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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 computationMon, 05 Dec 2011 13:48:35 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/05/t13231110073rudhobi1slitxt.htm/, Retrieved Fri, 03 May 2024 13:26:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=151163, Retrieved Fri, 03 May 2024 13:26:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact115
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMP         [Standard Deviation-Mean Plot] [Births] [2010-11-29 10:52:49] [b98453cac15ba1066b407e146608df68]
- RMP           [ARIMA Backward Selection] [Births] [2010-11-29 17:42:52] [b98453cac15ba1066b407e146608df68]
F R PD            [ARIMA Backward Selection] [ARIMA] [2010-12-02 20:40:17] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- R PD                [ARIMA Backward Selection] [] [2011-12-05 18:48:35] [ce4468323d272130d499477f5e05a6d2] [Current]
- R PD                  [ARIMA Backward Selection] [] [2011-12-20 19:48:55] [06c08141d7d783218a8164fd2ea166f2]
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Dataseries X:
276986
260633
291551
275383
275302
231693
238829
274215
277808
299060
286629
232313
294053
267510
309739
280733
287298
235672
256449
288997
290789
321898
291834
241380
295469
258200
306102
281480
283101
237414
274834
299340
300383
340862
318794
265740
322656
281563
323461
312579
310784
262785
273754
320036
310336
342206
320052
265582
326988
300713
346414
317325
326208
270657
278158
324584
321801
343542
354040
278179
330246
307344
375874
335309
339271
280264
293689
341161
345097
368712
369403
288384
340981
319072
374214
344529
337271
281016
282224
320984
325426
366276
380296
300727
359326
327610
383563
352405
329351
294486
333454
334339
358000
396057
386976
307155
363909
344700
397561
376791
337085
299252
323136
329091
346991
461999
436533
360372
415467
382110
432197
424254
386728
354508
375765
367986
402378
426516
433313
338461
416834
381099
445673
412408
393997
348241
380134
373688
393588
434192
430731
344468
411891
370497
437305
411270
385495
341273
384217
373223
415771
448634
454341
350297
419104
398027
456059
430052
399757
362731
384896
385349
432289
468891
442702
370178
439400
393900
468700
438800
430100
366300
391000
380900
431400
465400
471500
387500
446400
421500
504800
492071
421253
396682
428000
421900
465600
525793
499855
435287
479499
473027
554410
489574
462157
420331




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151163&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151163&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151163&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 time10 seconds
R Server'AstonUniversity' @ aston.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.5616-0.2868-0.0923-0.99980.08830.1647-0.6966
(p-val)(0 )(0.0012 )(0.2341 )(0 )(0.6365 )(0.2061 )(0 )
Estimates ( 2 )-0.5632-0.2895-0.0898-1.000200.1211-0.623
(p-val)(0 )(0.0011 )(0.246 )(0 )(NA )(0.1768 )(0 )
Estimates ( 3 )-0.541-0.24170-1.000200.1235-0.6247
(p-val)(0 )(0.002 )(NA )(0 )(NA )(0.17 )(0 )
Estimates ( 4 )-0.5435-0.22460-1.000200-0.5814
(p-val)(0 )(0.004 )(NA )(0 )(NA )(NA )(0 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & -0.5616 & -0.2868 & -0.0923 & -0.9998 & 0.0883 & 0.1647 & -0.6966 \tabularnewline
(p-val) & (0 ) & (0.0012 ) & (0.2341 ) & (0 ) & (0.6365 ) & (0.2061 ) & (0 ) \tabularnewline
Estimates ( 2 ) & -0.5632 & -0.2895 & -0.0898 & -1.0002 & 0 & 0.1211 & -0.623 \tabularnewline
(p-val) & (0 ) & (0.0011 ) & (0.246 ) & (0 ) & (NA ) & (0.1768 ) & (0 ) \tabularnewline
Estimates ( 3 ) & -0.541 & -0.2417 & 0 & -1.0002 & 0 & 0.1235 & -0.6247 \tabularnewline
(p-val) & (0 ) & (0.002 ) & (NA ) & (0 ) & (NA ) & (0.17 ) & (0 ) \tabularnewline
Estimates ( 4 ) & -0.5435 & -0.2246 & 0 & -1.0002 & 0 & 0 & -0.5814 \tabularnewline
(p-val) & (0 ) & (0.004 ) & (NA ) & (0 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151163&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ar3[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.5616[/C][C]-0.2868[/C][C]-0.0923[/C][C]-0.9998[/C][C]0.0883[/C][C]0.1647[/C][C]-0.6966[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0012 )[/C][C](0.2341 )[/C][C](0 )[/C][C](0.6365 )[/C][C](0.2061 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.5632[/C][C]-0.2895[/C][C]-0.0898[/C][C]-1.0002[/C][C]0[/C][C]0.1211[/C][C]-0.623[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0011 )[/C][C](0.246 )[/C][C](0 )[/C][C](NA )[/C][C](0.1768 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.541[/C][C]-0.2417[/C][C]0[/C][C]-1.0002[/C][C]0[/C][C]0.1235[/C][C]-0.6247[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.002 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](0.17 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.5435[/C][C]-0.2246[/C][C]0[/C][C]-1.0002[/C][C]0[/C][C]0[/C][C]-0.5814[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.004 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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][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][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][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][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][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][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][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][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][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][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][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][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][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][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/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][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/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][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151163&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151163&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
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )-0.5616-0.2868-0.0923-0.99980.08830.1647-0.6966
(p-val)(0 )(0.0012 )(0.2341 )(0 )(0.6365 )(0.2061 )(0 )
Estimates ( 2 )-0.5632-0.2895-0.0898-1.000200.1211-0.623
(p-val)(0 )(0.0011 )(0.246 )(0 )(NA )(0.1768 )(0 )
Estimates ( 3 )-0.541-0.24170-1.000200.1235-0.6247
(p-val)(0 )(0.002 )(NA )(0 )(NA )(0.17 )(0 )
Estimates ( 4 )-0.5435-0.22460-1.000200-0.5814
(p-val)(0 )(0.004 )(NA )(0 )(NA )(NA )(0 )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
1.30449222496613
8.37733252482086
-5.07717354846436
4.54703440013222
-3.36292258471267
10.7957782587327
1.52662986391075
-0.506543863839457
5.21758748152724
-10.2203559400822
-0.939699794509995
-6.0391882921763
-13.7671139474289
5.28048319677654
3.49388268896546
2.72191509831473
2.99262684179177
23.6064286976126
1.63303690747063
-2.47821625923261
7.29051911317096
4.04998070324525
2.90938435102005
-3.53818590150523
-12.0890326081821
-8.41456320080955
9.17321156310524
0.827402809765435
1.46203909246844
-15.1635096980948
6.10975152208571
-6.68669001523063
-4.12471968460813
-1.76218252210644
-0.0153394046583145
3.33600113967315
10.4249429749972
5.18299383918814
-6.30375581754534
2.15372485755015
-4.19397601274833
-15.6916162211301
0.483082341588017
1.65593114456682
-8.60512360644606
22.0750850249745
-5.25156297956065
-9.9939865006534
1.05101571561197
22.3047956473081
-3.21892994411296
-3.78102147343071
-9.4199388192435
-3.30647120750177
1.65695097755448
8.36925587399258
-0.838929721161382
7.65444014505488
-10.5536942239347
-10.7918503904023
-1.07044527903989
1.55575784650546
2.87783284977922
-9.63958189221061
-5.39835510692419
-13.3757216086743
-10.1423178383325
-0.306772522692924
14.7316309905013
21.4308387831418
6.171457223669
2.78725357328225
-5.19393949815657
-4.48401458762278
-1.15633916104445
-19.1807050028569
9.56601936814616
31.8088876737766
-18.8852574954713
4.02153989338955
4.81844060010854
-2.97593769101074
-5.85840715319092
-3.95152234956804
6.73968786633743
0.419831815830935
8.44007691581977
-20.7376286728947
-1.48010571706311
3.67475962718878
-13.2745130833936
-1.93263110681941
59.4498557344793
14.54529935208
9.53352475536461
-7.96307632931414
-7.0055828635666
-10.8217338764561
10.9355162240274
-3.40974124376879
8.44262868395734
-2.71510088932548
-19.5761487862528
2.48370747338826
-30.7376118390498
-0.24593005274799
-12.6036183983474
13.0655418378779
-0.394882403664419
7.96273471960752
-7.95866954989581
6.50834531047491
-1.1993414914655
7.2364369912092
-8.41020701682505
-6.4705221152908
-19.3681973735558
-5.99844681223413
-3.56808975519529
1.83708194802653
-6.49105412451408
4.27569929242438
0.176581717031863
0.931683882663412
-2.83678080386747
13.0280872459644
-1.97300291335701
13.5449551021224
-1.51427127864369
5.22460349660378
-10.4957374430768
-5.57196199920367
9.14272802952696
1.68296809078247
1.72107032703935
-5.255114107004
4.1926340487574
-7.38752212068854
-0.196343436480517
13.1694251248965
4.208125331191
-17.9931822041187
5.77045892798511
4.21529373815429
-6.57236109127688
3.53853146770071
0.0209724592091487
15.479668645339
-9.43214900200741
-10.7557611966552
-12.6638939302477
4.92936823899019
0.176147331601175
11.4396162070719
10.099938666455
-3.79506136842779
3.97344712683543
12.9419378505757
19.2011035136113
-27.5776055507858
3.18639778668805
5.15805711107089
5.03607890189556
-1.56800497607954
13.56315516472
-5.30347979134711
13.3273440141973
-12.7357454042206
15.1361185855331
7.60784052410023
-22.9867742097005
-7.53103532491062
0.017227228447983

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
1.30449222496613 \tabularnewline
8.37733252482086 \tabularnewline
-5.07717354846436 \tabularnewline
4.54703440013222 \tabularnewline
-3.36292258471267 \tabularnewline
10.7957782587327 \tabularnewline
1.52662986391075 \tabularnewline
-0.506543863839457 \tabularnewline
5.21758748152724 \tabularnewline
-10.2203559400822 \tabularnewline
-0.939699794509995 \tabularnewline
-6.0391882921763 \tabularnewline
-13.7671139474289 \tabularnewline
5.28048319677654 \tabularnewline
3.49388268896546 \tabularnewline
2.72191509831473 \tabularnewline
2.99262684179177 \tabularnewline
23.6064286976126 \tabularnewline
1.63303690747063 \tabularnewline
-2.47821625923261 \tabularnewline
7.29051911317096 \tabularnewline
4.04998070324525 \tabularnewline
2.90938435102005 \tabularnewline
-3.53818590150523 \tabularnewline
-12.0890326081821 \tabularnewline
-8.41456320080955 \tabularnewline
9.17321156310524 \tabularnewline
0.827402809765435 \tabularnewline
1.46203909246844 \tabularnewline
-15.1635096980948 \tabularnewline
6.10975152208571 \tabularnewline
-6.68669001523063 \tabularnewline
-4.12471968460813 \tabularnewline
-1.76218252210644 \tabularnewline
-0.0153394046583145 \tabularnewline
3.33600113967315 \tabularnewline
10.4249429749972 \tabularnewline
5.18299383918814 \tabularnewline
-6.30375581754534 \tabularnewline
2.15372485755015 \tabularnewline
-4.19397601274833 \tabularnewline
-15.6916162211301 \tabularnewline
0.483082341588017 \tabularnewline
1.65593114456682 \tabularnewline
-8.60512360644606 \tabularnewline
22.0750850249745 \tabularnewline
-5.25156297956065 \tabularnewline
-9.9939865006534 \tabularnewline
1.05101571561197 \tabularnewline
22.3047956473081 \tabularnewline
-3.21892994411296 \tabularnewline
-3.78102147343071 \tabularnewline
-9.4199388192435 \tabularnewline
-3.30647120750177 \tabularnewline
1.65695097755448 \tabularnewline
8.36925587399258 \tabularnewline
-0.838929721161382 \tabularnewline
7.65444014505488 \tabularnewline
-10.5536942239347 \tabularnewline
-10.7918503904023 \tabularnewline
-1.07044527903989 \tabularnewline
1.55575784650546 \tabularnewline
2.87783284977922 \tabularnewline
-9.63958189221061 \tabularnewline
-5.39835510692419 \tabularnewline
-13.3757216086743 \tabularnewline
-10.1423178383325 \tabularnewline
-0.306772522692924 \tabularnewline
14.7316309905013 \tabularnewline
21.4308387831418 \tabularnewline
6.171457223669 \tabularnewline
2.78725357328225 \tabularnewline
-5.19393949815657 \tabularnewline
-4.48401458762278 \tabularnewline
-1.15633916104445 \tabularnewline
-19.1807050028569 \tabularnewline
9.56601936814616 \tabularnewline
31.8088876737766 \tabularnewline
-18.8852574954713 \tabularnewline
4.02153989338955 \tabularnewline
4.81844060010854 \tabularnewline
-2.97593769101074 \tabularnewline
-5.85840715319092 \tabularnewline
-3.95152234956804 \tabularnewline
6.73968786633743 \tabularnewline
0.419831815830935 \tabularnewline
8.44007691581977 \tabularnewline
-20.7376286728947 \tabularnewline
-1.48010571706311 \tabularnewline
3.67475962718878 \tabularnewline
-13.2745130833936 \tabularnewline
-1.93263110681941 \tabularnewline
59.4498557344793 \tabularnewline
14.54529935208 \tabularnewline
9.53352475536461 \tabularnewline
-7.96307632931414 \tabularnewline
-7.0055828635666 \tabularnewline
-10.8217338764561 \tabularnewline
10.9355162240274 \tabularnewline
-3.40974124376879 \tabularnewline
8.44262868395734 \tabularnewline
-2.71510088932548 \tabularnewline
-19.5761487862528 \tabularnewline
2.48370747338826 \tabularnewline
-30.7376118390498 \tabularnewline
-0.24593005274799 \tabularnewline
-12.6036183983474 \tabularnewline
13.0655418378779 \tabularnewline
-0.394882403664419 \tabularnewline
7.96273471960752 \tabularnewline
-7.95866954989581 \tabularnewline
6.50834531047491 \tabularnewline
-1.1993414914655 \tabularnewline
7.2364369912092 \tabularnewline
-8.41020701682505 \tabularnewline
-6.4705221152908 \tabularnewline
-19.3681973735558 \tabularnewline
-5.99844681223413 \tabularnewline
-3.56808975519529 \tabularnewline
1.83708194802653 \tabularnewline
-6.49105412451408 \tabularnewline
4.27569929242438 \tabularnewline
0.176581717031863 \tabularnewline
0.931683882663412 \tabularnewline
-2.83678080386747 \tabularnewline
13.0280872459644 \tabularnewline
-1.97300291335701 \tabularnewline
13.5449551021224 \tabularnewline
-1.51427127864369 \tabularnewline
5.22460349660378 \tabularnewline
-10.4957374430768 \tabularnewline
-5.57196199920367 \tabularnewline
9.14272802952696 \tabularnewline
1.68296809078247 \tabularnewline
1.72107032703935 \tabularnewline
-5.255114107004 \tabularnewline
4.1926340487574 \tabularnewline
-7.38752212068854 \tabularnewline
-0.196343436480517 \tabularnewline
13.1694251248965 \tabularnewline
4.208125331191 \tabularnewline
-17.9931822041187 \tabularnewline
5.77045892798511 \tabularnewline
4.21529373815429 \tabularnewline
-6.57236109127688 \tabularnewline
3.53853146770071 \tabularnewline
0.0209724592091487 \tabularnewline
15.479668645339 \tabularnewline
-9.43214900200741 \tabularnewline
-10.7557611966552 \tabularnewline
-12.6638939302477 \tabularnewline
4.92936823899019 \tabularnewline
0.176147331601175 \tabularnewline
11.4396162070719 \tabularnewline
10.099938666455 \tabularnewline
-3.79506136842779 \tabularnewline
3.97344712683543 \tabularnewline
12.9419378505757 \tabularnewline
19.2011035136113 \tabularnewline
-27.5776055507858 \tabularnewline
3.18639778668805 \tabularnewline
5.15805711107089 \tabularnewline
5.03607890189556 \tabularnewline
-1.56800497607954 \tabularnewline
13.56315516472 \tabularnewline
-5.30347979134711 \tabularnewline
13.3273440141973 \tabularnewline
-12.7357454042206 \tabularnewline
15.1361185855331 \tabularnewline
7.60784052410023 \tabularnewline
-22.9867742097005 \tabularnewline
-7.53103532491062 \tabularnewline
0.017227228447983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=151163&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]1.30449222496613[/C][/ROW]
[ROW][C]8.37733252482086[/C][/ROW]
[ROW][C]-5.07717354846436[/C][/ROW]
[ROW][C]4.54703440013222[/C][/ROW]
[ROW][C]-3.36292258471267[/C][/ROW]
[ROW][C]10.7957782587327[/C][/ROW]
[ROW][C]1.52662986391075[/C][/ROW]
[ROW][C]-0.506543863839457[/C][/ROW]
[ROW][C]5.21758748152724[/C][/ROW]
[ROW][C]-10.2203559400822[/C][/ROW]
[ROW][C]-0.939699794509995[/C][/ROW]
[ROW][C]-6.0391882921763[/C][/ROW]
[ROW][C]-13.7671139474289[/C][/ROW]
[ROW][C]5.28048319677654[/C][/ROW]
[ROW][C]3.49388268896546[/C][/ROW]
[ROW][C]2.72191509831473[/C][/ROW]
[ROW][C]2.99262684179177[/C][/ROW]
[ROW][C]23.6064286976126[/C][/ROW]
[ROW][C]1.63303690747063[/C][/ROW]
[ROW][C]-2.47821625923261[/C][/ROW]
[ROW][C]7.29051911317096[/C][/ROW]
[ROW][C]4.04998070324525[/C][/ROW]
[ROW][C]2.90938435102005[/C][/ROW]
[ROW][C]-3.53818590150523[/C][/ROW]
[ROW][C]-12.0890326081821[/C][/ROW]
[ROW][C]-8.41456320080955[/C][/ROW]
[ROW][C]9.17321156310524[/C][/ROW]
[ROW][C]0.827402809765435[/C][/ROW]
[ROW][C]1.46203909246844[/C][/ROW]
[ROW][C]-15.1635096980948[/C][/ROW]
[ROW][C]6.10975152208571[/C][/ROW]
[ROW][C]-6.68669001523063[/C][/ROW]
[ROW][C]-4.12471968460813[/C][/ROW]
[ROW][C]-1.76218252210644[/C][/ROW]
[ROW][C]-0.0153394046583145[/C][/ROW]
[ROW][C]3.33600113967315[/C][/ROW]
[ROW][C]10.4249429749972[/C][/ROW]
[ROW][C]5.18299383918814[/C][/ROW]
[ROW][C]-6.30375581754534[/C][/ROW]
[ROW][C]2.15372485755015[/C][/ROW]
[ROW][C]-4.19397601274833[/C][/ROW]
[ROW][C]-15.6916162211301[/C][/ROW]
[ROW][C]0.483082341588017[/C][/ROW]
[ROW][C]1.65593114456682[/C][/ROW]
[ROW][C]-8.60512360644606[/C][/ROW]
[ROW][C]22.0750850249745[/C][/ROW]
[ROW][C]-5.25156297956065[/C][/ROW]
[ROW][C]-9.9939865006534[/C][/ROW]
[ROW][C]1.05101571561197[/C][/ROW]
[ROW][C]22.3047956473081[/C][/ROW]
[ROW][C]-3.21892994411296[/C][/ROW]
[ROW][C]-3.78102147343071[/C][/ROW]
[ROW][C]-9.4199388192435[/C][/ROW]
[ROW][C]-3.30647120750177[/C][/ROW]
[ROW][C]1.65695097755448[/C][/ROW]
[ROW][C]8.36925587399258[/C][/ROW]
[ROW][C]-0.838929721161382[/C][/ROW]
[ROW][C]7.65444014505488[/C][/ROW]
[ROW][C]-10.5536942239347[/C][/ROW]
[ROW][C]-10.7918503904023[/C][/ROW]
[ROW][C]-1.07044527903989[/C][/ROW]
[ROW][C]1.55575784650546[/C][/ROW]
[ROW][C]2.87783284977922[/C][/ROW]
[ROW][C]-9.63958189221061[/C][/ROW]
[ROW][C]-5.39835510692419[/C][/ROW]
[ROW][C]-13.3757216086743[/C][/ROW]
[ROW][C]-10.1423178383325[/C][/ROW]
[ROW][C]-0.306772522692924[/C][/ROW]
[ROW][C]14.7316309905013[/C][/ROW]
[ROW][C]21.4308387831418[/C][/ROW]
[ROW][C]6.171457223669[/C][/ROW]
[ROW][C]2.78725357328225[/C][/ROW]
[ROW][C]-5.19393949815657[/C][/ROW]
[ROW][C]-4.48401458762278[/C][/ROW]
[ROW][C]-1.15633916104445[/C][/ROW]
[ROW][C]-19.1807050028569[/C][/ROW]
[ROW][C]9.56601936814616[/C][/ROW]
[ROW][C]31.8088876737766[/C][/ROW]
[ROW][C]-18.8852574954713[/C][/ROW]
[ROW][C]4.02153989338955[/C][/ROW]
[ROW][C]4.81844060010854[/C][/ROW]
[ROW][C]-2.97593769101074[/C][/ROW]
[ROW][C]-5.85840715319092[/C][/ROW]
[ROW][C]-3.95152234956804[/C][/ROW]
[ROW][C]6.73968786633743[/C][/ROW]
[ROW][C]0.419831815830935[/C][/ROW]
[ROW][C]8.44007691581977[/C][/ROW]
[ROW][C]-20.7376286728947[/C][/ROW]
[ROW][C]-1.48010571706311[/C][/ROW]
[ROW][C]3.67475962718878[/C][/ROW]
[ROW][C]-13.2745130833936[/C][/ROW]
[ROW][C]-1.93263110681941[/C][/ROW]
[ROW][C]59.4498557344793[/C][/ROW]
[ROW][C]14.54529935208[/C][/ROW]
[ROW][C]9.53352475536461[/C][/ROW]
[ROW][C]-7.96307632931414[/C][/ROW]
[ROW][C]-7.0055828635666[/C][/ROW]
[ROW][C]-10.8217338764561[/C][/ROW]
[ROW][C]10.9355162240274[/C][/ROW]
[ROW][C]-3.40974124376879[/C][/ROW]
[ROW][C]8.44262868395734[/C][/ROW]
[ROW][C]-2.71510088932548[/C][/ROW]
[ROW][C]-19.5761487862528[/C][/ROW]
[ROW][C]2.48370747338826[/C][/ROW]
[ROW][C]-30.7376118390498[/C][/ROW]
[ROW][C]-0.24593005274799[/C][/ROW]
[ROW][C]-12.6036183983474[/C][/ROW]
[ROW][C]13.0655418378779[/C][/ROW]
[ROW][C]-0.394882403664419[/C][/ROW]
[ROW][C]7.96273471960752[/C][/ROW]
[ROW][C]-7.95866954989581[/C][/ROW]
[ROW][C]6.50834531047491[/C][/ROW]
[ROW][C]-1.1993414914655[/C][/ROW]
[ROW][C]7.2364369912092[/C][/ROW]
[ROW][C]-8.41020701682505[/C][/ROW]
[ROW][C]-6.4705221152908[/C][/ROW]
[ROW][C]-19.3681973735558[/C][/ROW]
[ROW][C]-5.99844681223413[/C][/ROW]
[ROW][C]-3.56808975519529[/C][/ROW]
[ROW][C]1.83708194802653[/C][/ROW]
[ROW][C]-6.49105412451408[/C][/ROW]
[ROW][C]4.27569929242438[/C][/ROW]
[ROW][C]0.176581717031863[/C][/ROW]
[ROW][C]0.931683882663412[/C][/ROW]
[ROW][C]-2.83678080386747[/C][/ROW]
[ROW][C]13.0280872459644[/C][/ROW]
[ROW][C]-1.97300291335701[/C][/ROW]
[ROW][C]13.5449551021224[/C][/ROW]
[ROW][C]-1.51427127864369[/C][/ROW]
[ROW][C]5.22460349660378[/C][/ROW]
[ROW][C]-10.4957374430768[/C][/ROW]
[ROW][C]-5.57196199920367[/C][/ROW]
[ROW][C]9.14272802952696[/C][/ROW]
[ROW][C]1.68296809078247[/C][/ROW]
[ROW][C]1.72107032703935[/C][/ROW]
[ROW][C]-5.255114107004[/C][/ROW]
[ROW][C]4.1926340487574[/C][/ROW]
[ROW][C]-7.38752212068854[/C][/ROW]
[ROW][C]-0.196343436480517[/C][/ROW]
[ROW][C]13.1694251248965[/C][/ROW]
[ROW][C]4.208125331191[/C][/ROW]
[ROW][C]-17.9931822041187[/C][/ROW]
[ROW][C]5.77045892798511[/C][/ROW]
[ROW][C]4.21529373815429[/C][/ROW]
[ROW][C]-6.57236109127688[/C][/ROW]
[ROW][C]3.53853146770071[/C][/ROW]
[ROW][C]0.0209724592091487[/C][/ROW]
[ROW][C]15.479668645339[/C][/ROW]
[ROW][C]-9.43214900200741[/C][/ROW]
[ROW][C]-10.7557611966552[/C][/ROW]
[ROW][C]-12.6638939302477[/C][/ROW]
[ROW][C]4.92936823899019[/C][/ROW]
[ROW][C]0.176147331601175[/C][/ROW]
[ROW][C]11.4396162070719[/C][/ROW]
[ROW][C]10.099938666455[/C][/ROW]
[ROW][C]-3.79506136842779[/C][/ROW]
[ROW][C]3.97344712683543[/C][/ROW]
[ROW][C]12.9419378505757[/C][/ROW]
[ROW][C]19.2011035136113[/C][/ROW]
[ROW][C]-27.5776055507858[/C][/ROW]
[ROW][C]3.18639778668805[/C][/ROW]
[ROW][C]5.15805711107089[/C][/ROW]
[ROW][C]5.03607890189556[/C][/ROW]
[ROW][C]-1.56800497607954[/C][/ROW]
[ROW][C]13.56315516472[/C][/ROW]
[ROW][C]-5.30347979134711[/C][/ROW]
[ROW][C]13.3273440141973[/C][/ROW]
[ROW][C]-12.7357454042206[/C][/ROW]
[ROW][C]15.1361185855331[/C][/ROW]
[ROW][C]7.60784052410023[/C][/ROW]
[ROW][C]-22.9867742097005[/C][/ROW]
[ROW][C]-7.53103532491062[/C][/ROW]
[ROW][C]0.017227228447983[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=151163&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=151163&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
1.30449222496613
8.37733252482086
-5.07717354846436
4.54703440013222
-3.36292258471267
10.7957782587327
1.52662986391075
-0.506543863839457
5.21758748152724
-10.2203559400822
-0.939699794509995
-6.0391882921763
-13.7671139474289
5.28048319677654
3.49388268896546
2.72191509831473
2.99262684179177
23.6064286976126
1.63303690747063
-2.47821625923261
7.29051911317096
4.04998070324525
2.90938435102005
-3.53818590150523
-12.0890326081821
-8.41456320080955
9.17321156310524
0.827402809765435
1.46203909246844
-15.1635096980948
6.10975152208571
-6.68669001523063
-4.12471968460813
-1.76218252210644
-0.0153394046583145
3.33600113967315
10.4249429749972
5.18299383918814
-6.30375581754534
2.15372485755015
-4.19397601274833
-15.6916162211301
0.483082341588017
1.65593114456682
-8.60512360644606
22.0750850249745
-5.25156297956065
-9.9939865006534
1.05101571561197
22.3047956473081
-3.21892994411296
-3.78102147343071
-9.4199388192435
-3.30647120750177
1.65695097755448
8.36925587399258
-0.838929721161382
7.65444014505488
-10.5536942239347
-10.7918503904023
-1.07044527903989
1.55575784650546
2.87783284977922
-9.63958189221061
-5.39835510692419
-13.3757216086743
-10.1423178383325
-0.306772522692924
14.7316309905013
21.4308387831418
6.171457223669
2.78725357328225
-5.19393949815657
-4.48401458762278
-1.15633916104445
-19.1807050028569
9.56601936814616
31.8088876737766
-18.8852574954713
4.02153989338955
4.81844060010854
-2.97593769101074
-5.85840715319092
-3.95152234956804
6.73968786633743
0.419831815830935
8.44007691581977
-20.7376286728947
-1.48010571706311
3.67475962718878
-13.2745130833936
-1.93263110681941
59.4498557344793
14.54529935208
9.53352475536461
-7.96307632931414
-7.0055828635666
-10.8217338764561
10.9355162240274
-3.40974124376879
8.44262868395734
-2.71510088932548
-19.5761487862528
2.48370747338826
-30.7376118390498
-0.24593005274799
-12.6036183983474
13.0655418378779
-0.394882403664419
7.96273471960752
-7.95866954989581
6.50834531047491
-1.1993414914655
7.2364369912092
-8.41020701682505
-6.4705221152908
-19.3681973735558
-5.99844681223413
-3.56808975519529
1.83708194802653
-6.49105412451408
4.27569929242438
0.176581717031863
0.931683882663412
-2.83678080386747
13.0280872459644
-1.97300291335701
13.5449551021224
-1.51427127864369
5.22460349660378
-10.4957374430768
-5.57196199920367
9.14272802952696
1.68296809078247
1.72107032703935
-5.255114107004
4.1926340487574
-7.38752212068854
-0.196343436480517
13.1694251248965
4.208125331191
-17.9931822041187
5.77045892798511
4.21529373815429
-6.57236109127688
3.53853146770071
0.0209724592091487
15.479668645339
-9.43214900200741
-10.7557611966552
-12.6638939302477
4.92936823899019
0.176147331601175
11.4396162070719
10.099938666455
-3.79506136842779
3.97344712683543
12.9419378505757
19.2011035136113
-27.5776055507858
3.18639778668805
5.15805711107089
5.03607890189556
-1.56800497607954
13.56315516472
-5.30347979134711
13.3273440141973
-12.7357454042206
15.1361185855331
7.60784052410023
-22.9867742097005
-7.53103532491062
0.017227228447983



Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 2 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; par3 = 2 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
R code (references can be found in the software module):
library(lattice)
if (par1 == 'TRUE') par1 <- TRUE
if (par1 == 'FALSE') par1 <- FALSE
par2 <- as.numeric(par2) #Box-Cox lambda transformation parameter
par3 <- as.numeric(par3) #degree of non-seasonal differencing
par4 <- as.numeric(par4) #degree of seasonal differencing
par5 <- as.numeric(par5) #seasonal period
par6 <- as.numeric(par6) #degree (p) of the non-seasonal AR(p) polynomial
par7 <- as.numeric(par7) #degree (q) of the non-seasonal MA(q) polynomial
par8 <- as.numeric(par8) #degree (P) of the seasonal AR(P) polynomial
par9 <- as.numeric(par9) #degree (Q) of the seasonal MA(Q) polynomial
armaGR <- function(arima.out, names, n){
try1 <- arima.out$coef
try2 <- sqrt(diag(arima.out$var.coef))
try.data.frame <- data.frame(matrix(NA,ncol=4,nrow=length(names)))
dimnames(try.data.frame) <- list(names,c('coef','std','tstat','pv'))
try.data.frame[,1] <- try1
for(i in 1:length(try2)) try.data.frame[which(rownames(try.data.frame)==names(try2)[i]),2] <- try2[i]
try.data.frame[,3] <- try.data.frame[,1] / try.data.frame[,2]
try.data.frame[,4] <- round((1-pt(abs(try.data.frame[,3]),df=n-(length(try2)+1)))*2,5)
vector <- rep(NA,length(names))
vector[is.na(try.data.frame[,4])] <- 0
maxi <- which.max(try.data.frame[,4])
continue <- max(try.data.frame[,4],na.rm=TRUE) > .05
vector[maxi] <- 0
list(summary=try.data.frame,next.vector=vector,continue=continue)
}
arimaSelect <- function(series, order=c(13,0,0), seasonal=list(order=c(2,0,0),period=12), include.mean=F){
nrc <- order[1]+order[3]+seasonal$order[1]+seasonal$order[3]
coeff <- matrix(NA, nrow=nrc*2, ncol=nrc)
pval <- matrix(NA, nrow=nrc*2, ncol=nrc)
mylist <- rep(list(NULL), nrc)
names <- NULL
if(order[1] > 0) names <- paste('ar',1:order[1],sep='')
if(order[3] > 0) names <- c( names , paste('ma',1:order[3],sep='') )
if(seasonal$order[1] > 0) names <- c(names, paste('sar',1:seasonal$order[1],sep=''))
if(seasonal$order[3] > 0) names <- c(names, paste('sma',1:seasonal$order[3],sep=''))
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML')
mylist[[1]] <- arima.out
last.arma <- armaGR(arima.out, names, length(series))
mystop <- FALSE
i <- 1
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- 2
aic <- arima.out$aic
while(!mystop){
mylist[[i]] <- arima.out
arima.out <- arima(series, order=order, seasonal=seasonal, include.mean=include.mean, method='ML', fixed=last.arma$next.vector)
aic <- c(aic, arima.out$aic)
last.arma <- armaGR(arima.out, names, length(series))
mystop <- !last.arma$continue
coeff[i,] <- last.arma[[1]][,1]
pval [i,] <- last.arma[[1]][,4]
i <- i+1
}
list(coeff, pval, mylist, aic=aic)
}
arimaSelectplot <- function(arimaSelect.out,noms,choix){
noms <- names(arimaSelect.out[[3]][[1]]$coef)
coeff <- arimaSelect.out[[1]]
k <- min(which(is.na(coeff[,1])))-1
coeff <- coeff[1:k,]
pval <- arimaSelect.out[[2]][1:k,]
aic <- arimaSelect.out$aic[1:k]
coeff[coeff==0] <- NA
n <- ncol(coeff)
if(missing(choix)) choix <- k
layout(matrix(c(1,1,1,2,
3,3,3,2,
3,3,3,4,
5,6,7,7),nr=4),
widths=c(10,35,45,15),
heights=c(30,30,15,15))
couleurs <- rainbow(75)[1:50]#(50)
ticks <- pretty(coeff)
par(mar=c(1,1,3,1))
plot(aic,k:1-.5,type='o',pch=21,bg='blue',cex=2,axes=F,lty=2,xpd=NA)
points(aic[choix],k-choix+.5,pch=21,cex=4,bg=2,xpd=NA)
title('aic',line=2)
par(mar=c(3,0,0,0))
plot(0,axes=F,xlab='',ylab='',xlim=range(ticks),ylim=c(.1,1))
rect(xleft = min(ticks) + (0:49)/50*(max(ticks)-min(ticks)),
xright = min(ticks) + (1:50)/50*(max(ticks)-min(ticks)),
ytop = rep(1,50),
ybottom= rep(0,50),col=couleurs,border=NA)
axis(1,ticks)
rect(xleft=min(ticks),xright=max(ticks),ytop=1,ybottom=0)
text(mean(coeff,na.rm=T),.5,'coefficients',cex=2,font=2)
par(mar=c(1,1,3,1))
image(1:n,1:k,t(coeff[k:1,]),axes=F,col=couleurs,zlim=range(ticks))
for(i in 1:n) for(j in 1:k) if(!is.na(coeff[j,i])) {
if(pval[j,i]<.01) symb = 'green'
else if( (pval[j,i]<.05) & (pval[j,i]>=.01)) symb = 'orange'
else if( (pval[j,i]<.1) & (pval[j,i]>=.05)) symb = 'red'
else symb = 'black'
polygon(c(i+.5 ,i+.2 ,i+.5 ,i+.5),
c(k-j+0.5,k-j+0.5,k-j+0.8,k-j+0.5),
col=symb)
if(j==choix) {
rect(xleft=i-.5,
xright=i+.5,
ybottom=k-j+1.5,
ytop=k-j+.5,
lwd=4)
text(i,
k-j+1,
round(coeff[j,i],2),
cex=1.2,
font=2)
}
else{
rect(xleft=i-.5,xright=i+.5,ybottom=k-j+1.5,ytop=k-j+.5)
text(i,k-j+1,round(coeff[j,i],2),cex=1.2,font=1)
}
}
axis(3,1:n,noms)
par(mar=c(0.5,0,0,0.5))
plot(0,axes=F,xlab='',ylab='',type='n',xlim=c(0,8),ylim=c(-.2,.8))
cols <- c('green','orange','red','black')
niv <- c('0','0.01','0.05','0.1')
for(i in 0:3){
polygon(c(1+2*i ,1+2*i ,1+2*i-.5 ,1+2*i),
c(.4 ,.7 , .4 , .4),
col=cols[i+1])
text(2*i,0.5,niv[i+1],cex=1.5)
}
text(8,.5,1,cex=1.5)
text(4,0,'p-value',cex=2)
box()
residus <- arimaSelect.out[[3]][[choix]]$res
par(mar=c(1,2,4,1))
acf(residus,main='')
title('acf',line=.5)
par(mar=c(1,2,4,1))
pacf(residus,main='')
title('pacf',line=.5)
par(mar=c(2,2,4,1))
qqnorm(residus,main='')
title('qq-norm',line=.5)
qqline(residus)
residus
}
if (par2 == 0) x <- log(x)
if (par2 != 0) x <- x^par2
(selection <- arimaSelect(x, order=c(par6,par3,par7), seasonal=list(order=c(par8,par4,par9), period=par5)))
bitmap(file='test1.png')
resid <- arimaSelectplot(selection)
dev.off()
resid
bitmap(file='test2.png')
acf(resid,length(resid)/2, main='Residual Autocorrelation Function')
dev.off()
bitmap(file='test3.png')
pacf(resid,length(resid)/2, main='Residual Partial Autocorrelation Function')
dev.off()
bitmap(file='test4.png')
cpgram(resid, main='Residual Cumulative Periodogram')
dev.off()
bitmap(file='test5.png')
hist(resid, main='Residual Histogram', xlab='values of Residuals')
dev.off()
bitmap(file='test6.png')
densityplot(~resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test7.png')
qqnorm(resid, main='Residual Normal Q-Q Plot')
qqline(resid)
dev.off()
ncols <- length(selection[[1]][1,])
nrows <- length(selection[[2]][,1])-1
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ARIMA Parameter Estimation and Backward Selection', ncols+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Iteration', header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,names(selection[[3]][[1]]$coef)[i],header=TRUE)
}
a<-table.row.end(a)
for (j in 1:nrows) {
a<-table.row.start(a)
mydum <- 'Estimates ('
mydum <- paste(mydum,j)
mydum <- paste(mydum,')')
a<-table.element(a,mydum, header=TRUE)
for (i in 1:ncols) {
a<-table.element(a,round(selection[[1]][j,i],4))
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(p-val)', header=TRUE)
for (i in 1:ncols) {
mydum <- '('
mydum <- paste(mydum,round(selection[[2]][j,i],4),sep='')
mydum <- paste(mydum,')')
a<-table.element(a,mydum)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Estimated ARIMA Residuals', 1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Value', 1,TRUE)
a<-table.row.end(a)
for (i in (par4*par5+par3):length(resid)) {
a<-table.row.start(a)
a<-table.element(a,resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')