ROW][C]0.273455918705473[/C][/ROW]
[ROW][C]-0.224500131619378[/C][/ROW]
[ROW][C]-0.243846206234801[/C][/ROW]
[ROW][C]-1.10646962372921[/C][/ROW]
[ROW][C]-0.0326091583785065[/C][/ROW]
[ROW][C]0.549057326731057[/C][/ROW]
[ROW][C]0.103171731988667[/C][/ROW]
[ROW][C]-0.884283825633521[/C][/ROW]
[ROW][C]-0.333679108830001[/C][/ROW]
[ROW][C]-0.414771401947834[/C][/ROW]
[ROW][C]-0.335151161309839[/C][/ROW]
[ROW][C]-0.445011431268229[/C][/ROW]
[ROW][C]-0.0128575814715765[/C][/ROW]
[ROW][C]0.519556851120394[/C][/ROW]
[ROW][C]-0.69698077493146[/C][/ROW]
[ROW][C]-0.0498598984938788[/C][/ROW]
[ROW][C]-0.514252272196781[/C][/ROW]
[ROW][C]0.583668607531954[/C][/ROW]
[ROW][C]-0.333319122170617[/C][/ROW]
[ROW][C]0.662837243948878[/C][/ROW]
[ROW][C]0.0561917854880375[/C][/ROW]
[ROW][C]-0.041152497735812[/C][/ROW]
[ROW][C]-0.853189729557153[/C][/ROW]
[ROW][C]-0.113091473746239[/C][/ROW]
[ROW][C]0.734507295745328[/C][/ROW]
[ROW][C]-0.50641355320503[/C][/ROW]
[ROW][C]1.02342654951866[/C][/ROW]
[ROW][C]0.411021443268426[/C][/ROW]
[ROW][C]-0.600246448228955[/C][/ROW]
[ROW][C]-0.99896682661693[/C][/ROW]
[ROW][C]-0.252766413008753[/C][/ROW]
[ROW][C]0.252299552234367[/C][/ROW]
[ROW][C]1.00571064251044[/C][/ROW]
[ROW][C]0.0102246939947914[/C][/ROW]
[ROW][C]-0.537293108635791[/C][/ROW]
[ROW][C]-0.563639189076632[/C][/ROW]
[ROW][C]-0.275112656990782[/C][/ROW]
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[ROW][C]-0.618747380276495[/C][/ROW]
[ROW][C]0.845445889852936[/C][/ROW]
[ROW][C]-0.330790134887514[/C][/ROW]
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[ROW][C]-0.0121392879717632[/C][/ROW]
[ROW][C]0.516891082519025[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203604&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203604&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
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0.19975885994943
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0.0439997587924109
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0.492799631360409
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1.20366940089563
0.416221225201744
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Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 0 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 0 ; 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')