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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 computationWed, 09 Dec 2009 09:53:26 -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/09/t1260377704tiexk9met8jkink.htm/, Retrieved Thu, 31 Oct 2024 23:27:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65047, Retrieved Thu, 31 Oct 2024 23:27:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsworkshop 10
Estimated Impact192
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]
-    D    [ARIMA Backward Selection] [workshop 10] [2009-12-09 16:53:26] [6198946fb53eb5eb18db46bb758f7fde] [Current]
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Dataseries X:
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
311.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65047&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]8 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=65047&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65047&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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1ma2ma3
Estimates ( 1 )1.00480.2453-0.3054-0.56-0.62120.19
(p-val)(0.036 )(0.6829 )(0.0795 )(0.2406 )(0.0962 )(0.2198 )
Estimates ( 2 )1.18980-0.2371-0.7431-0.46570.2168
(p-val)(0 )(NA )(0.0014 )(0 )(0 )(0.0635 )
Estimates ( 3 )-0.548300.16551.02060.36860
(p-val)(0.4076 )(NA )(0.0863 )(0.1249 )(0.257 )(NA )
Estimates ( 4 )000.10260.4740.10790
(p-val)(NA )(NA )(0.0862 )(0 )(0.0724 )(NA )
Estimates ( 5 )0000.4710.06510
(p-val)(NA )(NA )(NA )(0 )(0.2063 )(NA )
Estimates ( 6 )0000.452600
(p-val)(NA )(NA )(NA )(0 )(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 & ma2 & ma3 \tabularnewline
Estimates ( 1 ) & 1.0048 & 0.2453 & -0.3054 & -0.56 & -0.6212 & 0.19 \tabularnewline
(p-val) & (0.036 ) & (0.6829 ) & (0.0795 ) & (0.2406 ) & (0.0962 ) & (0.2198 ) \tabularnewline
Estimates ( 2 ) & 1.1898 & 0 & -0.2371 & -0.7431 & -0.4657 & 0.2168 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.0014 ) & (0 ) & (0 ) & (0.0635 ) \tabularnewline
Estimates ( 3 ) & -0.5483 & 0 & 0.1655 & 1.0206 & 0.3686 & 0 \tabularnewline
(p-val) & (0.4076 ) & (NA ) & (0.0863 ) & (0.1249 ) & (0.257 ) & (NA ) \tabularnewline
Estimates ( 4 ) & 0 & 0 & 0.1026 & 0.474 & 0.1079 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (0.0862 ) & (0 ) & (0.0724 ) & (NA ) \tabularnewline
Estimates ( 5 ) & 0 & 0 & 0 & 0.471 & 0.0651 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (0.2063 ) & (NA ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0.4526 & 0 & 0 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (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=65047&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]ma2[/C][C]ma3[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]1.0048[/C][C]0.2453[/C][C]-0.3054[/C][C]-0.56[/C][C]-0.6212[/C][C]0.19[/C][/ROW]
[ROW][C](p-val)[/C][C](0.036 )[/C][C](0.6829 )[/C][C](0.0795 )[/C][C](0.2406 )[/C][C](0.0962 )[/C][C](0.2198 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]1.1898[/C][C]0[/C][C]-0.2371[/C][C]-0.7431[/C][C]-0.4657[/C][C]0.2168[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](0.0014 )[/C][C](0 )[/C][C](0 )[/C][C](0.0635 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.5483[/C][C]0[/C][C]0.1655[/C][C]1.0206[/C][C]0.3686[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4076 )[/C][C](NA )[/C][C](0.0863 )[/C][C](0.1249 )[/C][C](0.257 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0[/C][C]0.1026[/C][C]0.474[/C][C]0.1079[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](0.0862 )[/C][C](0 )[/C][C](0.0724 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0.471[/C][C]0.0651[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](0.2063 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0.4526[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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=65047&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65047&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
Iterationar1ar2ar3ma1ma2ma3
Estimates ( 1 )1.00480.2453-0.3054-0.56-0.62120.19
(p-val)(0.036 )(0.6829 )(0.0795 )(0.2406 )(0.0962 )(0.2198 )
Estimates ( 2 )1.18980-0.2371-0.7431-0.46570.2168
(p-val)(0 )(NA )(0.0014 )(0 )(0 )(0.0635 )
Estimates ( 3 )-0.548300.16551.02060.36860
(p-val)(0.4076 )(NA )(0.0863 )(0.1249 )(0.257 )(NA )
Estimates ( 4 )000.10260.4740.10790
(p-val)(NA )(NA )(0.0862 )(0 )(0.0724 )(NA )
Estimates ( 5 )0000.4710.06510
(p-val)(NA )(NA )(NA )(0 )(0.2063 )(NA )
Estimates ( 6 )0000.452600
(p-val)(NA )(NA )(NA )(0 )(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
0.280199828223704
17.7910026027855
30.9190868084164
19.4012264067382
8.16895023633064
-9.4098807396458
-3.59956881740647
-4.19189859655881
-3.99117970798696
-9.44715864070972
-0.590349220059257
-4.70687436778417
-1.34453145057442
-1.66025630661340
-11.1304494614246
-0.0492401659399775
-7.85215677985866
-1.69826834878808
-13.5888647895721
-1.88880166545272
-2.62563021276162
0.459696140744995
-1.54558407957649
-3.60192700897250
-2.30279342086749
-5.68083320525335
-0.674287973984917
-1.21254486139105
5.31503299087262
19.5754541188943
17.0335259196109
1.50239838411818
3.08336869318725
2.74985656836378
0.504018193198874
-12.3164327922979
11.3684766353350
-11.5529203944577
-0.598485964454994
3.03405423727116
-3.69013682974361
8.4405969501301
-6.83544608926479
-8.32989231645416
-1.53142790574719
-12.5363469722947
-3.79542118621549
-0.696097943178586
-15.0250220412899
1.62241116609846
-2.08598171061158
-1.72308750223775
2.04741757695842
-4.25219345228228
0.269572246936889
-0.750134131890263
0.735777908835502
-3.49772875929847
10.5995985597235
-6.56490661038094
13.802115851849
-4.97366830182742
1.74410966368447
0.00230045737498585
-1.91463416455213
-1.99831806235403
2.36590116398702
-2.38428677239381
-1.33098480140649
-0.117849576057012
0.64216357889083
1.40520048764287
0.896313748132684
-0.313667894903972
-4.01061071197159
1.50950078652519
-1.24989403400497
-0.809550783909032
1.96268951346215
-3.07176079521457
2.3190806469205
1.50765334526687
-3.06111965077986
5.74369367015112
2.99389802815787
-3.98413128781709
2.68168870900746
0.296257618658046
3.18586475408739
0.880104866393594
-1.32196367812912
1.86537243217532
0.107438275404036
-1.27205105578440
1.59216718007411
-0.667126054353957
2.21057160979615
-0.597790904111832
-0.662348000801899
-0.449101299555309
0.754658160915028
-0.0262205444648203
-5.23678175701809
1.26833802037487
0.843528650193548
-1.57989458026452
-7.11075555486644
23.7521701217122
73.1751909700449
-15.6133958679770
-4.20985754636757
-0.900558099072327
-2.50173518058432
-6.0630007357467
-10.4813271366776
-14.2683484683185
5.70307176190573
1.84268060835711
-1.83923972539111
-2.35364869087800
-6.67163943855161
-2.30428710435496
-7.88027541918274
-5.93820862276112
2.01006415373325
-3.56017294020268
-8.15395161481655
7.97246313659952
3.17567201530022
-14.0148550139979
3.29452956661697
9.46064932430573
-4.37064962263122
-5.05727239192419
10.3666303597435
-6.35363944604535
0.817772152505427
6.22846759516142
-7.28697638239379
3.9268087694191
0.124813074857798
3.3855548419337
1.69720869628225
2.8801629040359
7.33288747249634
-4.94145300631874
9.45011598737449
0.670517056917276
-0.531078855750252
6.00649481908755
-8.19460547148691
-3.83122785207394
-3.66190220711826
-11.5257365935452
9.06726567828446
-5.5204815691954
6.80993320660366
-10.2482052813629
3.78375149415848
4.18498735597439
1.68244378621478
6.63507026670982
-5.93478914331087
2.16342907711135
-0.132633570382097
-8.57837736046798
3.64922568813137
-0.760362722710283
2.72056278240655
-8.9319369477916
7.0300015498679
-7.22975848322784
1.64767394731638
-6.4053931605535
-5.19020142339701
-0.338284979784646
1.89724820248398
-4.17161845687889
-2.95860341129389
5.96515653845222
-0.617089835859645
-5.79770079995023
0.871010981710867
4.76719652690775
-5.80215405478623
5.12256353421208
-5.93508143666088
-5.13796119884466
-1.69351092730679
-8.96781386848903
1.63427944656689
-1.38592764047510
10.5464000777625
-3.87733826791833
2.63967989085523
2.7090918475576
0.452105654062279
1.11067306670412
-5.35258407597254
1.748865590636
-8.2752702952333
5.58396071100029
2.60860380899555
3.40774958690875
-1.87495316453715
-0.738721056518244
0.570021865912139
1.67960266430720
5.47176153134171
71.6133379496375
79.8124183253436
-44.4556759435520
14.9433261484363
-0.94431562768625
-10.5280962795755
6.62042775166475
-18.8329226108399
-4.16033390789079
0.785723178977491
-1.7992322730791
-0.00367959516228211
-3.08112746513262
3.85151338640213
-7.11354388522392
-10.0001294004230
0.0733934097960969
-5.58350940274988
-14.8748315786651
8.66986428061182
-7.11525392547782
0.486976381953298
-8.06613579541408
3.16760964448656
-6.76686133912756
3.98110174286103
-7.13462221411442
4.20136204663243
-7.61442616181773
5.7130193934679
-2.89520984761771
2.59175558920128
18.5677235065522
29.2854870844915
6.49708349514071
26.9331038821228
6.8909653110781
-0.899270109540339
-5.22506310256
-19.98034164126
0.85132684727796
-13.3001682267795
-7.39077552586076
-0.852884047530551
6.88290365948109
-10.5864607052155
-3.56167406364870
-6.83314532981677
1.95043419090638
-7.17382098242211
-5.1479675248201
-8.80814936240455
2.3839772857732
-4.14944558968534
0.699264123827106
-0.759217182270447
5.11208103661698
-3.35846536233788
4.14908258697221
-9.13564890450914
3.93294997039993
-0.457721368268835
-3.54045938811987
10.5974285615695
-19.2611025110456
9.38242413195428
16.1346848940971
5.48939004390843
-8.03606640699763
-1.47223677431560
-4.08335679499186
-7.98080547142655
5.52496976219356
-2.28278144214948
-9.78446766212357
2.55730459837957
-7.96752289143825
0.486371831902034
1.48963609841934
-1.43331429294642
-0.621862809648917
-7.31377373527516
0.385422975332347
6.19462259984891
-8.84288652177679
-1.33812236234331
-0.893999234060743
-0.491789422914565
-2.41015316400734
-6.33275069683776
5.43976812698759
-1.14994666548921
-8.51250885850305
15.7844326502743
-3.38058088411537
-6.73532609975103
7.2925692330852
-7.7964439935671
2.39749985044273
-5.52168226998441
3.44473517550171
-5.66305025828825
18.5431419173517
-15.8655037298680
7.16572067912773
5.15772626840436
-9.99591983285762
3.77248716390079
0.173867991397685
-2.62750388499438
1.02628816537214
-9.7123384838203
-0.0921073412997657
10.2757081395605
-3.13407127338075
-2.79279055862361
9.11950559259793
-6.51364780042991
-5.2256663488709
-5.21453233756176
15.5963690308135
-7.00671449606028
1.88490064646970
-3.83165309283766
-4.51793013577031
2.17749769102153
26.5684949417670
-23.5560689654665
16.2656414442147
16.9721739032565
-0.153217561572376
7.66719187808457
-11.8014283630027
19.8595392241749
-12.0859133756637
4.39975229369128
-16.6855172274796
1.77277190698294
-0.0486978184672466
-5.89247899832884
15.4786481706685
-7.80712772140907
8.369575826762
-22.5339561921372
9.66904970236453
1.11275819115122
-3.45363716481995
-1.94571285513797
3.14132013199674
-5.85294978075291

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.280199828223704 \tabularnewline
17.7910026027855 \tabularnewline
30.9190868084164 \tabularnewline
19.4012264067382 \tabularnewline
8.16895023633064 \tabularnewline
-9.4098807396458 \tabularnewline
-3.59956881740647 \tabularnewline
-4.19189859655881 \tabularnewline
-3.99117970798696 \tabularnewline
-9.44715864070972 \tabularnewline
-0.590349220059257 \tabularnewline
-4.70687436778417 \tabularnewline
-1.34453145057442 \tabularnewline
-1.66025630661340 \tabularnewline
-11.1304494614246 \tabularnewline
-0.0492401659399775 \tabularnewline
-7.85215677985866 \tabularnewline
-1.69826834878808 \tabularnewline
-13.5888647895721 \tabularnewline
-1.88880166545272 \tabularnewline
-2.62563021276162 \tabularnewline
0.459696140744995 \tabularnewline
-1.54558407957649 \tabularnewline
-3.60192700897250 \tabularnewline
-2.30279342086749 \tabularnewline
-5.68083320525335 \tabularnewline
-0.674287973984917 \tabularnewline
-1.21254486139105 \tabularnewline
5.31503299087262 \tabularnewline
19.5754541188943 \tabularnewline
17.0335259196109 \tabularnewline
1.50239838411818 \tabularnewline
3.08336869318725 \tabularnewline
2.74985656836378 \tabularnewline
0.504018193198874 \tabularnewline
-12.3164327922979 \tabularnewline
11.3684766353350 \tabularnewline
-11.5529203944577 \tabularnewline
-0.598485964454994 \tabularnewline
3.03405423727116 \tabularnewline
-3.69013682974361 \tabularnewline
8.4405969501301 \tabularnewline
-6.83544608926479 \tabularnewline
-8.32989231645416 \tabularnewline
-1.53142790574719 \tabularnewline
-12.5363469722947 \tabularnewline
-3.79542118621549 \tabularnewline
-0.696097943178586 \tabularnewline
-15.0250220412899 \tabularnewline
1.62241116609846 \tabularnewline
-2.08598171061158 \tabularnewline
-1.72308750223775 \tabularnewline
2.04741757695842 \tabularnewline
-4.25219345228228 \tabularnewline
0.269572246936889 \tabularnewline
-0.750134131890263 \tabularnewline
0.735777908835502 \tabularnewline
-3.49772875929847 \tabularnewline
10.5995985597235 \tabularnewline
-6.56490661038094 \tabularnewline
13.802115851849 \tabularnewline
-4.97366830182742 \tabularnewline
1.74410966368447 \tabularnewline
0.00230045737498585 \tabularnewline
-1.91463416455213 \tabularnewline
-1.99831806235403 \tabularnewline
2.36590116398702 \tabularnewline
-2.38428677239381 \tabularnewline
-1.33098480140649 \tabularnewline
-0.117849576057012 \tabularnewline
0.64216357889083 \tabularnewline
1.40520048764287 \tabularnewline
0.896313748132684 \tabularnewline
-0.313667894903972 \tabularnewline
-4.01061071197159 \tabularnewline
1.50950078652519 \tabularnewline
-1.24989403400497 \tabularnewline
-0.809550783909032 \tabularnewline
1.96268951346215 \tabularnewline
-3.07176079521457 \tabularnewline
2.3190806469205 \tabularnewline
1.50765334526687 \tabularnewline
-3.06111965077986 \tabularnewline
5.74369367015112 \tabularnewline
2.99389802815787 \tabularnewline
-3.98413128781709 \tabularnewline
2.68168870900746 \tabularnewline
0.296257618658046 \tabularnewline
3.18586475408739 \tabularnewline
0.880104866393594 \tabularnewline
-1.32196367812912 \tabularnewline
1.86537243217532 \tabularnewline
0.107438275404036 \tabularnewline
-1.27205105578440 \tabularnewline
1.59216718007411 \tabularnewline
-0.667126054353957 \tabularnewline
2.21057160979615 \tabularnewline
-0.597790904111832 \tabularnewline
-0.662348000801899 \tabularnewline
-0.449101299555309 \tabularnewline
0.754658160915028 \tabularnewline
-0.0262205444648203 \tabularnewline
-5.23678175701809 \tabularnewline
1.26833802037487 \tabularnewline
0.843528650193548 \tabularnewline
-1.57989458026452 \tabularnewline
-7.11075555486644 \tabularnewline
23.7521701217122 \tabularnewline
73.1751909700449 \tabularnewline
-15.6133958679770 \tabularnewline
-4.20985754636757 \tabularnewline
-0.900558099072327 \tabularnewline
-2.50173518058432 \tabularnewline
-6.0630007357467 \tabularnewline
-10.4813271366776 \tabularnewline
-14.2683484683185 \tabularnewline
5.70307176190573 \tabularnewline
1.84268060835711 \tabularnewline
-1.83923972539111 \tabularnewline
-2.35364869087800 \tabularnewline
-6.67163943855161 \tabularnewline
-2.30428710435496 \tabularnewline
-7.88027541918274 \tabularnewline
-5.93820862276112 \tabularnewline
2.01006415373325 \tabularnewline
-3.56017294020268 \tabularnewline
-8.15395161481655 \tabularnewline
7.97246313659952 \tabularnewline
3.17567201530022 \tabularnewline
-14.0148550139979 \tabularnewline
3.29452956661697 \tabularnewline
9.46064932430573 \tabularnewline
-4.37064962263122 \tabularnewline
-5.05727239192419 \tabularnewline
10.3666303597435 \tabularnewline
-6.35363944604535 \tabularnewline
0.817772152505427 \tabularnewline
6.22846759516142 \tabularnewline
-7.28697638239379 \tabularnewline
3.9268087694191 \tabularnewline
0.124813074857798 \tabularnewline
3.3855548419337 \tabularnewline
1.69720869628225 \tabularnewline
2.8801629040359 \tabularnewline
7.33288747249634 \tabularnewline
-4.94145300631874 \tabularnewline
9.45011598737449 \tabularnewline
0.670517056917276 \tabularnewline
-0.531078855750252 \tabularnewline
6.00649481908755 \tabularnewline
-8.19460547148691 \tabularnewline
-3.83122785207394 \tabularnewline
-3.66190220711826 \tabularnewline
-11.5257365935452 \tabularnewline
9.06726567828446 \tabularnewline
-5.5204815691954 \tabularnewline
6.80993320660366 \tabularnewline
-10.2482052813629 \tabularnewline
3.78375149415848 \tabularnewline
4.18498735597439 \tabularnewline
1.68244378621478 \tabularnewline
6.63507026670982 \tabularnewline
-5.93478914331087 \tabularnewline
2.16342907711135 \tabularnewline
-0.132633570382097 \tabularnewline
-8.57837736046798 \tabularnewline
3.64922568813137 \tabularnewline
-0.760362722710283 \tabularnewline
2.72056278240655 \tabularnewline
-8.9319369477916 \tabularnewline
7.0300015498679 \tabularnewline
-7.22975848322784 \tabularnewline
1.64767394731638 \tabularnewline
-6.4053931605535 \tabularnewline
-5.19020142339701 \tabularnewline
-0.338284979784646 \tabularnewline
1.89724820248398 \tabularnewline
-4.17161845687889 \tabularnewline
-2.95860341129389 \tabularnewline
5.96515653845222 \tabularnewline
-0.617089835859645 \tabularnewline
-5.79770079995023 \tabularnewline
0.871010981710867 \tabularnewline
4.76719652690775 \tabularnewline
-5.80215405478623 \tabularnewline
5.12256353421208 \tabularnewline
-5.93508143666088 \tabularnewline
-5.13796119884466 \tabularnewline
-1.69351092730679 \tabularnewline
-8.96781386848903 \tabularnewline
1.63427944656689 \tabularnewline
-1.38592764047510 \tabularnewline
10.5464000777625 \tabularnewline
-3.87733826791833 \tabularnewline
2.63967989085523 \tabularnewline
2.7090918475576 \tabularnewline
0.452105654062279 \tabularnewline
1.11067306670412 \tabularnewline
-5.35258407597254 \tabularnewline
1.748865590636 \tabularnewline
-8.2752702952333 \tabularnewline
5.58396071100029 \tabularnewline
2.60860380899555 \tabularnewline
3.40774958690875 \tabularnewline
-1.87495316453715 \tabularnewline
-0.738721056518244 \tabularnewline
0.570021865912139 \tabularnewline
1.67960266430720 \tabularnewline
5.47176153134171 \tabularnewline
71.6133379496375 \tabularnewline
79.8124183253436 \tabularnewline
-44.4556759435520 \tabularnewline
14.9433261484363 \tabularnewline
-0.94431562768625 \tabularnewline
-10.5280962795755 \tabularnewline
6.62042775166475 \tabularnewline
-18.8329226108399 \tabularnewline
-4.16033390789079 \tabularnewline
0.785723178977491 \tabularnewline
-1.7992322730791 \tabularnewline
-0.00367959516228211 \tabularnewline
-3.08112746513262 \tabularnewline
3.85151338640213 \tabularnewline
-7.11354388522392 \tabularnewline
-10.0001294004230 \tabularnewline
0.0733934097960969 \tabularnewline
-5.58350940274988 \tabularnewline
-14.8748315786651 \tabularnewline
8.66986428061182 \tabularnewline
-7.11525392547782 \tabularnewline
0.486976381953298 \tabularnewline
-8.06613579541408 \tabularnewline
3.16760964448656 \tabularnewline
-6.76686133912756 \tabularnewline
3.98110174286103 \tabularnewline
-7.13462221411442 \tabularnewline
4.20136204663243 \tabularnewline
-7.61442616181773 \tabularnewline
5.7130193934679 \tabularnewline
-2.89520984761771 \tabularnewline
2.59175558920128 \tabularnewline
18.5677235065522 \tabularnewline
29.2854870844915 \tabularnewline
6.49708349514071 \tabularnewline
26.9331038821228 \tabularnewline
6.8909653110781 \tabularnewline
-0.899270109540339 \tabularnewline
-5.22506310256 \tabularnewline
-19.98034164126 \tabularnewline
0.85132684727796 \tabularnewline
-13.3001682267795 \tabularnewline
-7.39077552586076 \tabularnewline
-0.852884047530551 \tabularnewline
6.88290365948109 \tabularnewline
-10.5864607052155 \tabularnewline
-3.56167406364870 \tabularnewline
-6.83314532981677 \tabularnewline
1.95043419090638 \tabularnewline
-7.17382098242211 \tabularnewline
-5.1479675248201 \tabularnewline
-8.80814936240455 \tabularnewline
2.3839772857732 \tabularnewline
-4.14944558968534 \tabularnewline
0.699264123827106 \tabularnewline
-0.759217182270447 \tabularnewline
5.11208103661698 \tabularnewline
-3.35846536233788 \tabularnewline
4.14908258697221 \tabularnewline
-9.13564890450914 \tabularnewline
3.93294997039993 \tabularnewline
-0.457721368268835 \tabularnewline
-3.54045938811987 \tabularnewline
10.5974285615695 \tabularnewline
-19.2611025110456 \tabularnewline
9.38242413195428 \tabularnewline
16.1346848940971 \tabularnewline
5.48939004390843 \tabularnewline
-8.03606640699763 \tabularnewline
-1.47223677431560 \tabularnewline
-4.08335679499186 \tabularnewline
-7.98080547142655 \tabularnewline
5.52496976219356 \tabularnewline
-2.28278144214948 \tabularnewline
-9.78446766212357 \tabularnewline
2.55730459837957 \tabularnewline
-7.96752289143825 \tabularnewline
0.486371831902034 \tabularnewline
1.48963609841934 \tabularnewline
-1.43331429294642 \tabularnewline
-0.621862809648917 \tabularnewline
-7.31377373527516 \tabularnewline
0.385422975332347 \tabularnewline
6.19462259984891 \tabularnewline
-8.84288652177679 \tabularnewline
-1.33812236234331 \tabularnewline
-0.893999234060743 \tabularnewline
-0.491789422914565 \tabularnewline
-2.41015316400734 \tabularnewline
-6.33275069683776 \tabularnewline
5.43976812698759 \tabularnewline
-1.14994666548921 \tabularnewline
-8.51250885850305 \tabularnewline
15.7844326502743 \tabularnewline
-3.38058088411537 \tabularnewline
-6.73532609975103 \tabularnewline
7.2925692330852 \tabularnewline
-7.7964439935671 \tabularnewline
2.39749985044273 \tabularnewline
-5.52168226998441 \tabularnewline
3.44473517550171 \tabularnewline
-5.66305025828825 \tabularnewline
18.5431419173517 \tabularnewline
-15.8655037298680 \tabularnewline
7.16572067912773 \tabularnewline
5.15772626840436 \tabularnewline
-9.99591983285762 \tabularnewline
3.77248716390079 \tabularnewline
0.173867991397685 \tabularnewline
-2.62750388499438 \tabularnewline
1.02628816537214 \tabularnewline
-9.7123384838203 \tabularnewline
-0.0921073412997657 \tabularnewline
10.2757081395605 \tabularnewline
-3.13407127338075 \tabularnewline
-2.79279055862361 \tabularnewline
9.11950559259793 \tabularnewline
-6.51364780042991 \tabularnewline
-5.2256663488709 \tabularnewline
-5.21453233756176 \tabularnewline
15.5963690308135 \tabularnewline
-7.00671449606028 \tabularnewline
1.88490064646970 \tabularnewline
-3.83165309283766 \tabularnewline
-4.51793013577031 \tabularnewline
2.17749769102153 \tabularnewline
26.5684949417670 \tabularnewline
-23.5560689654665 \tabularnewline
16.2656414442147 \tabularnewline
16.9721739032565 \tabularnewline
-0.153217561572376 \tabularnewline
7.66719187808457 \tabularnewline
-11.8014283630027 \tabularnewline
19.8595392241749 \tabularnewline
-12.0859133756637 \tabularnewline
4.39975229369128 \tabularnewline
-16.6855172274796 \tabularnewline
1.77277190698294 \tabularnewline
-0.0486978184672466 \tabularnewline
-5.89247899832884 \tabularnewline
15.4786481706685 \tabularnewline
-7.80712772140907 \tabularnewline
8.369575826762 \tabularnewline
-22.5339561921372 \tabularnewline
9.66904970236453 \tabularnewline
1.11275819115122 \tabularnewline
-3.45363716481995 \tabularnewline
-1.94571285513797 \tabularnewline
3.14132013199674 \tabularnewline
-5.85294978075291 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65047&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.280199828223704[/C][/ROW]
[ROW][C]17.7910026027855[/C][/ROW]
[ROW][C]30.9190868084164[/C][/ROW]
[ROW][C]19.4012264067382[/C][/ROW]
[ROW][C]8.16895023633064[/C][/ROW]
[ROW][C]-9.4098807396458[/C][/ROW]
[ROW][C]-3.59956881740647[/C][/ROW]
[ROW][C]-4.19189859655881[/C][/ROW]
[ROW][C]-3.99117970798696[/C][/ROW]
[ROW][C]-9.44715864070972[/C][/ROW]
[ROW][C]-0.590349220059257[/C][/ROW]
[ROW][C]-4.70687436778417[/C][/ROW]
[ROW][C]-1.34453145057442[/C][/ROW]
[ROW][C]-1.66025630661340[/C][/ROW]
[ROW][C]-11.1304494614246[/C][/ROW]
[ROW][C]-0.0492401659399775[/C][/ROW]
[ROW][C]-7.85215677985866[/C][/ROW]
[ROW][C]-1.69826834878808[/C][/ROW]
[ROW][C]-13.5888647895721[/C][/ROW]
[ROW][C]-1.88880166545272[/C][/ROW]
[ROW][C]-2.62563021276162[/C][/ROW]
[ROW][C]0.459696140744995[/C][/ROW]
[ROW][C]-1.54558407957649[/C][/ROW]
[ROW][C]-3.60192700897250[/C][/ROW]
[ROW][C]-2.30279342086749[/C][/ROW]
[ROW][C]-5.68083320525335[/C][/ROW]
[ROW][C]-0.674287973984917[/C][/ROW]
[ROW][C]-1.21254486139105[/C][/ROW]
[ROW][C]5.31503299087262[/C][/ROW]
[ROW][C]19.5754541188943[/C][/ROW]
[ROW][C]17.0335259196109[/C][/ROW]
[ROW][C]1.50239838411818[/C][/ROW]
[ROW][C]3.08336869318725[/C][/ROW]
[ROW][C]2.74985656836378[/C][/ROW]
[ROW][C]0.504018193198874[/C][/ROW]
[ROW][C]-12.3164327922979[/C][/ROW]
[ROW][C]11.3684766353350[/C][/ROW]
[ROW][C]-11.5529203944577[/C][/ROW]
[ROW][C]-0.598485964454994[/C][/ROW]
[ROW][C]3.03405423727116[/C][/ROW]
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[ROW][C]3.14132013199674[/C][/ROW]
[ROW][C]-5.85294978075291[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65047&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65047&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
0.280199828223704
17.7910026027855
30.9190868084164
19.4012264067382
8.16895023633064
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0.459696140744995
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5.31503299087262
19.5754541188943
17.0335259196109
1.50239838411818
3.08336869318725
2.74985656836378
0.504018193198874
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11.3684766353350
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3.03405423727116
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8.4405969501301
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2.04741757695842
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0.269572246936889
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10.5995985597235
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13.802115851849
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1.74410966368447
0.00230045737498585
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2.68168870900746
0.296257618658046
3.18586475408739
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1.86537243217532
0.107438275404036
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1.59216718007411
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1.84268060835711
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2.01006415373325
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Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 0 ;
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
par6 <- 3
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par7 <- 3
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')