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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 04:02:01 -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/t126044296975wo0afddrcy9x5.htm/, Retrieved Thu, 28 Mar 2024 11:32:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65274, Retrieved Thu, 28 Mar 2024 11:32:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2009-12-07 09:18:36] [b98453cac15ba1066b407e146608df68]
-   P     [ARIMA Backward Selection] [Bivariate Granger...] [2009-12-10 11:02:01] [4996e0131d5120d29a6e9a8dccb25dc3] [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
311.3




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=65274&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=65274&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65274&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
Iterationar1ar2ar3ma1ma2ma3
Estimates ( 1 )1.08380.1741-0.3113-0.6379-0.57570.2242
(p-val)(0.0131 )(0.7639 )(0.0961 )(0.1403 )(0.1196 )(0.0774 )
Estimates ( 2 )1.20920-0.257-0.7609-0.46340.2341
(p-val)(0 )(NA )(2e-04 )(0 )(0 )(0.0356 )
Estimates ( 3 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & ma2 & ma3 \tabularnewline
Estimates ( 1 ) & 1.0838 & 0.1741 & -0.3113 & -0.6379 & -0.5757 & 0.2242 \tabularnewline
(p-val) & (0.0131 ) & (0.7639 ) & (0.0961 ) & (0.1403 ) & (0.1196 ) & (0.0774 ) \tabularnewline
Estimates ( 2 ) & 1.2092 & 0 & -0.257 & -0.7609 & -0.4634 & 0.2341 \tabularnewline
(p-val) & (0 ) & (NA ) & (2e-04 ) & (0 ) & (0 ) & (0.0356 ) \tabularnewline
Estimates ( 3 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65274&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]1.0838[/C][C]0.1741[/C][C]-0.3113[/C][C]-0.6379[/C][C]-0.5757[/C][C]0.2242[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0131 )[/C][C](0.7639 )[/C][C](0.0961 )[/C][C](0.1403 )[/C][C](0.1196 )[/C][C](0.0774 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]1.2092[/C][C]0[/C][C]-0.257[/C][C]-0.7609[/C][C]-0.4634[/C][C]0.2341[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](2e-04 )[/C][C](0 )[/C][C](0 )[/C][C](0.0356 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/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 ( 4 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65274&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65274&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 )1.08380.1741-0.3113-0.6379-0.57570.2242
(p-val)(0.0131 )(0.7639 )(0.0961 )(0.1403 )(0.1196 )(0.0774 )
Estimates ( 2 )1.20920-0.257-0.7609-0.46340.2341
(p-val)(0 )(NA )(2e-04 )(0 )(0 )(0.0356 )
Estimates ( 3 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.254999833774829
22.0701127215431
8.36433828391489
30.8117254014586
16.1915303262615
6.24749029236703
-12.5367394721302
-2.99149529021186
-1.99425306328435
0.616226560421233
-6.10438312656669
3.29326413094505
-1.34475635055114
2.16220259803984
0.466465974381937
-8.37988679497044
1.48370050979315
-5.40621641384426
0.454652483104513
-12.2716717423166
-0.204655073195814
-1.65007197953775
2.2499955730374
-1.85072624893547
-3.36297236638801
-3.11127083993449
-5.70999310421676
-1.15860462228058
-1.47297866434169
4.83726638982067
18.4879583998847
15.9990020744729
-0.762899460203878
0.0112220977064385
1.3842373026051
0.89284726360497
-11.8728807771594
12.1403193622251
-9.69272372785388
1.11326869377849
3.11182513764967
-0.95107537837022
8.2991404937486
-5.52815286228392
-8.03312290877159
-1.39711503099374
-10.6840378639756
-2.99793154095573
0.155719827951926
-13.8030738239336
0.949584637638583
-2.01776250062261
-1.64806131778646
0.188895537280101
-5.21658815078316
-1.73878861782259
-2.36983813436027
-0.738754452044633
-5.40250883157664
8.77323738318406
-8.23029060709358
11.7926105191813
-7.33865453157388
0.531720620160846
-2.98289659640448
-1.99268202263952
-4.0331179520217
1.86839750147757
-3.54546654935486
-2.18041264706927
-1.59791086951404
-0.0361264572957054
0.143878134860028
-0.181223705822040
-1.69529400554080
-5.26940155166161
0.223770851028809
-2.09755898599944
-1.66141903219942
0.609686974283372
-3.94093125644668
0.935032373313295
0.355335408572966
-4.00754840309992
4.11896807867096
2.09077239482874
-5.00724500408199
0.892618249119731
-0.47043102758429
2.56408550389126
-0.148039240230724
-2.01616689774507
0.78761278403654
-0.265973850379846
-1.78183526604901
0.975976980907762
-0.956098524851435
1.83376618001511
-1.05718359034783
-0.983711932194683
-1.02401672871088
0.648999038562759
-0.307399207929173
-5.44828028816987
0.748530813117733
0.807692163808363
-1.53518905153138
-7.84527212784021
23.2071383216831
73.4485307687724
-14.6053792930629
-9.40777899139098
-4.81055185518272
3.35106634316061
-3.79420508124521
-5.64997765383986
-11.2173325290618
10.0672300577765
5.86769464141568
1.73666086121692
-1.37628462588406
-4.50337406059257
-0.733990268130773
-5.6678713962875
-4.11723361312781
3.49163419931303
-1.68594129207168
-7.40955190636959
8.0527714123691
4.16775701292084
-13.6757201247907
1.88698404241234
9.93119132753133
-3.36256059779036
-6.39305062201697
9.5970424170886
-5.72942723216623
0.393522358709841
5.33521811476509
-6.40571283128695
2.91412975635367
0.176779662158301
3.80299050597848
1.18051750486235
3.04672238220564
6.9161975919528
-4.59046691623531
9.06031876228998
1.10964029013548
0.421461777323031
5.77114143151452
-6.5870302548684
-3.3016723392041
-2.53145228268496
-9.44157152769664
10.0649447841715
-3.65388920881500
7.75207619786451
-10.6102068732342
4.47910444302628
3.74098874446585
3.29686805947676
5.61323212277222
-5.43529984105783
1.62707826145041
0.233598946423149
-7.54150886665361
3.62436151768803
0.385344627010861
3.6117653672756
-8.99763469287082
7.27214111125262
-6.95314475462037
2.42793695726124
-7.05701197745247
-4.20497877209415
-1.05124754081122
2.86965396941186
-4.61358097637659
-3.51615529133832
4.9704704271252
-0.672914022470041
-6.88821952811307
-0.625146035775328
4.25994032537233
-6.2442871922452
3.67683013348566
-6.92687937413921
-5.83168715409857
-3.2733267671613
-9.00379804429194
0.345970207119453
-2.14488921433102
9.47495807031888
-5.73945147141511
0.577539255620068
-0.0469167316188161
-0.470752466411669
-1.19287528878935
-6.62489306500083
0.0269620479469641
-9.26678806533768
4.47232672122208
1.39986476925363
2.70913561830144
-4.24544682858211
-2.16018509025056
-1.23765432288699
0.922931063844973
4.14733817864788
70.6170412843508
80.0707806722043
-45.8281061212248
5.92464498991463
-0.651257091866893
-0.744758246171697
9.45305381381474
-9.6506489668287
1.23785322178659
7.72250525679537
5.79004908882755
5.39857729368777
2.26065938025286
8.72910249967995
-2.13483706735515
-5.39529435290078
4.17531286250437
-0.0688265817895152
-10.3998957107002
12.1493153142952
-2.51581849294009
4.08278823378915
-6.27394098118605
6.46362676298868
-4.82536129652737
6.85669469232256
-5.81452601933648
6.61866900958894
-6.65161051730322
7.91667761333603
-2.16084181279363
4.71252508070221
18.7202679500435
31.5955319255078
7.0557992615288
26.5402914043781
7.4793236576099
2.03048937540645
-3.54461824731930
-14.1202599626465
6.16523187100784
-5.65792656868895
-0.557694162946648
4.24608661879968
13.2380476647214
-6.21788983879323
-0.0393103731083011
-3.83771489702011
6.50104067433956
-4.04804033390145
-1.71971393984079
-6.78101520026825
5.54961596111714
-1.88059376171758
3.04104819782335
0.0779795340485605
6.85138306311806
-2.68972267690885
5.14150137347304
-8.67974031824717
5.1308635061407
0.222098917037450
-1.61151381598843
10.4722924383439
-17.5851590445628
9.25035911591482
16.938331441986
8.16622907781725
-9.41006421663363
-1.60663059556635
-3.23191810304965
-5.64888680360242
6.63188673589147
-0.0322172043660745
-8.59283713571465
2.69555633080753
-6.47031503258074
1.38620641538037
1.71798455022879
-0.419870204259263
-1.20463701491107
-7.29910168767634
-0.0186201398777230
6.2825756230238
-8.51846178012792
-2.50967574245252
-1.62241555792219
-0.242552852567086
-3.36408701776258
-6.97477028075518
4.25612541567635
-1.57408379054557
-9.5084921334368
13.7454219116105
-3.70865078235602
-8.15829715765676
4.42384942062809
-7.71016523690223
0.90919398202758
-6.83400440995552
2.84880959333343
-7.20084873773176
17.7484005324881
-17.3354460521230
5.63128760242429
2.47409691079099
-9.01301727646203
0.482589816608236
0.0849720663744363
-3.46172200253705
-0.387601880073341
-10.6372814849686
-1.32523661715739
9.2770950017914
-3.29043557814422
-5.00613890644327
7.02646453155881
-7.14360524009903
-6.93127412738559
-7.08020348780749
15.2671024108337
-7.64346423055233
0.351944858128752
-6.58094779372229
-4.72676887838609
0.104412097958072
26.3582120771485
-24.4503510514464
13.4544105976783
14.6127326243637
1.81827787247582
3.73666909932297
-11.8339992650592
19.1381017889858
-10.8482584757963
5.41611011807587
-17.2652955393648
4.33872987969944
0.280312178014088
-2.71121441691193
14.6921640753653
-6.00035778683898
7.89816143741565
-22.8783238677690
10.3275811890924
1.37809321008169
-0.77357953511641
-3.87828264585647
4.27286907545171
-6.10806366141081

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.254999833774829 \tabularnewline
22.0701127215431 \tabularnewline
8.36433828391489 \tabularnewline
30.8117254014586 \tabularnewline
16.1915303262615 \tabularnewline
6.24749029236703 \tabularnewline
-12.5367394721302 \tabularnewline
-2.99149529021186 \tabularnewline
-1.99425306328435 \tabularnewline
0.616226560421233 \tabularnewline
-6.10438312656669 \tabularnewline
3.29326413094505 \tabularnewline
-1.34475635055114 \tabularnewline
2.16220259803984 \tabularnewline
0.466465974381937 \tabularnewline
-8.37988679497044 \tabularnewline
1.48370050979315 \tabularnewline
-5.40621641384426 \tabularnewline
0.454652483104513 \tabularnewline
-12.2716717423166 \tabularnewline
-0.204655073195814 \tabularnewline
-1.65007197953775 \tabularnewline
2.2499955730374 \tabularnewline
-1.85072624893547 \tabularnewline
-3.36297236638801 \tabularnewline
-3.11127083993449 \tabularnewline
-5.70999310421676 \tabularnewline
-1.15860462228058 \tabularnewline
-1.47297866434169 \tabularnewline
4.83726638982067 \tabularnewline
18.4879583998847 \tabularnewline
15.9990020744729 \tabularnewline
-0.762899460203878 \tabularnewline
0.0112220977064385 \tabularnewline
1.3842373026051 \tabularnewline
0.89284726360497 \tabularnewline
-11.8728807771594 \tabularnewline
12.1403193622251 \tabularnewline
-9.69272372785388 \tabularnewline
1.11326869377849 \tabularnewline
3.11182513764967 \tabularnewline
-0.95107537837022 \tabularnewline
8.2991404937486 \tabularnewline
-5.52815286228392 \tabularnewline
-8.03312290877159 \tabularnewline
-1.39711503099374 \tabularnewline
-10.6840378639756 \tabularnewline
-2.99793154095573 \tabularnewline
0.155719827951926 \tabularnewline
-13.8030738239336 \tabularnewline
0.949584637638583 \tabularnewline
-2.01776250062261 \tabularnewline
-1.64806131778646 \tabularnewline
0.188895537280101 \tabularnewline
-5.21658815078316 \tabularnewline
-1.73878861782259 \tabularnewline
-2.36983813436027 \tabularnewline
-0.738754452044633 \tabularnewline
-5.40250883157664 \tabularnewline
8.77323738318406 \tabularnewline
-8.23029060709358 \tabularnewline
11.7926105191813 \tabularnewline
-7.33865453157388 \tabularnewline
0.531720620160846 \tabularnewline
-2.98289659640448 \tabularnewline
-1.99268202263952 \tabularnewline
-4.0331179520217 \tabularnewline
1.86839750147757 \tabularnewline
-3.54546654935486 \tabularnewline
-2.18041264706927 \tabularnewline
-1.59791086951404 \tabularnewline
-0.0361264572957054 \tabularnewline
0.143878134860028 \tabularnewline
-0.181223705822040 \tabularnewline
-1.69529400554080 \tabularnewline
-5.26940155166161 \tabularnewline
0.223770851028809 \tabularnewline
-2.09755898599944 \tabularnewline
-1.66141903219942 \tabularnewline
0.609686974283372 \tabularnewline
-3.94093125644668 \tabularnewline
0.935032373313295 \tabularnewline
0.355335408572966 \tabularnewline
-4.00754840309992 \tabularnewline
4.11896807867096 \tabularnewline
2.09077239482874 \tabularnewline
-5.00724500408199 \tabularnewline
0.892618249119731 \tabularnewline
-0.47043102758429 \tabularnewline
2.56408550389126 \tabularnewline
-0.148039240230724 \tabularnewline
-2.01616689774507 \tabularnewline
0.78761278403654 \tabularnewline
-0.265973850379846 \tabularnewline
-1.78183526604901 \tabularnewline
0.975976980907762 \tabularnewline
-0.956098524851435 \tabularnewline
1.83376618001511 \tabularnewline
-1.05718359034783 \tabularnewline
-0.983711932194683 \tabularnewline
-1.02401672871088 \tabularnewline
0.648999038562759 \tabularnewline
-0.307399207929173 \tabularnewline
-5.44828028816987 \tabularnewline
0.748530813117733 \tabularnewline
0.807692163808363 \tabularnewline
-1.53518905153138 \tabularnewline
-7.84527212784021 \tabularnewline
23.2071383216831 \tabularnewline
73.4485307687724 \tabularnewline
-14.6053792930629 \tabularnewline
-9.40777899139098 \tabularnewline
-4.81055185518272 \tabularnewline
3.35106634316061 \tabularnewline
-3.79420508124521 \tabularnewline
-5.64997765383986 \tabularnewline
-11.2173325290618 \tabularnewline
10.0672300577765 \tabularnewline
5.86769464141568 \tabularnewline
1.73666086121692 \tabularnewline
-1.37628462588406 \tabularnewline
-4.50337406059257 \tabularnewline
-0.733990268130773 \tabularnewline
-5.6678713962875 \tabularnewline
-4.11723361312781 \tabularnewline
3.49163419931303 \tabularnewline
-1.68594129207168 \tabularnewline
-7.40955190636959 \tabularnewline
8.0527714123691 \tabularnewline
4.16775701292084 \tabularnewline
-13.6757201247907 \tabularnewline
1.88698404241234 \tabularnewline
9.93119132753133 \tabularnewline
-3.36256059779036 \tabularnewline
-6.39305062201697 \tabularnewline
9.5970424170886 \tabularnewline
-5.72942723216623 \tabularnewline
0.393522358709841 \tabularnewline
5.33521811476509 \tabularnewline
-6.40571283128695 \tabularnewline
2.91412975635367 \tabularnewline
0.176779662158301 \tabularnewline
3.80299050597848 \tabularnewline
1.18051750486235 \tabularnewline
3.04672238220564 \tabularnewline
6.9161975919528 \tabularnewline
-4.59046691623531 \tabularnewline
9.06031876228998 \tabularnewline
1.10964029013548 \tabularnewline
0.421461777323031 \tabularnewline
5.77114143151452 \tabularnewline
-6.5870302548684 \tabularnewline
-3.3016723392041 \tabularnewline
-2.53145228268496 \tabularnewline
-9.44157152769664 \tabularnewline
10.0649447841715 \tabularnewline
-3.65388920881500 \tabularnewline
7.75207619786451 \tabularnewline
-10.6102068732342 \tabularnewline
4.47910444302628 \tabularnewline
3.74098874446585 \tabularnewline
3.29686805947676 \tabularnewline
5.61323212277222 \tabularnewline
-5.43529984105783 \tabularnewline
1.62707826145041 \tabularnewline
0.233598946423149 \tabularnewline
-7.54150886665361 \tabularnewline
3.62436151768803 \tabularnewline
0.385344627010861 \tabularnewline
3.6117653672756 \tabularnewline
-8.99763469287082 \tabularnewline
7.27214111125262 \tabularnewline
-6.95314475462037 \tabularnewline
2.42793695726124 \tabularnewline
-7.05701197745247 \tabularnewline
-4.20497877209415 \tabularnewline
-1.05124754081122 \tabularnewline
2.86965396941186 \tabularnewline
-4.61358097637659 \tabularnewline
-3.51615529133832 \tabularnewline
4.9704704271252 \tabularnewline
-0.672914022470041 \tabularnewline
-6.88821952811307 \tabularnewline
-0.625146035775328 \tabularnewline
4.25994032537233 \tabularnewline
-6.2442871922452 \tabularnewline
3.67683013348566 \tabularnewline
-6.92687937413921 \tabularnewline
-5.83168715409857 \tabularnewline
-3.2733267671613 \tabularnewline
-9.00379804429194 \tabularnewline
0.345970207119453 \tabularnewline
-2.14488921433102 \tabularnewline
9.47495807031888 \tabularnewline
-5.73945147141511 \tabularnewline
0.577539255620068 \tabularnewline
-0.0469167316188161 \tabularnewline
-0.470752466411669 \tabularnewline
-1.19287528878935 \tabularnewline
-6.62489306500083 \tabularnewline
0.0269620479469641 \tabularnewline
-9.26678806533768 \tabularnewline
4.47232672122208 \tabularnewline
1.39986476925363 \tabularnewline
2.70913561830144 \tabularnewline
-4.24544682858211 \tabularnewline
-2.16018509025056 \tabularnewline
-1.23765432288699 \tabularnewline
0.922931063844973 \tabularnewline
4.14733817864788 \tabularnewline
70.6170412843508 \tabularnewline
80.0707806722043 \tabularnewline
-45.8281061212248 \tabularnewline
5.92464498991463 \tabularnewline
-0.651257091866893 \tabularnewline
-0.744758246171697 \tabularnewline
9.45305381381474 \tabularnewline
-9.6506489668287 \tabularnewline
1.23785322178659 \tabularnewline
7.72250525679537 \tabularnewline
5.79004908882755 \tabularnewline
5.39857729368777 \tabularnewline
2.26065938025286 \tabularnewline
8.72910249967995 \tabularnewline
-2.13483706735515 \tabularnewline
-5.39529435290078 \tabularnewline
4.17531286250437 \tabularnewline
-0.0688265817895152 \tabularnewline
-10.3998957107002 \tabularnewline
12.1493153142952 \tabularnewline
-2.51581849294009 \tabularnewline
4.08278823378915 \tabularnewline
-6.27394098118605 \tabularnewline
6.46362676298868 \tabularnewline
-4.82536129652737 \tabularnewline
6.85669469232256 \tabularnewline
-5.81452601933648 \tabularnewline
6.61866900958894 \tabularnewline
-6.65161051730322 \tabularnewline
7.91667761333603 \tabularnewline
-2.16084181279363 \tabularnewline
4.71252508070221 \tabularnewline
18.7202679500435 \tabularnewline
31.5955319255078 \tabularnewline
7.0557992615288 \tabularnewline
26.5402914043781 \tabularnewline
7.4793236576099 \tabularnewline
2.03048937540645 \tabularnewline
-3.54461824731930 \tabularnewline
-14.1202599626465 \tabularnewline
6.16523187100784 \tabularnewline
-5.65792656868895 \tabularnewline
-0.557694162946648 \tabularnewline
4.24608661879968 \tabularnewline
13.2380476647214 \tabularnewline
-6.21788983879323 \tabularnewline
-0.0393103731083011 \tabularnewline
-3.83771489702011 \tabularnewline
6.50104067433956 \tabularnewline
-4.04804033390145 \tabularnewline
-1.71971393984079 \tabularnewline
-6.78101520026825 \tabularnewline
5.54961596111714 \tabularnewline
-1.88059376171758 \tabularnewline
3.04104819782335 \tabularnewline
0.0779795340485605 \tabularnewline
6.85138306311806 \tabularnewline
-2.68972267690885 \tabularnewline
5.14150137347304 \tabularnewline
-8.67974031824717 \tabularnewline
5.1308635061407 \tabularnewline
0.222098917037450 \tabularnewline
-1.61151381598843 \tabularnewline
10.4722924383439 \tabularnewline
-17.5851590445628 \tabularnewline
9.25035911591482 \tabularnewline
16.938331441986 \tabularnewline
8.16622907781725 \tabularnewline
-9.41006421663363 \tabularnewline
-1.60663059556635 \tabularnewline
-3.23191810304965 \tabularnewline
-5.64888680360242 \tabularnewline
6.63188673589147 \tabularnewline
-0.0322172043660745 \tabularnewline
-8.59283713571465 \tabularnewline
2.69555633080753 \tabularnewline
-6.47031503258074 \tabularnewline
1.38620641538037 \tabularnewline
1.71798455022879 \tabularnewline
-0.419870204259263 \tabularnewline
-1.20463701491107 \tabularnewline
-7.29910168767634 \tabularnewline
-0.0186201398777230 \tabularnewline
6.2825756230238 \tabularnewline
-8.51846178012792 \tabularnewline
-2.50967574245252 \tabularnewline
-1.62241555792219 \tabularnewline
-0.242552852567086 \tabularnewline
-3.36408701776258 \tabularnewline
-6.97477028075518 \tabularnewline
4.25612541567635 \tabularnewline
-1.57408379054557 \tabularnewline
-9.5084921334368 \tabularnewline
13.7454219116105 \tabularnewline
-3.70865078235602 \tabularnewline
-8.15829715765676 \tabularnewline
4.42384942062809 \tabularnewline
-7.71016523690223 \tabularnewline
0.90919398202758 \tabularnewline
-6.83400440995552 \tabularnewline
2.84880959333343 \tabularnewline
-7.20084873773176 \tabularnewline
17.7484005324881 \tabularnewline
-17.3354460521230 \tabularnewline
5.63128760242429 \tabularnewline
2.47409691079099 \tabularnewline
-9.01301727646203 \tabularnewline
0.482589816608236 \tabularnewline
0.0849720663744363 \tabularnewline
-3.46172200253705 \tabularnewline
-0.387601880073341 \tabularnewline
-10.6372814849686 \tabularnewline
-1.32523661715739 \tabularnewline
9.2770950017914 \tabularnewline
-3.29043557814422 \tabularnewline
-5.00613890644327 \tabularnewline
7.02646453155881 \tabularnewline
-7.14360524009903 \tabularnewline
-6.93127412738559 \tabularnewline
-7.08020348780749 \tabularnewline
15.2671024108337 \tabularnewline
-7.64346423055233 \tabularnewline
0.351944858128752 \tabularnewline
-6.58094779372229 \tabularnewline
-4.72676887838609 \tabularnewline
0.104412097958072 \tabularnewline
26.3582120771485 \tabularnewline
-24.4503510514464 \tabularnewline
13.4544105976783 \tabularnewline
14.6127326243637 \tabularnewline
1.81827787247582 \tabularnewline
3.73666909932297 \tabularnewline
-11.8339992650592 \tabularnewline
19.1381017889858 \tabularnewline
-10.8482584757963 \tabularnewline
5.41611011807587 \tabularnewline
-17.2652955393648 \tabularnewline
4.33872987969944 \tabularnewline
0.280312178014088 \tabularnewline
-2.71121441691193 \tabularnewline
14.6921640753653 \tabularnewline
-6.00035778683898 \tabularnewline
7.89816143741565 \tabularnewline
-22.8783238677690 \tabularnewline
10.3275811890924 \tabularnewline
1.37809321008169 \tabularnewline
-0.77357953511641 \tabularnewline
-3.87828264585647 \tabularnewline
4.27286907545171 \tabularnewline
-6.10806366141081 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65274&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.254999833774829[/C][/ROW]
[ROW][C]22.0701127215431[/C][/ROW]
[ROW][C]8.36433828391489[/C][/ROW]
[ROW][C]30.8117254014586[/C][/ROW]
[ROW][C]16.1915303262615[/C][/ROW]
[ROW][C]6.24749029236703[/C][/ROW]
[ROW][C]-12.5367394721302[/C][/ROW]
[ROW][C]-2.99149529021186[/C][/ROW]
[ROW][C]-1.99425306328435[/C][/ROW]
[ROW][C]0.616226560421233[/C][/ROW]
[ROW][C]-6.10438312656669[/C][/ROW]
[ROW][C]3.29326413094505[/C][/ROW]
[ROW][C]-1.34475635055114[/C][/ROW]
[ROW][C]2.16220259803984[/C][/ROW]
[ROW][C]0.466465974381937[/C][/ROW]
[ROW][C]-8.37988679497044[/C][/ROW]
[ROW][C]1.48370050979315[/C][/ROW]
[ROW][C]-5.40621641384426[/C][/ROW]
[ROW][C]0.454652483104513[/C][/ROW]
[ROW][C]-12.2716717423166[/C][/ROW]
[ROW][C]-0.204655073195814[/C][/ROW]
[ROW][C]-1.65007197953775[/C][/ROW]
[ROW][C]2.2499955730374[/C][/ROW]
[ROW][C]-1.85072624893547[/C][/ROW]
[ROW][C]-3.36297236638801[/C][/ROW]
[ROW][C]-3.11127083993449[/C][/ROW]
[ROW][C]-5.70999310421676[/C][/ROW]
[ROW][C]-1.15860462228058[/C][/ROW]
[ROW][C]-1.47297866434169[/C][/ROW]
[ROW][C]4.83726638982067[/C][/ROW]
[ROW][C]18.4879583998847[/C][/ROW]
[ROW][C]15.9990020744729[/C][/ROW]
[ROW][C]-0.762899460203878[/C][/ROW]
[ROW][C]0.0112220977064385[/C][/ROW]
[ROW][C]1.3842373026051[/C][/ROW]
[ROW][C]0.89284726360497[/C][/ROW]
[ROW][C]-11.8728807771594[/C][/ROW]
[ROW][C]12.1403193622251[/C][/ROW]
[ROW][C]-9.69272372785388[/C][/ROW]
[ROW][C]1.11326869377849[/C][/ROW]
[ROW][C]3.11182513764967[/C][/ROW]
[ROW][C]-0.95107537837022[/C][/ROW]
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[ROW][C]4.27286907545171[/C][/ROW]
[ROW][C]-6.10806366141081[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65274&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65274&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
0.254999833774829
22.0701127215431
8.36433828391489
30.8117254014586
16.1915303262615
6.24749029236703
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0.616226560421233
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-1.34475635055114
2.16220259803984
0.466465974381937
-8.37988679497044
1.48370050979315
-5.40621641384426
0.454652483104513
-12.2716717423166
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-1.65007197953775
2.2499955730374
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4.83726638982067
18.4879583998847
15.9990020744729
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0.0112220977064385
1.3842373026051
0.89284726360497
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12.1403193622251
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1.11326869377849
3.11182513764967
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8.2991404937486
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0.155719827951926
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0.949584637638583
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0.188895537280101
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8.77323738318406
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11.7926105191813
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0.531720620160846
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4.11896807867096
2.09077239482874
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2.56408550389126
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23.2071383216831
73.4485307687724
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3.35106634316061
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10.0672300577765
5.86769464141568
1.73666086121692
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Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; 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')