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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_arimabackwardselection.wasp

Title produced by software

ARIMA Backward Selection

Date of computation

Sun, 07 Dec 2008 06:50:27 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/07/t1228658006v9c21zwg7k7bxr8.htm/, Retrieved Sun, 19 May 2024 10:06:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=29985, Retrieved Sun, 19 May 2024 10:06:23 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

207

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Identification an...] [2008-12-07 12:51:02] [b943bd7078334192ff8343563ee31113]
- RM      [Variance Reduction Matrix] [Identification an...] [2008-12-07 12:57:34] [b943bd7078334192ff8343563ee31113]
- RMP       [(Partial) Autocorrelation Function] [Identification an...] [2008-12-07 13:03:39] [b943bd7078334192ff8343563ee31113]
-   P         [(Partial) Autocorrelation Function] [Identification an...] [2008-12-07 13:12:12] [b943bd7078334192ff8343563ee31113]
-   P           [(Partial) Autocorrelation Function] [Identification an...] [2008-12-07 13:13:52] [b943bd7078334192ff8343563ee31113]
- RMP             [Spectral Analysis] [Identification an...] [2008-12-07 13:17:22] [b943bd7078334192ff8343563ee31113]
- RMP               [(Partial) Autocorrelation Function] [Identification an...] [2008-12-07 13:23:16] [b943bd7078334192ff8343563ee31113]
- RMP                   [ARIMA Backward Selection] [Identification an...] [2008-12-07 13:50:27] [620b6ad5c4696049e39cb73ce029682c] [Current]

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	20 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 20 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29985&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]20 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29985&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29985&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	20 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ma1	sar1	sar2	sma1
Estimates ( 1 )	0.4867	0.1754	-0.3974	-0.1005	-0.0616	-0.6417
(p-val)	(0.0053 )	(0.0103 )	(0.0223 )	(0.3733 )	(0.4942 )	(0 )
Estimates ( 2 )	0.4706	0.1836	-0.3842	-0.0462	0	-0.6958
(p-val)	(0.0074 )	(0.0062 )	(0.0293 )	(0.5533 )	(NA )	(0 )
Estimates ( 3 )	0.4617	0.1882	-0.3767	0	0	-0.7209
(p-val)	(0.0078 )	(0.0044 )	(0.0307 )	(NA )	(NA )	(0 )
Estimates ( 4 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 5 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ma1 & sar1 & sar2 & sma1 \tabularnewline
Estimates ( 1 ) & 0.4867 & 0.1754 & -0.3974 & -0.1005 & -0.0616 & -0.6417 \tabularnewline
(p-val) & (0.0053 ) & (0.0103 ) & (0.0223 ) & (0.3733 ) & (0.4942 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.4706 & 0.1836 & -0.3842 & -0.0462 & 0 & -0.6958 \tabularnewline
(p-val) & (0.0074 ) & (0.0062 ) & (0.0293 ) & (0.5533 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.4617 & 0.1882 & -0.3767 & 0 & 0 & -0.7209 \tabularnewline
(p-val) & (0.0078 ) & (0.0044 ) & (0.0307 ) & (NA ) & (NA ) & (0 ) \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=29985&T=1

[TABLE]
[ROW][C]ARIMA Parameter Estimation and Backward Selection[/C][/ROW]
[ROW][C]Iteration[/C][C]ar1[/C][C]ar2[/C][C]ma1[/C][C]sar1[/C][C]sar2[/C][C]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]0.4867[/C][C]0.1754[/C][C]-0.3974[/C][C]-0.1005[/C][C]-0.0616[/C][C]-0.6417[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0053 )[/C][C](0.0103 )[/C][C](0.0223 )[/C][C](0.3733 )[/C][C](0.4942 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4706[/C][C]0.1836[/C][C]-0.3842[/C][C]-0.0462[/C][C]0[/C][C]-0.6958[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0074 )[/C][C](0.0062 )[/C][C](0.0293 )[/C][C](0.5533 )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4617[/C][C]0.1882[/C][C]-0.3767[/C][C]0[/C][C]0[/C][C]-0.7209[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0078 )[/C][C](0.0044 )[/C][C](0.0307 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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=29985&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29985&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
Iteration	ar1	ar2	ma1	sar1	sar2	sma1
Estimates ( 1 )	0.4867	0.1754	-0.3974	-0.1005	-0.0616	-0.6417
(p-val)	(0.0053 )	(0.0103 )	(0.0223 )	(0.3733 )	(0.4942 )	(0 )
Estimates ( 2 )	0.4706	0.1836	-0.3842	-0.0462	0	-0.6958
(p-val)	(0.0074 )	(0.0062 )	(0.0293 )	(0.5533 )	(NA )	(0 )
Estimates ( 3 )	0.4617	0.1882	-0.3767	0	0	-0.7209
(p-val)	(0.0078 )	(0.0044 )	(0.0307 )	(NA )	(NA )	(0 )
Estimates ( 4 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 5 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )

Estimated ARIMA Residuals
Value
-0.0447135254057289
-0.0681917505762537
0.197918269651678
0.364977510620088
1.51337996999665
-0.359461693691976
0.45727523041751
-0.57620043294824
-0.358061446232802
1.26000912123171
-1.34266139976322
-0.35328957358835
0.161395642932500
-0.857823002237743
-0.555144912906632
-0.727027912896176
-0.369042263797863
-0.0578971494258523
-0.769036819512976
-0.853703554717786
0.700854566656408
-0.676058768164262
0.868578036299269
-0.108845889031216
-1.57887204378987
-1.12268950419340
0.396923853622347
0.0148495716985337
0.140259354146611
0.369865090017400
-0.279776100957448
0.304546129374559
0.89246630132279
0.0893800849901583
0.135269390964401
-1.20736758916146
-0.230372956324712
-0.0877971467358163
-0.333232968700083
0.601966274292883
0.489790332225651
-0.300400374290469
0.101351037261532
0.596346949507982
-0.476838868670803
-0.418379378139474
-0.117113003128320
-0.147180752640127
0.532293993176542
-0.97652836136989
0.331179682229465
0.869619341814696
-0.452578488640548
-0.421389495081562
-0.0683600582409602
0.326102643389245
0.848949465983035
0.455827856174843
0.823473299799597
0.933736134483959
1.23343911343583
0.408339889327737
0.287976013648022
-0.211203399262392
-0.26746888448263
-1.11645429746006
-0.0317247391805415
0.547972214615268
0.120802504579993
-0.868321572899463
-0.311155682318071
-0.380689103458596
-0.305618645165434
-0.409940940629235
-0.00967793818094487
0.495426346317262
-0.704347377782621
-0.0634698799077804
-0.513898813095963
0.589511460211348
-0.349423191514948
0.641305316970871
0.0343658048066296
-0.0527521707847403
-0.880138086064973
-0.110617811790144
0.72501756925047
-0.50521028974349
1.01506155671780
0.43216548799774
-0.605966485791567
-0.99965759053113
-0.27389849284345
0.276038635212532
1.00336898159556
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-0.560227111415777
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0.279628860545686
0.678638847881453
0.661069131975032
-0.70445040046804
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0.350125399527117
0.512074040489585
0.85520690566602
0.194069541129319
0.723929039190279
1.18131067466569
0.142881529418706
0.0179257995250461
-0.497956663834366
-0.299719686708145
0.13578826718012
-0.170331261322571
-1.03309848526185
-0.173819270981208
-0.963024087494654
0.698379738338191
-0.367089112099373
-0.263848480521371
-0.418819019705775
-1.12067684130346
0.0500585218566849
0.608903579961013
0.134690290705067
0.330041346251133
0.127838693452427
0.581319133189022
-0.0387079404231407
-0.87009719588871
-0.462113796209216
-0.775456512938392
1.39586946858571
-0.373271729156736
-0.268080178495553
1.08656232020876
-0.325879956625943
0.30661854580103
-0.552774193024615
0.92873551812778
0.0930554081651027
0.793725486232807
-0.065519604981355
0.319389737562044
-0.466224915802782
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0.0101377315170772
0.166709542735312
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-0.69851227236935
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-0.218027304003678
-0.373584444409826
0.0529514291064552
0.162726895775348
0.827781137704626
-0.72432587114029
-0.470004758887977
1.04570491894587
-0.192863385043723
-0.571180241566901
0.531291141800283
-0.305718225774085
0.338050047265944
0.476242123002142
-0.813898476177581
-0.0347526277053308
0.307275272829426
0.298265456198981
-0.356205548254038
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0.161914373981577
0.157622109196245
0.301928817597439
-0.561981939197509
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-0.0291188115684367
0.228470615147599
-0.510675390948568
1.12387561836619
-1.12598799925206
0.290323759940524
0.190186117926447
0.0853119352073367
-0.708038984184211
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-0.304012118525856
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0.581469268648653
0.330213799588304
-0.98336658128738
-0.736262987401372
-0.338744405918134
0.760537197961208
-0.261709569689877
-0.476048054381912
-0.110367749140534
1.69080994499436
0.149871961184005
-0.89444396082796
0.0854707851146147
-0.117916014199087
-0.215892867221059
-0.394092027714760
0.0924263193372724
0.0585916693359945
0.253245870903461
0.370568029535024
-0.386523946052773
0.44711088695323
0.623154287912755
-0.18420312632899
0.705478214447731
-0.317030723051987
-0.927475249957395
-0.0393758871207126
1.00274756979778
0.8126192957394
0.298293414246215
0.152551034783924
-0.150639291031303
0.110741932620983
0.552786542442701
0.0434953166201636
0.363209762301508
-0.0227970072986597
0.504138677494819
0.138779288758692
-0.0853035322320448
-0.496484911004379
-0.085225190932821
-0.24783097123531
-0.120849214574782
-0.451770221741425
0.61215046254869
0.350625300773721
-0.359290488749134
-0.484436529749283
0.339430849683981
-0.0417944824981891
-0.0100068963511211
-0.403912573207126
0.151831208256742
-0.229701002747648
-0.217361644447954
-0.332188934008454
0.264417994333332
0.176869375080399
-0.176677799133906
-0.135670367287513
-0.827944201456603
-0.0722734406226822
-0.117607844675079
0.314635053990593
-0.154386181254541
0.163002765274961
-0.244739216048874
-0.204481561414473
0.0360259322778522
0.00417168086710988
0.266111866725257
-0.651003115382985
0.589994534633064
0.312322956124845
0.552669928507138
-0.0962287495308535
-0.467083613082158
-0.198196785450764
0.395575343853932
0.175735235035432
0.315653623024304
-0.161149078355707
0.882040919078068
0.0637848770922342
0.858633849500186
0.822761597335615
1.77266886640136
-0.56698109721755
0.15326167536922
-0.279232928037539
0.0495720501580029
-1.16973330228155
-0.0821094014825214
0.0681021373590283
-0.201544972568768
0.0764370026554621
-0.526360143767313
-0.080413993420527
-0.390603734575086
-0.366201530236125
-0.236325628269532
-0.0197748909456996
-0.400593231081995
0.273305314146634
0.571670834760174
0.355107021718436
-0.598053246840853
0.0680124300154832
0.082515772868584
-0.236688908168786
-0.720993032671946
0.445855526588522
-0.278184398397156
-0.813695707628466
0.0382223636610357
0.287981461814532
-0.397401294466808
0.453441799021202
-0.336993630230210
0.0350016886845095
-0.129074066055286
-0.929487248631519
0.112538314921928
-0.266214840019551
0.318919352864553
-0.235823743455037
0.312056036056965
-0.607348900611906
0.821439553663096
-0.330769416059351
-0.0368470908816096
-0.223411512677555
-0.0162393529440892
0.494328755143258

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.0447135254057289 \tabularnewline
-0.0681917505762537 \tabularnewline
0.197918269651678 \tabularnewline
0.364977510620088 \tabularnewline
1.51337996999665 \tabularnewline
-0.359461693691976 \tabularnewline
0.45727523041751 \tabularnewline
-0.57620043294824 \tabularnewline
-0.358061446232802 \tabularnewline
1.26000912123171 \tabularnewline
-1.34266139976322 \tabularnewline
-0.35328957358835 \tabularnewline
0.161395642932500 \tabularnewline
-0.857823002237743 \tabularnewline
-0.555144912906632 \tabularnewline
-0.727027912896176 \tabularnewline
-0.369042263797863 \tabularnewline
-0.0578971494258523 \tabularnewline
-0.769036819512976 \tabularnewline
-0.853703554717786 \tabularnewline
0.700854566656408 \tabularnewline
-0.676058768164262 \tabularnewline
0.868578036299269 \tabularnewline
-0.108845889031216 \tabularnewline
-1.57887204378987 \tabularnewline
-1.12268950419340 \tabularnewline
0.396923853622347 \tabularnewline
0.0148495716985337 \tabularnewline
0.140259354146611 \tabularnewline
0.369865090017400 \tabularnewline
-0.279776100957448 \tabularnewline
0.304546129374559 \tabularnewline
0.89246630132279 \tabularnewline
0.0893800849901583 \tabularnewline
0.135269390964401 \tabularnewline
-1.20736758916146 \tabularnewline
-0.230372956324712 \tabularnewline
-0.0877971467358163 \tabularnewline
-0.333232968700083 \tabularnewline
0.601966274292883 \tabularnewline
0.489790332225651 \tabularnewline
-0.300400374290469 \tabularnewline
0.101351037261532 \tabularnewline
0.596346949507982 \tabularnewline
-0.476838868670803 \tabularnewline
-0.418379378139474 \tabularnewline
-0.117113003128320 \tabularnewline
-0.147180752640127 \tabularnewline
0.532293993176542 \tabularnewline
-0.97652836136989 \tabularnewline
0.331179682229465 \tabularnewline
0.869619341814696 \tabularnewline
-0.452578488640548 \tabularnewline
-0.421389495081562 \tabularnewline
-0.0683600582409602 \tabularnewline
0.326102643389245 \tabularnewline
0.848949465983035 \tabularnewline
0.455827856174843 \tabularnewline
0.823473299799597 \tabularnewline
0.933736134483959 \tabularnewline
1.23343911343583 \tabularnewline
0.408339889327737 \tabularnewline
0.287976013648022 \tabularnewline
-0.211203399262392 \tabularnewline
-0.26746888448263 \tabularnewline
-1.11645429746006 \tabularnewline
-0.0317247391805415 \tabularnewline
0.547972214615268 \tabularnewline
0.120802504579993 \tabularnewline
-0.868321572899463 \tabularnewline
-0.311155682318071 \tabularnewline
-0.380689103458596 \tabularnewline
-0.305618645165434 \tabularnewline
-0.409940940629235 \tabularnewline
-0.00967793818094487 \tabularnewline
0.495426346317262 \tabularnewline
-0.704347377782621 \tabularnewline
-0.0634698799077804 \tabularnewline
-0.513898813095963 \tabularnewline
0.589511460211348 \tabularnewline
-0.349423191514948 \tabularnewline
0.641305316970871 \tabularnewline
0.0343658048066296 \tabularnewline
-0.0527521707847403 \tabularnewline
-0.880138086064973 \tabularnewline
-0.110617811790144 \tabularnewline
0.72501756925047 \tabularnewline
-0.50521028974349 \tabularnewline
1.01506155671780 \tabularnewline
0.43216548799774 \tabularnewline
-0.605966485791567 \tabularnewline
-0.99965759053113 \tabularnewline
-0.27389849284345 \tabularnewline
0.276038635212532 \tabularnewline
1.00336898159556 \tabularnewline
0.0129978230730709 \tabularnewline
-0.560227111415777 \tabularnewline
-0.5497042226285 \tabularnewline
-0.264710628647504 \tabularnewline
0.279628860545686 \tabularnewline
0.678638847881453 \tabularnewline
0.661069131975032 \tabularnewline
-0.70445040046804 \tabularnewline
-0.201260458558037 \tabularnewline
0.350125399527117 \tabularnewline
0.512074040489585 \tabularnewline
0.85520690566602 \tabularnewline
0.194069541129319 \tabularnewline
0.723929039190279 \tabularnewline
1.18131067466569 \tabularnewline
0.142881529418706 \tabularnewline
0.0179257995250461 \tabularnewline
-0.497956663834366 \tabularnewline
-0.299719686708145 \tabularnewline
0.13578826718012 \tabularnewline
-0.170331261322571 \tabularnewline
-1.03309848526185 \tabularnewline
-0.173819270981208 \tabularnewline
-0.963024087494654 \tabularnewline
0.698379738338191 \tabularnewline
-0.367089112099373 \tabularnewline
-0.263848480521371 \tabularnewline
-0.418819019705775 \tabularnewline
-1.12067684130346 \tabularnewline
0.0500585218566849 \tabularnewline
0.608903579961013 \tabularnewline
0.134690290705067 \tabularnewline
0.330041346251133 \tabularnewline
0.127838693452427 \tabularnewline
0.581319133189022 \tabularnewline
-0.0387079404231407 \tabularnewline
-0.87009719588871 \tabularnewline
-0.462113796209216 \tabularnewline
-0.775456512938392 \tabularnewline
1.39586946858571 \tabularnewline
-0.373271729156736 \tabularnewline
-0.268080178495553 \tabularnewline
1.08656232020876 \tabularnewline
-0.325879956625943 \tabularnewline
0.30661854580103 \tabularnewline
-0.552774193024615 \tabularnewline
0.92873551812778 \tabularnewline
0.0930554081651027 \tabularnewline
0.793725486232807 \tabularnewline
-0.065519604981355 \tabularnewline
0.319389737562044 \tabularnewline
-0.466224915802782 \tabularnewline
-0.262872804051179 \tabularnewline
0.0101377315170772 \tabularnewline
0.166709542735312 \tabularnewline
-0.279488697112066 \tabularnewline
-0.416437753414019 \tabularnewline
-0.221155653856518 \tabularnewline
-0.180257931483894 \tabularnewline
-0.69851227236935 \tabularnewline
-0.0309661522809571 \tabularnewline
-0.218027304003678 \tabularnewline
-0.373584444409826 \tabularnewline
0.0529514291064552 \tabularnewline
0.162726895775348 \tabularnewline
0.827781137704626 \tabularnewline
-0.72432587114029 \tabularnewline
-0.470004758887977 \tabularnewline
1.04570491894587 \tabularnewline
-0.192863385043723 \tabularnewline
-0.571180241566901 \tabularnewline
0.531291141800283 \tabularnewline
-0.305718225774085 \tabularnewline
0.338050047265944 \tabularnewline
0.476242123002142 \tabularnewline
-0.813898476177581 \tabularnewline
-0.0347526277053308 \tabularnewline
0.307275272829426 \tabularnewline
0.298265456198981 \tabularnewline
-0.356205548254038 \tabularnewline
-0.356604249095821 \tabularnewline
0.161914373981577 \tabularnewline
0.157622109196245 \tabularnewline
0.301928817597439 \tabularnewline
-0.561981939197509 \tabularnewline
-0.0543444375476472 \tabularnewline
-0.242625454212677 \tabularnewline
-0.0291188115684367 \tabularnewline
0.228470615147599 \tabularnewline
-0.510675390948568 \tabularnewline
1.12387561836619 \tabularnewline
-1.12598799925206 \tabularnewline
0.290323759940524 \tabularnewline
0.190186117926447 \tabularnewline
0.0853119352073367 \tabularnewline
-0.708038984184211 \tabularnewline
0.0821216896507192 \tabularnewline
-0.307060855871161 \tabularnewline
0.525053351114722 \tabularnewline
-0.601608778992936 \tabularnewline
0.538181813805711 \tabularnewline
-0.306512068440087 \tabularnewline
0.654542042634459 \tabularnewline
-0.537374859105788 \tabularnewline
-0.186945155224672 \tabularnewline
-0.0415286501447794 \tabularnewline
-0.088263041293848 \tabularnewline
-0.256492308476563 \tabularnewline
-0.413059228131041 \tabularnewline
-0.270151685196506 \tabularnewline
-0.435615700881137 \tabularnewline
0.546649355706714 \tabularnewline
0.323884532911909 \tabularnewline
0.66193199947779 \tabularnewline
0.402774354187289 \tabularnewline
-0.428179653017036 \tabularnewline
-0.185363732362372 \tabularnewline
-0.146275809458303 \tabularnewline
0.177901303024011 \tabularnewline
-0.370503388015224 \tabularnewline
0.205238676749933 \tabularnewline
-0.0894107716874954 \tabularnewline
-0.0207802651967249 \tabularnewline
-0.00240156087411431 \tabularnewline
0.0272372596737925 \tabularnewline
-0.304012118525856 \tabularnewline
1.50787083809185 \tabularnewline
0.25904982248659 \tabularnewline
-0.546357646937622 \tabularnewline
0.581469268648653 \tabularnewline
0.330213799588304 \tabularnewline
-0.98336658128738 \tabularnewline
-0.736262987401372 \tabularnewline
-0.338744405918134 \tabularnewline
0.760537197961208 \tabularnewline
-0.261709569689877 \tabularnewline
-0.476048054381912 \tabularnewline
-0.110367749140534 \tabularnewline
1.69080994499436 \tabularnewline
0.149871961184005 \tabularnewline
-0.89444396082796 \tabularnewline
0.0854707851146147 \tabularnewline
-0.117916014199087 \tabularnewline
-0.215892867221059 \tabularnewline
-0.394092027714760 \tabularnewline
0.0924263193372724 \tabularnewline
0.0585916693359945 \tabularnewline
0.253245870903461 \tabularnewline
0.370568029535024 \tabularnewline
-0.386523946052773 \tabularnewline
0.44711088695323 \tabularnewline
0.623154287912755 \tabularnewline
-0.18420312632899 \tabularnewline
0.705478214447731 \tabularnewline
-0.317030723051987 \tabularnewline
-0.927475249957395 \tabularnewline
-0.0393758871207126 \tabularnewline
1.00274756979778 \tabularnewline
0.8126192957394 \tabularnewline
0.298293414246215 \tabularnewline
0.152551034783924 \tabularnewline
-0.150639291031303 \tabularnewline
0.110741932620983 \tabularnewline
0.552786542442701 \tabularnewline
0.0434953166201636 \tabularnewline
0.363209762301508 \tabularnewline
-0.0227970072986597 \tabularnewline
0.504138677494819 \tabularnewline
0.138779288758692 \tabularnewline
-0.0853035322320448 \tabularnewline
-0.496484911004379 \tabularnewline
-0.085225190932821 \tabularnewline
-0.24783097123531 \tabularnewline
-0.120849214574782 \tabularnewline
-0.451770221741425 \tabularnewline
0.61215046254869 \tabularnewline
0.350625300773721 \tabularnewline
-0.359290488749134 \tabularnewline
-0.484436529749283 \tabularnewline
0.339430849683981 \tabularnewline
-0.0417944824981891 \tabularnewline
-0.0100068963511211 \tabularnewline
-0.403912573207126 \tabularnewline
0.151831208256742 \tabularnewline
-0.229701002747648 \tabularnewline
-0.217361644447954 \tabularnewline
-0.332188934008454 \tabularnewline
0.264417994333332 \tabularnewline
0.176869375080399 \tabularnewline
-0.176677799133906 \tabularnewline
-0.135670367287513 \tabularnewline
-0.827944201456603 \tabularnewline
-0.0722734406226822 \tabularnewline
-0.117607844675079 \tabularnewline
0.314635053990593 \tabularnewline
-0.154386181254541 \tabularnewline
0.163002765274961 \tabularnewline
-0.244739216048874 \tabularnewline
-0.204481561414473 \tabularnewline
0.0360259322778522 \tabularnewline
0.00417168086710988 \tabularnewline
0.266111866725257 \tabularnewline
-0.651003115382985 \tabularnewline
0.589994534633064 \tabularnewline
0.312322956124845 \tabularnewline
0.552669928507138 \tabularnewline
-0.0962287495308535 \tabularnewline
-0.467083613082158 \tabularnewline
-0.198196785450764 \tabularnewline
0.395575343853932 \tabularnewline
0.175735235035432 \tabularnewline
0.315653623024304 \tabularnewline
-0.161149078355707 \tabularnewline
0.882040919078068 \tabularnewline
0.0637848770922342 \tabularnewline
0.858633849500186 \tabularnewline
0.822761597335615 \tabularnewline
1.77266886640136 \tabularnewline
-0.56698109721755 \tabularnewline
0.15326167536922 \tabularnewline
-0.279232928037539 \tabularnewline
0.0495720501580029 \tabularnewline
-1.16973330228155 \tabularnewline
-0.0821094014825214 \tabularnewline
0.0681021373590283 \tabularnewline
-0.201544972568768 \tabularnewline
0.0764370026554621 \tabularnewline
-0.526360143767313 \tabularnewline
-0.080413993420527 \tabularnewline
-0.390603734575086 \tabularnewline
-0.366201530236125 \tabularnewline
-0.236325628269532 \tabularnewline
-0.0197748909456996 \tabularnewline
-0.400593231081995 \tabularnewline
0.273305314146634 \tabularnewline
0.571670834760174 \tabularnewline
0.355107021718436 \tabularnewline
-0.598053246840853 \tabularnewline
0.0680124300154832 \tabularnewline
0.082515772868584 \tabularnewline
-0.236688908168786 \tabularnewline
-0.720993032671946 \tabularnewline
0.445855526588522 \tabularnewline
-0.278184398397156 \tabularnewline
-0.813695707628466 \tabularnewline
0.0382223636610357 \tabularnewline
0.287981461814532 \tabularnewline
-0.397401294466808 \tabularnewline
0.453441799021202 \tabularnewline
-0.336993630230210 \tabularnewline
0.0350016886845095 \tabularnewline
-0.129074066055286 \tabularnewline
-0.929487248631519 \tabularnewline
0.112538314921928 \tabularnewline
-0.266214840019551 \tabularnewline
0.318919352864553 \tabularnewline
-0.235823743455037 \tabularnewline
0.312056036056965 \tabularnewline
-0.607348900611906 \tabularnewline
0.821439553663096 \tabularnewline
-0.330769416059351 \tabularnewline
-0.0368470908816096 \tabularnewline
-0.223411512677555 \tabularnewline
-0.0162393529440892 \tabularnewline
0.494328755143258 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=29985&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.0447135254057289[/C][/ROW]
[ROW][C]-0.0681917505762537[/C][/ROW]
[ROW][C]0.197918269651678[/C][/ROW]
[ROW][C]0.364977510620088[/C][/ROW]
[ROW][C]1.51337996999665[/C][/ROW]
[ROW][C]-0.359461693691976[/C][/ROW]
[ROW][C]0.45727523041751[/C][/ROW]
[ROW][C]-0.57620043294824[/C][/ROW]
[ROW][C]-0.358061446232802[/C][/ROW]
[ROW][C]1.26000912123171[/C][/ROW]
[ROW][C]-1.34266139976322[/C][/ROW]
[ROW][C]-0.35328957358835[/C][/ROW]
[ROW][C]0.161395642932500[/C][/ROW]
[ROW][C]-0.857823002237743[/C][/ROW]
[ROW][C]-0.555144912906632[/C][/ROW]
[ROW][C]-0.727027912896176[/C][/ROW]
[ROW][C]-0.369042263797863[/C][/ROW]
[ROW][C]-0.0578971494258523[/C][/ROW]
[ROW][C]-0.769036819512976[/C][/ROW]
[ROW][C]-0.853703554717786[/C][/ROW]
[ROW][C]0.700854566656408[/C][/ROW]
[ROW][C]-0.676058768164262[/C][/ROW]
[ROW][C]0.868578036299269[/C][/ROW]
[ROW][C]-0.108845889031216[/C][/ROW]
[ROW][C]-1.57887204378987[/C][/ROW]
[ROW][C]-1.12268950419340[/C][/ROW]
[ROW][C]0.396923853622347[/C][/ROW]
[ROW][C]0.0148495716985337[/C][/ROW]
[ROW][C]0.140259354146611[/C][/ROW]
[ROW][C]0.369865090017400[/C][/ROW]
[ROW][C]-0.279776100957448[/C][/ROW]
[ROW][C]0.304546129374559[/C][/ROW]
[ROW][C]0.89246630132279[/C][/ROW]
[ROW][C]0.0893800849901583[/C][/ROW]
[ROW][C]0.135269390964401[/C][/ROW]
[ROW][C]-1.20736758916146[/C][/ROW]
[ROW][C]-0.230372956324712[/C][/ROW]
[ROW][C]-0.0877971467358163[/C][/ROW]
[ROW][C]-0.333232968700083[/C][/ROW]
[ROW][C]0.601966274292883[/C][/ROW]
[ROW][C]0.489790332225651[/C][/ROW]
[ROW][C]-0.300400374290469[/C][/ROW]
[ROW][C]0.101351037261532[/C][/ROW]
[ROW][C]0.596346949507982[/C][/ROW]
[ROW][C]-0.476838868670803[/C][/ROW]
[ROW][C]-0.418379378139474[/C][/ROW]
[ROW][C]-0.117113003128320[/C][/ROW]
[ROW][C]-0.147180752640127[/C][/ROW]
[ROW][C]0.532293993176542[/C][/ROW]
[ROW][C]-0.97652836136989[/C][/ROW]
[ROW][C]0.331179682229465[/C][/ROW]
[ROW][C]0.869619341814696[/C][/ROW]
[ROW][C]-0.452578488640548[/C][/ROW]
[ROW][C]-0.421389495081562[/C][/ROW]
[ROW][C]-0.0683600582409602[/C][/ROW]
[ROW][C]0.326102643389245[/C][/ROW]
[ROW][C]0.848949465983035[/C][/ROW]
[ROW][C]0.455827856174843[/C][/ROW]
[ROW][C]0.823473299799597[/C][/ROW]
[ROW][C]0.933736134483959[/C][/ROW]
[ROW][C]1.23343911343583[/C][/ROW]
[ROW][C]0.408339889327737[/C][/ROW]
[ROW][C]0.287976013648022[/C][/ROW]
[ROW][C]-0.211203399262392[/C][/ROW]
[ROW][C]-0.26746888448263[/C][/ROW]
[ROW][C]-1.11645429746006[/C][/ROW]
[ROW][C]-0.0317247391805415[/C][/ROW]
[ROW][C]0.547972214615268[/C][/ROW]
[ROW][C]0.120802504579993[/C][/ROW]
[ROW][C]-0.868321572899463[/C][/ROW]
[ROW][C]-0.311155682318071[/C][/ROW]
[ROW][C]-0.380689103458596[/C][/ROW]
[ROW][C]-0.305618645165434[/C][/ROW]
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[ROW][C]-0.880138086064973[/C][/ROW]
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[ROW][C]0.72501756925047[/C][/ROW]
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[ROW][C]1.01506155671780[/C][/ROW]
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[ROW][C]1.00336898159556[/C][/ROW]
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[ROW][C]0.723929039190279[/C][/ROW]
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[ROW][C]0.698379738338191[/C][/ROW]
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[ROW][C]0.339430849683981[/C][/ROW]
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[ROW][C]-0.720993032671946[/C][/ROW]
[ROW][C]0.445855526588522[/C][/ROW]
[ROW][C]-0.278184398397156[/C][/ROW]
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[ROW][C]0.0350016886845095[/C][/ROW]
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[ROW][C]-0.929487248631519[/C][/ROW]
[ROW][C]0.112538314921928[/C][/ROW]
[ROW][C]-0.266214840019551[/C][/ROW]
[ROW][C]0.318919352864553[/C][/ROW]
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[ROW][C]-0.607348900611906[/C][/ROW]
[ROW][C]0.821439553663096[/C][/ROW]
[ROW][C]-0.330769416059351[/C][/ROW]
[ROW][C]-0.0368470908816096[/C][/ROW]
[ROW][C]-0.223411512677555[/C][/ROW]
[ROW][C]-0.0162393529440892[/C][/ROW]
[ROW][C]0.494328755143258[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=29985&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=29985&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Estimated ARIMA Residuals
Value
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0.197918269651678
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1.51337996999665
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0.161395642932500
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0.396923853622347
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0.140259354146611
0.369865090017400
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0.135269390964401
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0.601966274292883
0.489790332225651
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0.101351037261532
0.596346949507982
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0.869619341814696
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0.848949465983035
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0.823473299799597
0.933736134483959
1.23343911343583
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0.287976013648022
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1.39586946858571
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0.0529514291064552
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0.827781137704626
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1.04570491894587
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0.531291141800283
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0.338050047265944
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Figure 1

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Postscript link

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Parameters (Session):

par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 2 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code