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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, 10 Dec 2009 05:52:15 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/10/t1260449607mepcvf1g9i6r17z.htm/, Retrieved Fri, 29 Mar 2024 12:57:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65325, Retrieved Fri, 29 Mar 2024 12:57:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2009-12-07 09:18:36] [b98453cac15ba1066b407e146608df68]
- R PD    [ARIMA Backward Selection] [ARIMA backwards e...] [2009-12-10 12:52:15] [b1ac221d009d6e5c29a4ef1869874933] [Current]
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Dataseries X:
255
280.2
299.9
339.2
374.2
393.5
389.2
381.7
375.2
369
357.4
352.1
346.5
342.9
340.3
328.3
322.9
314.3
308.9
294
285.6
281.2
280.3
278.8
274.5
270.4
263.4
259.9
258
262.7
284.7
311.3
322.1
327
331.3
333.3
321.4
327
320
314.7
316.7
314.4
321.3
318.2
307.2
301.3
287.5
277.7
274.4
258.8
253.3
251
248.4
249.5
246.1
244.5
243.6
244
240.8
249.8
248
259.4
260.5
260.8
261.3
259.5
256.6
257.9
256.5
254.2
253.3
253.8
255.5
257.1
257.3
253.2
252.8
252
250.7
252.2
250
251
253.4
251.2
255.6
261.1
258.9
259.9
261.2
264.7
267.1
266.4
267.7
268.6
267.5
268.5
268.5
270.5
270.9
270.1
269.3
269.8
270.1
264.9
263.7
264.8
263.7
255.9
276.2
360.1
380.5
373.7
369.8
366.6
359.3
345.8
326.2
324.5
328.1
327.5
324.4
316.5
310.9
301.5
291.7
290.4
287.4
277.7
281.6
288
276
272.9
283
283.3
276.8
284.5
282.7
281.2
287.4
283.1
284
285.5
289.2
292.5
296.4
305.2
303.9
311.5
316.3
316.7
322.5
317.1
309.8
303.8
290.3
293.7
291.7
296.5
289.1
288.5
293.8
297.7
305.4
302.7
302.5
303
294.5
294.1
294.5
297.1
289.4
292.4
287.9
286.6
280.5
272.4
269.2
270.6
267.3
262.5
266.8
268.8
263.1
261.2
266
262.5
265.2
261.3
253.7
249.2
239.1
236.4
235.2
245.2
246.2
247.7
251.4
253.3
254.8
250
249.3
241.5
243.3
248
253
252.9
251.5
251.6
253.5
259.8
334.1
448
445.8
445
448.2
438.2
439.8
423.4
410.8
408.4
406.7
405.9
402.7
405.1
399.6
386.5
381.4
375.2
357.7
359
355
352.7
344.4
343.8
338
339
333.3
334.4
328.3
330.7
330
331.6
351.2
389.4
410.9
442.8
462.8
466.9
461.7
439.2
430.3
416.1
402.5
397.3
403.3
395.9
387.8
378.6
377.1
370.4
362
350.3
348.2
344.6
343.5
342.8
347.6
346.6
349.5
342.1
342
342.8
339.3
348.2
333.7
334.7
354
367.7
363.3
358.4
353.1
343.1
344.6
344.4
333.9
331.7
324.3
321.2
322.4
321.7
320.5
312.8
309.7
315.6
309.7
304.6
302.5
301.5
298.8
291.3
293.6
294.6
285.9
297.6
301.1
293.8
297.7
292.9
292.1
287.2
288.2
283.8
299.9
292.4
293.3
300.8
293.7
293.1
294.4
292.1
291.9
282.5
277.9
287.5
289.2
285.6
293.2
290.8
283.1
275
287.8
287.8
287.4
284
277.8
277.6
304.9
294
300.9
324
332.9
341.6
333.4
348.2
344.7
344.7
329.3
323.5
323.2
317.4
330.1
329.2
334.9
315.8
315.4
319.6
317.3
313.8
315.8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65325&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65325&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65325&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1
Estimates ( 1 )-0.29030.26680.02540.7709
(p-val)(0.2163 )(0.0412 )(0.7074 )(8e-04 )
Estimates ( 2 )-0.32760.283100.8127
(p-val)(0.0266 )(0.0025 )(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 & ar3 & ma1 \tabularnewline
Estimates ( 1 ) & -0.2903 & 0.2668 & 0.0254 & 0.7709 \tabularnewline
(p-val) & (0.2163 ) & (0.0412 ) & (0.7074 ) & (8e-04 ) \tabularnewline
Estimates ( 2 ) & -0.3276 & 0.2831 & 0 & 0.8127 \tabularnewline
(p-val) & (0.0266 ) & (0.0025 ) & (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=65325&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][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.2903[/C][C]0.2668[/C][C]0.0254[/C][C]0.7709[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2163 )[/C][C](0.0412 )[/C][C](0.7074 )[/C][C](8e-04 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.3276[/C][C]0.2831[/C][C]0[/C][C]0.8127[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0266 )[/C][C](0.0025 )[/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=65325&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65325&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
Iterationar1ar2ar3ma1
Estimates ( 1 )-0.29030.26680.02540.7709
(p-val)(0.2163 )(0.0412 )(0.7074 )(8e-04 )
Estimates ( 2 )-0.32760.283100.8127
(p-val)(0.0266 )(0.0025 )(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
0.254999839094220
22.4319591650934
8.48842388782407
31.3463855898503
16.4307531043167
5.83896338062774
-13.5110600811090
-4.38191112466065
-4.64342627735144
-2.39790452591552
-9.62528652477005
0.570943887624659
-4.32575439695619
-0.182017814293898
-1.87574824595658
-10.2057603789681
-0.230847339674417
-6.72183403274037
-0.96877058596556
-13.2888545772784
-0.821710152105348
-2.09225227224809
2.05576265908427
-1.95834748421589
-2.87374391592999
-2.70981140864819
-4.91585677432382
-0.539200028910704
-0.528445392880883
5.66770512150502
19.5912064655249
16.6782556422981
-0.324432057346130
0.62863975961177
1.67963822390779
0.371340804566055
-12.8775776267713
11.4296276252202
-11.0611990322411
0.00346328867632788
2.18399002373965
-1.81094647485137
7.2296368391813
-6.10745133373262
-8.97418755942159
-1.52360381634567
-11.3245326707611
-3.22215742294171
0.171025586455974
-13.7240838626719
1.68089407031351
-0.94630970938392
-0.673915406733183
1.61835126513499
-3.57605895217995
-0.0575932817974939
-0.440928918848127
0.992017595010534
-3.56777889753950
10.7375768084468
-6.62119014859055
13.6618109739571
-5.87101523414179
2.14938460627729
-1.65327811554391
-0.488459649838376
-3.18696521674349
3.38241859572378
-2.81052544916736
-0.812910071103062
-0.600537523143146
1.35092036766434
1.10239545083985
1.13316315085206
-0.675343428807992
-3.99146903039653
1.39269826726442
-0.90092720976628
-0.626701935247837
1.82936219704021
-2.80759897669694
2.15856170667811
1.57510009493382
-2.92835941879108
5.35302577308198
3.17663069263514
-4.17017197516196
1.99673634478671
0.498077315788521
3.2825787596509
0.513265327558997
-1.36585663352099
1.42035493594449
0.308147398087385
-1.40531621594590
1.49082091133204
-0.58837224346479
2.21474580717279
-0.752160866253178
-0.637666510531062
-0.698260228519075
1.00930004135398
-0.0991070280523445
-5.14956559619407
1.16743922929851
1.23142783859339
-1.27752055255331
-7.39744426593012
24.0037804465778
73.3978643036192
-17.0433483598236
-10.6407324782818
-5.24835613292333
1.00883588565796
-7.7930224578501
-8.65869573424078
-14.8149852556135
7.81840437562465
2.65222440129133
-0.647340822637602
-3.69233717574662
-5.88508052671949
-2.51422788586342
-6.90087179738498
-5.51390227135977
2.75612305239525
-2.64823208977958
-7.93323676440247
8.03325803078184
4.00373005119076
-14.0223031447819
2.41932644884906
10.3738756815349
-3.63274781939742
-6.22829796179849
10.2774044702253
-5.76086113841342
0.529405854740503
5.64083374504918
-6.40265452447846
2.97145576456347
0.460132043966041
3.6499869957272
1.13728684737345
2.95590401278167
6.67892004013754
-5.01859228943829
8.64429877520325
0.465446667362301
-0.560051354013069
4.8738848077146
-7.70236535273841
-4.48757050657264
-3.36655882488708
-10.5615123520445
9.40929040052248
-4.51199964639466
7.13394406654271
-11.0588878377847
4.54713641036335
3.4727849377918
3.10971865952314
5.03617525011884
-5.5224494609264
1.11979949631387
0.103175300488147
-8.3123529619366
3.41207082967645
-0.0913309850903374
3.10947484518960
-9.43880474905006
7.33708759080986
-7.29690796370653
2.41419798633649
-7.21410758532448
-3.84827202332542
-0.924185322709377
3.49974864183110
-4.53162555512853
-2.55673855178179
5.72239639604123
0.201539059408447
-6.29990285494819
0.658831637876347
5.21044964047462
-5.47131995573022
4.669422609305
-5.90409140906638
-4.81211872408119
-2.02477104940002
-7.71856688045722
1.71213817310903
-0.494466601578836
11.0101336050544
-4.19573686002042
2.38720522299124
1.77400696597917
1.18084487092162
0.115962558837481
-5.05502919303578
1.3549066876015
-7.80519635946473
5.86148679398352
2.80288446242383
3.92184270041065
-2.97155245575783
-0.591913069507683
0.0493835548839172
2.26700538036749
5.1129096245939
71.6779060930572
78.4842263418149
-49.6216172308631
4.53492158396637
-2.83881740520457
-6.61345814434947
2.9620228701109
-15.6324006306163
-5.48245600735038
2.50348712788991
-0.547677980896651
0.089588994507892
-2.98666970367879
4.03012614950143
-7.03593057019492
-9.83165168817243
0.082603741685773
-4.10915839553894
-14.4381632324365
9.13395706651778
-5.83706702731951
1.13687335117464
-8.80988353119693
4.49732810453395
-7.16807660046874
5.21324959008388
-7.86574850569718
5.3896132292204
-8.44005784869063
6.98699339274236
-3.78989137616418
3.83316463307244
17.2353073687647
30.1941905689489
4.0429685831125
24.3341333654459
3.79350558313359
-2.07653575627290
-8.55656690022442
-19.0161709248054
0.51076949159301
-11.0420299816847
-6.26308903035226
-0.304817714625699
8.71519507527108
-10.6432589448967
-3.51198394753857
-7.0223128642038
3.59201097571020
-7.24377623420821
-4.12662549500368
-9.13153341869616
3.95448295599766
-3.92274329264029
1.73683486695643
-1.34426605242237
6.01810308301884
-4.03107698390414
4.45433041803705
-9.84720403070321
4.59456284541989
-0.870304656100984
-2.38196981887972
9.50934761306826
-18.3335741237702
8.63832372936247
16.5733848323495
6.6285220869159
-10.7074793143504
-2.06927970568887
-4.30185257639516
-6.80308899070047
5.38020455302103
-1.10923286304018
-9.84876422483933
2.35939559544693
-7.05095932093792
1.04138421349944
1.52763905341652
-0.513961678924147
-1.24827048781736
-6.92985985249669
0.344841816875828
6.8191645989325
-8.42108577452404
-1.81630737900798
-0.756283541847381
0.484156868238983
-2.67346792333416
-5.9026415734678
5.41887486133697
-0.43995039193112
-8.49340295481375
15.3965838085419
-2.67682314713255
-7.12072796741182
6.03870055644524
-6.46442221965043
1.93511147939154
-5.44253386004891
4.10865883397503
-5.94929346164577
19.2667520816755
-16.5301888092306
7.28203173165411
3.73916675853980
-7.85468628859752
1.37014389955289
1.77304625226162
-2.94870949202556
1.07385936190718
-9.70530090925843
0.264771017551993
10.5735938764548
-2.19779779987260
-3.85651070556673
8.8300821162079
-6.08351849470188
-5.64313118255961
-5.53805464550231
16.8332320492933
-6.90371041075633
1.71293184398468
-5.16217198919259
-3.10090218561851
1.30797461935060
27.9742776052329
-24.3288098893009
15.2118504993314
15.5901488769643
2.02380488267653
3.384926182708
-11.2461179802918
18.5414705615909
-11.5305501167771
4.13259590400349
-18.0283962985412
3.71625167708868
-0.739661045093044
-3.37774192252505
13.8478240145191
-6.33330862787614
7.08016552623405
-22.986270650001
10.2772361566453
1.11229753328581
-1.34567404518049
-4.24061525181133
4.75973021928405

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.254999839094220 \tabularnewline
22.4319591650934 \tabularnewline
8.48842388782407 \tabularnewline
31.3463855898503 \tabularnewline
16.4307531043167 \tabularnewline
5.83896338062774 \tabularnewline
-13.5110600811090 \tabularnewline
-4.38191112466065 \tabularnewline
-4.64342627735144 \tabularnewline
-2.39790452591552 \tabularnewline
-9.62528652477005 \tabularnewline
0.570943887624659 \tabularnewline
-4.32575439695619 \tabularnewline
-0.182017814293898 \tabularnewline
-1.87574824595658 \tabularnewline
-10.2057603789681 \tabularnewline
-0.230847339674417 \tabularnewline
-6.72183403274037 \tabularnewline
-0.96877058596556 \tabularnewline
-13.2888545772784 \tabularnewline
-0.821710152105348 \tabularnewline
-2.09225227224809 \tabularnewline
2.05576265908427 \tabularnewline
-1.95834748421589 \tabularnewline
-2.87374391592999 \tabularnewline
-2.70981140864819 \tabularnewline
-4.91585677432382 \tabularnewline
-0.539200028910704 \tabularnewline
-0.528445392880883 \tabularnewline
5.66770512150502 \tabularnewline
19.5912064655249 \tabularnewline
16.6782556422981 \tabularnewline
-0.324432057346130 \tabularnewline
0.62863975961177 \tabularnewline
1.67963822390779 \tabularnewline
0.371340804566055 \tabularnewline
-12.8775776267713 \tabularnewline
11.4296276252202 \tabularnewline
-11.0611990322411 \tabularnewline
0.00346328867632788 \tabularnewline
2.18399002373965 \tabularnewline
-1.81094647485137 \tabularnewline
7.2296368391813 \tabularnewline
-6.10745133373262 \tabularnewline
-8.97418755942159 \tabularnewline
-1.52360381634567 \tabularnewline
-11.3245326707611 \tabularnewline
-3.22215742294171 \tabularnewline
0.171025586455974 \tabularnewline
-13.7240838626719 \tabularnewline
1.68089407031351 \tabularnewline
-0.94630970938392 \tabularnewline
-0.673915406733183 \tabularnewline
1.61835126513499 \tabularnewline
-3.57605895217995 \tabularnewline
-0.0575932817974939 \tabularnewline
-0.440928918848127 \tabularnewline
0.992017595010534 \tabularnewline
-3.56777889753950 \tabularnewline
10.7375768084468 \tabularnewline
-6.62119014859055 \tabularnewline
13.6618109739571 \tabularnewline
-5.87101523414179 \tabularnewline
2.14938460627729 \tabularnewline
-1.65327811554391 \tabularnewline
-0.488459649838376 \tabularnewline
-3.18696521674349 \tabularnewline
3.38241859572378 \tabularnewline
-2.81052544916736 \tabularnewline
-0.812910071103062 \tabularnewline
-0.600537523143146 \tabularnewline
1.35092036766434 \tabularnewline
1.10239545083985 \tabularnewline
1.13316315085206 \tabularnewline
-0.675343428807992 \tabularnewline
-3.99146903039653 \tabularnewline
1.39269826726442 \tabularnewline
-0.90092720976628 \tabularnewline
-0.626701935247837 \tabularnewline
1.82936219704021 \tabularnewline
-2.80759897669694 \tabularnewline
2.15856170667811 \tabularnewline
1.57510009493382 \tabularnewline
-2.92835941879108 \tabularnewline
5.35302577308198 \tabularnewline
3.17663069263514 \tabularnewline
-4.17017197516196 \tabularnewline
1.99673634478671 \tabularnewline
0.498077315788521 \tabularnewline
3.2825787596509 \tabularnewline
0.513265327558997 \tabularnewline
-1.36585663352099 \tabularnewline
1.42035493594449 \tabularnewline
0.308147398087385 \tabularnewline
-1.40531621594590 \tabularnewline
1.49082091133204 \tabularnewline
-0.58837224346479 \tabularnewline
2.21474580717279 \tabularnewline
-0.752160866253178 \tabularnewline
-0.637666510531062 \tabularnewline
-0.698260228519075 \tabularnewline
1.00930004135398 \tabularnewline
-0.0991070280523445 \tabularnewline
-5.14956559619407 \tabularnewline
1.16743922929851 \tabularnewline
1.23142783859339 \tabularnewline
-1.27752055255331 \tabularnewline
-7.39744426593012 \tabularnewline
24.0037804465778 \tabularnewline
73.3978643036192 \tabularnewline
-17.0433483598236 \tabularnewline
-10.6407324782818 \tabularnewline
-5.24835613292333 \tabularnewline
1.00883588565796 \tabularnewline
-7.7930224578501 \tabularnewline
-8.65869573424078 \tabularnewline
-14.8149852556135 \tabularnewline
7.81840437562465 \tabularnewline
2.65222440129133 \tabularnewline
-0.647340822637602 \tabularnewline
-3.69233717574662 \tabularnewline
-5.88508052671949 \tabularnewline
-2.51422788586342 \tabularnewline
-6.90087179738498 \tabularnewline
-5.51390227135977 \tabularnewline
2.75612305239525 \tabularnewline
-2.64823208977958 \tabularnewline
-7.93323676440247 \tabularnewline
8.03325803078184 \tabularnewline
4.00373005119076 \tabularnewline
-14.0223031447819 \tabularnewline
2.41932644884906 \tabularnewline
10.3738756815349 \tabularnewline
-3.63274781939742 \tabularnewline
-6.22829796179849 \tabularnewline
10.2774044702253 \tabularnewline
-5.76086113841342 \tabularnewline
0.529405854740503 \tabularnewline
5.64083374504918 \tabularnewline
-6.40265452447846 \tabularnewline
2.97145576456347 \tabularnewline
0.460132043966041 \tabularnewline
3.6499869957272 \tabularnewline
1.13728684737345 \tabularnewline
2.95590401278167 \tabularnewline
6.67892004013754 \tabularnewline
-5.01859228943829 \tabularnewline
8.64429877520325 \tabularnewline
0.465446667362301 \tabularnewline
-0.560051354013069 \tabularnewline
4.8738848077146 \tabularnewline
-7.70236535273841 \tabularnewline
-4.48757050657264 \tabularnewline
-3.36655882488708 \tabularnewline
-10.5615123520445 \tabularnewline
9.40929040052248 \tabularnewline
-4.51199964639466 \tabularnewline
7.13394406654271 \tabularnewline
-11.0588878377847 \tabularnewline
4.54713641036335 \tabularnewline
3.4727849377918 \tabularnewline
3.10971865952314 \tabularnewline
5.03617525011884 \tabularnewline
-5.5224494609264 \tabularnewline
1.11979949631387 \tabularnewline
0.103175300488147 \tabularnewline
-8.3123529619366 \tabularnewline
3.41207082967645 \tabularnewline
-0.0913309850903374 \tabularnewline
3.10947484518960 \tabularnewline
-9.43880474905006 \tabularnewline
7.33708759080986 \tabularnewline
-7.29690796370653 \tabularnewline
2.41419798633649 \tabularnewline
-7.21410758532448 \tabularnewline
-3.84827202332542 \tabularnewline
-0.924185322709377 \tabularnewline
3.49974864183110 \tabularnewline
-4.53162555512853 \tabularnewline
-2.55673855178179 \tabularnewline
5.72239639604123 \tabularnewline
0.201539059408447 \tabularnewline
-6.29990285494819 \tabularnewline
0.658831637876347 \tabularnewline
5.21044964047462 \tabularnewline
-5.47131995573022 \tabularnewline
4.669422609305 \tabularnewline
-5.90409140906638 \tabularnewline
-4.81211872408119 \tabularnewline
-2.02477104940002 \tabularnewline
-7.71856688045722 \tabularnewline
1.71213817310903 \tabularnewline
-0.494466601578836 \tabularnewline
11.0101336050544 \tabularnewline
-4.19573686002042 \tabularnewline
2.38720522299124 \tabularnewline
1.77400696597917 \tabularnewline
1.18084487092162 \tabularnewline
0.115962558837481 \tabularnewline
-5.05502919303578 \tabularnewline
1.3549066876015 \tabularnewline
-7.80519635946473 \tabularnewline
5.86148679398352 \tabularnewline
2.80288446242383 \tabularnewline
3.92184270041065 \tabularnewline
-2.97155245575783 \tabularnewline
-0.591913069507683 \tabularnewline
0.0493835548839172 \tabularnewline
2.26700538036749 \tabularnewline
5.1129096245939 \tabularnewline
71.6779060930572 \tabularnewline
78.4842263418149 \tabularnewline
-49.6216172308631 \tabularnewline
4.53492158396637 \tabularnewline
-2.83881740520457 \tabularnewline
-6.61345814434947 \tabularnewline
2.9620228701109 \tabularnewline
-15.6324006306163 \tabularnewline
-5.48245600735038 \tabularnewline
2.50348712788991 \tabularnewline
-0.547677980896651 \tabularnewline
0.089588994507892 \tabularnewline
-2.98666970367879 \tabularnewline
4.03012614950143 \tabularnewline
-7.03593057019492 \tabularnewline
-9.83165168817243 \tabularnewline
0.082603741685773 \tabularnewline
-4.10915839553894 \tabularnewline
-14.4381632324365 \tabularnewline
9.13395706651778 \tabularnewline
-5.83706702731951 \tabularnewline
1.13687335117464 \tabularnewline
-8.80988353119693 \tabularnewline
4.49732810453395 \tabularnewline
-7.16807660046874 \tabularnewline
5.21324959008388 \tabularnewline
-7.86574850569718 \tabularnewline
5.3896132292204 \tabularnewline
-8.44005784869063 \tabularnewline
6.98699339274236 \tabularnewline
-3.78989137616418 \tabularnewline
3.83316463307244 \tabularnewline
17.2353073687647 \tabularnewline
30.1941905689489 \tabularnewline
4.0429685831125 \tabularnewline
24.3341333654459 \tabularnewline
3.79350558313359 \tabularnewline
-2.07653575627290 \tabularnewline
-8.55656690022442 \tabularnewline
-19.0161709248054 \tabularnewline
0.51076949159301 \tabularnewline
-11.0420299816847 \tabularnewline
-6.26308903035226 \tabularnewline
-0.304817714625699 \tabularnewline
8.71519507527108 \tabularnewline
-10.6432589448967 \tabularnewline
-3.51198394753857 \tabularnewline
-7.0223128642038 \tabularnewline
3.59201097571020 \tabularnewline
-7.24377623420821 \tabularnewline
-4.12662549500368 \tabularnewline
-9.13153341869616 \tabularnewline
3.95448295599766 \tabularnewline
-3.92274329264029 \tabularnewline
1.73683486695643 \tabularnewline
-1.34426605242237 \tabularnewline
6.01810308301884 \tabularnewline
-4.03107698390414 \tabularnewline
4.45433041803705 \tabularnewline
-9.84720403070321 \tabularnewline
4.59456284541989 \tabularnewline
-0.870304656100984 \tabularnewline
-2.38196981887972 \tabularnewline
9.50934761306826 \tabularnewline
-18.3335741237702 \tabularnewline
8.63832372936247 \tabularnewline
16.5733848323495 \tabularnewline
6.6285220869159 \tabularnewline
-10.7074793143504 \tabularnewline
-2.06927970568887 \tabularnewline
-4.30185257639516 \tabularnewline
-6.80308899070047 \tabularnewline
5.38020455302103 \tabularnewline
-1.10923286304018 \tabularnewline
-9.84876422483933 \tabularnewline
2.35939559544693 \tabularnewline
-7.05095932093792 \tabularnewline
1.04138421349944 \tabularnewline
1.52763905341652 \tabularnewline
-0.513961678924147 \tabularnewline
-1.24827048781736 \tabularnewline
-6.92985985249669 \tabularnewline
0.344841816875828 \tabularnewline
6.8191645989325 \tabularnewline
-8.42108577452404 \tabularnewline
-1.81630737900798 \tabularnewline
-0.756283541847381 \tabularnewline
0.484156868238983 \tabularnewline
-2.67346792333416 \tabularnewline
-5.9026415734678 \tabularnewline
5.41887486133697 \tabularnewline
-0.43995039193112 \tabularnewline
-8.49340295481375 \tabularnewline
15.3965838085419 \tabularnewline
-2.67682314713255 \tabularnewline
-7.12072796741182 \tabularnewline
6.03870055644524 \tabularnewline
-6.46442221965043 \tabularnewline
1.93511147939154 \tabularnewline
-5.44253386004891 \tabularnewline
4.10865883397503 \tabularnewline
-5.94929346164577 \tabularnewline
19.2667520816755 \tabularnewline
-16.5301888092306 \tabularnewline
7.28203173165411 \tabularnewline
3.73916675853980 \tabularnewline
-7.85468628859752 \tabularnewline
1.37014389955289 \tabularnewline
1.77304625226162 \tabularnewline
-2.94870949202556 \tabularnewline
1.07385936190718 \tabularnewline
-9.70530090925843 \tabularnewline
0.264771017551993 \tabularnewline
10.5735938764548 \tabularnewline
-2.19779779987260 \tabularnewline
-3.85651070556673 \tabularnewline
8.8300821162079 \tabularnewline
-6.08351849470188 \tabularnewline
-5.64313118255961 \tabularnewline
-5.53805464550231 \tabularnewline
16.8332320492933 \tabularnewline
-6.90371041075633 \tabularnewline
1.71293184398468 \tabularnewline
-5.16217198919259 \tabularnewline
-3.10090218561851 \tabularnewline
1.30797461935060 \tabularnewline
27.9742776052329 \tabularnewline
-24.3288098893009 \tabularnewline
15.2118504993314 \tabularnewline
15.5901488769643 \tabularnewline
2.02380488267653 \tabularnewline
3.384926182708 \tabularnewline
-11.2461179802918 \tabularnewline
18.5414705615909 \tabularnewline
-11.5305501167771 \tabularnewline
4.13259590400349 \tabularnewline
-18.0283962985412 \tabularnewline
3.71625167708868 \tabularnewline
-0.739661045093044 \tabularnewline
-3.37774192252505 \tabularnewline
13.8478240145191 \tabularnewline
-6.33330862787614 \tabularnewline
7.08016552623405 \tabularnewline
-22.986270650001 \tabularnewline
10.2772361566453 \tabularnewline
1.11229753328581 \tabularnewline
-1.34567404518049 \tabularnewline
-4.24061525181133 \tabularnewline
4.75973021928405 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65325&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.254999839094220[/C][/ROW]
[ROW][C]22.4319591650934[/C][/ROW]
[ROW][C]8.48842388782407[/C][/ROW]
[ROW][C]31.3463855898503[/C][/ROW]
[ROW][C]16.4307531043167[/C][/ROW]
[ROW][C]5.83896338062774[/C][/ROW]
[ROW][C]-13.5110600811090[/C][/ROW]
[ROW][C]-4.38191112466065[/C][/ROW]
[ROW][C]-4.64342627735144[/C][/ROW]
[ROW][C]-2.39790452591552[/C][/ROW]
[ROW][C]-9.62528652477005[/C][/ROW]
[ROW][C]0.570943887624659[/C][/ROW]
[ROW][C]-4.32575439695619[/C][/ROW]
[ROW][C]-0.182017814293898[/C][/ROW]
[ROW][C]-1.87574824595658[/C][/ROW]
[ROW][C]-10.2057603789681[/C][/ROW]
[ROW][C]-0.230847339674417[/C][/ROW]
[ROW][C]-6.72183403274037[/C][/ROW]
[ROW][C]-0.96877058596556[/C][/ROW]
[ROW][C]-13.2888545772784[/C][/ROW]
[ROW][C]-0.821710152105348[/C][/ROW]
[ROW][C]-2.09225227224809[/C][/ROW]
[ROW][C]2.05576265908427[/C][/ROW]
[ROW][C]-1.95834748421589[/C][/ROW]
[ROW][C]-2.87374391592999[/C][/ROW]
[ROW][C]-2.70981140864819[/C][/ROW]
[ROW][C]-4.91585677432382[/C][/ROW]
[ROW][C]-0.539200028910704[/C][/ROW]
[ROW][C]-0.528445392880883[/C][/ROW]
[ROW][C]5.66770512150502[/C][/ROW]
[ROW][C]19.5912064655249[/C][/ROW]
[ROW][C]16.6782556422981[/C][/ROW]
[ROW][C]-0.324432057346130[/C][/ROW]
[ROW][C]0.62863975961177[/C][/ROW]
[ROW][C]1.67963822390779[/C][/ROW]
[ROW][C]0.371340804566055[/C][/ROW]
[ROW][C]-12.8775776267713[/C][/ROW]
[ROW][C]11.4296276252202[/C][/ROW]
[ROW][C]-11.0611990322411[/C][/ROW]
[ROW][C]0.00346328867632788[/C][/ROW]
[ROW][C]2.18399002373965[/C][/ROW]
[ROW][C]-1.81094647485137[/C][/ROW]
[ROW][C]7.2296368391813[/C][/ROW]
[ROW][C]-6.10745133373262[/C][/ROW]
[ROW][C]-8.97418755942159[/C][/ROW]
[ROW][C]-1.52360381634567[/C][/ROW]
[ROW][C]-11.3245326707611[/C][/ROW]
[ROW][C]-3.22215742294171[/C][/ROW]
[ROW][C]0.171025586455974[/C][/ROW]
[ROW][C]-13.7240838626719[/C][/ROW]
[ROW][C]1.68089407031351[/C][/ROW]
[ROW][C]-0.94630970938392[/C][/ROW]
[ROW][C]-0.673915406733183[/C][/ROW]
[ROW][C]1.61835126513499[/C][/ROW]
[ROW][C]-3.57605895217995[/C][/ROW]
[ROW][C]-0.0575932817974939[/C][/ROW]
[ROW][C]-0.440928918848127[/C][/ROW]
[ROW][C]0.992017595010534[/C][/ROW]
[ROW][C]-3.56777889753950[/C][/ROW]
[ROW][C]10.7375768084468[/C][/ROW]
[ROW][C]-6.62119014859055[/C][/ROW]
[ROW][C]13.6618109739571[/C][/ROW]
[ROW][C]-5.87101523414179[/C][/ROW]
[ROW][C]2.14938460627729[/C][/ROW]
[ROW][C]-1.65327811554391[/C][/ROW]
[ROW][C]-0.488459649838376[/C][/ROW]
[ROW][C]-3.18696521674349[/C][/ROW]
[ROW][C]3.38241859572378[/C][/ROW]
[ROW][C]-2.81052544916736[/C][/ROW]
[ROW][C]-0.812910071103062[/C][/ROW]
[ROW][C]-0.600537523143146[/C][/ROW]
[ROW][C]1.35092036766434[/C][/ROW]
[ROW][C]1.10239545083985[/C][/ROW]
[ROW][C]1.13316315085206[/C][/ROW]
[ROW][C]-0.675343428807992[/C][/ROW]
[ROW][C]-3.99146903039653[/C][/ROW]
[ROW][C]1.39269826726442[/C][/ROW]
[ROW][C]-0.90092720976628[/C][/ROW]
[ROW][C]-0.626701935247837[/C][/ROW]
[ROW][C]1.82936219704021[/C][/ROW]
[ROW][C]-2.80759897669694[/C][/ROW]
[ROW][C]2.15856170667811[/C][/ROW]
[ROW][C]1.57510009493382[/C][/ROW]
[ROW][C]-2.92835941879108[/C][/ROW]
[ROW][C]5.35302577308198[/C][/ROW]
[ROW][C]3.17663069263514[/C][/ROW]
[ROW][C]-4.17017197516196[/C][/ROW]
[ROW][C]1.99673634478671[/C][/ROW]
[ROW][C]0.498077315788521[/C][/ROW]
[ROW][C]3.2825787596509[/C][/ROW]
[ROW][C]0.513265327558997[/C][/ROW]
[ROW][C]-1.36585663352099[/C][/ROW]
[ROW][C]1.42035493594449[/C][/ROW]
[ROW][C]0.308147398087385[/C][/ROW]
[ROW][C]-1.40531621594590[/C][/ROW]
[ROW][C]1.49082091133204[/C][/ROW]
[ROW][C]-0.58837224346479[/C][/ROW]
[ROW][C]2.21474580717279[/C][/ROW]
[ROW][C]-0.752160866253178[/C][/ROW]
[ROW][C]-0.637666510531062[/C][/ROW]
[ROW][C]-0.698260228519075[/C][/ROW]
[ROW][C]1.00930004135398[/C][/ROW]
[ROW][C]-0.0991070280523445[/C][/ROW]
[ROW][C]-5.14956559619407[/C][/ROW]
[ROW][C]1.16743922929851[/C][/ROW]
[ROW][C]1.23142783859339[/C][/ROW]
[ROW][C]-1.27752055255331[/C][/ROW]
[ROW][C]-7.39744426593012[/C][/ROW]
[ROW][C]24.0037804465778[/C][/ROW]
[ROW][C]73.3978643036192[/C][/ROW]
[ROW][C]-17.0433483598236[/C][/ROW]
[ROW][C]-10.6407324782818[/C][/ROW]
[ROW][C]-5.24835613292333[/C][/ROW]
[ROW][C]1.00883588565796[/C][/ROW]
[ROW][C]-7.7930224578501[/C][/ROW]
[ROW][C]-8.65869573424078[/C][/ROW]
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[ROW][C]-4.24061525181133[/C][/ROW]
[ROW][C]4.75973021928405[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65325&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65325&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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22.4319591650934
8.48842388782407
31.3463855898503
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5.66770512150502
19.5912064655249
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1.67963822390779
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2.18399002373965
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Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 1 ; par7 = 0 ; par8 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 0 ;
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')