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R Software Module

rwasp_arimabackwardselection.wasp

Title produced by software

ARIMA Backward Selection

Date of computation

Sat, 12 Dec 2009 04:22:52 -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/2009/Dec/12/t1260617055alkbnbnjsxep3ck.htm/, Retrieved Mon, 29 Apr 2024 15:37:30 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=66902, Retrieved Mon, 29 Apr 2024 15:37:30 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

WS10,arima1

Estimated Impact

112

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

-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
- RMPD    [ARIMA Backward Selection] [] [2009-12-12 11:22:52] [30f5b608e5a1bbbae86b1702c0071566] [Current]

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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	'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 & 20 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66902&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66902&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66902&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	'Gwilym Jenkins' @ 72.249.127.135

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ar3	ma1	sar1	sar2	sma1
Estimates ( 1 )	-0.289	0.2676	0.0213	0.7755	0.6638	0.0708	-0.6887
(p-val)	(0 )	(0 )	(0.6653 )	(0 )	(0 )	(0.6306 )	(0 )
Estimates ( 2 )	-0.3215	0.2804	0	0.81	0.8183	0.0639	-0.8438
(p-val)	(0.0457 )	(0.006 )	(NA )	(0 )	(0 )	(0.2781 )	(0 )
Estimates ( 3 )	-0.332	0.2999	0	0.8224	-0.9979	0	0.9891
(p-val)	(0.0191 )	(0.0012 )	(NA )	(0 )	(0 )	(NA )	(0 )
Estimates ( 4 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 5 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 12 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 13 )	NA	NA	NA	NA	NA	NA	NA
(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 ) & -0.289 & 0.2676 & 0.0213 & 0.7755 & 0.6638 & 0.0708 & -0.6887 \tabularnewline
(p-val) & (0 ) & (0 ) & (0.6653 ) & (0 ) & (0 ) & (0.6306 ) & (0 ) \tabularnewline
Estimates ( 2 ) & -0.3215 & 0.2804 & 0 & 0.81 & 0.8183 & 0.0639 & -0.8438 \tabularnewline
(p-val) & (0.0457 ) & (0.006 ) & (NA ) & (0 ) & (0 ) & (0.2781 ) & (0 ) \tabularnewline
Estimates ( 3 ) & -0.332 & 0.2999 & 0 & 0.8224 & -0.9979 & 0 & 0.9891 \tabularnewline
(p-val) & (0.0191 ) & (0.0012 ) & (NA ) & (0 ) & (0 ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \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=66902&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]-0.289[/C][C]0.2676[/C][C]0.0213[/C][C]0.7755[/C][C]0.6638[/C][C]0.0708[/C][C]-0.6887[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](0.6653 )[/C][C](0 )[/C][C](0 )[/C][C](0.6306 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.3215[/C][C]0.2804[/C][C]0[/C][C]0.81[/C][C]0.8183[/C][C]0.0639[/C][C]-0.8438[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0457 )[/C][C](0.006 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](0.2781 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.332[/C][C]0.2999[/C][C]0[/C][C]0.8224[/C][C]-0.9979[/C][C]0[/C][C]0.9891[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0191 )[/C][C](0.0012 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/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][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 ( 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=66902&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66902&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	ar3	ma1	sar1	sar2	sma1
Estimates ( 1 )	-0.289	0.2676	0.0213	0.7755	0.6638	0.0708	-0.6887
(p-val)	(0 )	(0 )	(0.6653 )	(0 )	(0 )	(0.6306 )	(0 )
Estimates ( 2 )	-0.3215	0.2804	0	0.81	0.8183	0.0639	-0.8438
(p-val)	(0.0457 )	(0.006 )	(NA )	(0 )	(0 )	(0.2781 )	(0 )
Estimates ( 3 )	-0.332	0.2999	0	0.8224	-0.9979	0	0.9891
(p-val)	(0.0191 )	(0.0012 )	(NA )	(0 )	(0 )	(NA )	(0 )
Estimates ( 4 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 5 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 6 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 7 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 8 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 9 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 10 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 11 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 12 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )
Estimates ( 13 )	NA	NA	NA	NA	NA	NA	NA
(p-val)	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )	(NA )

Estimated ARIMA Residuals
Value
0.254999837514634
22.3226561141259
8.4237121927371
31.4840967891282
16.5161163829024
6.10629223042067
-12.7918432975015
-3.8888687922972
-4.5147400268326
-2.49899167140276
-9.65172800263859
0.630780336137417
-4.29202525286443
0.144093452694747
-1.80994365970992
-9.54016368517427
0.0709092051393568
-6.60695825215738
-1.44072814974947
-13.2867838967999
-1.04914047601546
-2.20174739282080
1.48095864724017
-2.08000212982048
-3.50015185059558
-3.61431620744694
-5.35375790479499
-2.28735695644625
-1.35003115372179
5.10303935720208
20.0833965881239
16.4033902060373
-0.0212652321290263
1.14865841876686
2.37164085497488
0.260574971410166
-13.0376189881084
10.7361767260307
-11.5669480927834
-0.786199330695074
1.61312176254781
-1.80558096741588
8.18005223910157
-4.93724873583824
-8.78531748551356
-1.04392609510290
-11.2962438947210
-3.18048133791269
-0.302304985521730
-14.1610036518591
1.45262072957604
-1.66493215893700
-1.40312765594212
1.44044165697868
-3.94174676213769
-0.257042482620874
-0.462315180358159
1.01225225939854
-3.7239133363085
10.6454102952348
-6.15682156664977
12.4414513125185
-5.28648924851091
1.66226586866823
-2.05037777282935
-0.537310887205521
-3.97973775485266
3.65961394085734
-2.43056190259458
-0.516016129310074
-0.161820894523965
1.78860855446208
1.27931309839162
1.35584501959290
-0.521915300599316
-4.20548225481622
0.919568715268673
-1.02446830651914
-1.22627907305345
2.14453507119079
-2.47221940864232
2.29255357486522
2.19054966368731
-3.28711713368396
6.12371463600336
2.41261011582007
-3.66397984638235
1.64802758920126
0.23925091815541
3.13555609634777
0.29962961369995
-1.30597505012642
1.95748824814583
0.476178838572573
-0.799517469300704
1.10947279764473
0.0442829008006028
1.60979309656664
-0.355706999776107
-0.747608782537832
-0.953871088989577
1.05702558257425
-0.270478963253514
-5.25857208708511
1.82064767825495
1.20474253524657
-0.98644112184596
-7.4837703860611
24.2434463672049
72.4526493311843
-16.960601635877
-9.81037132629332
-4.35922956655178
0.609716438537836
-7.99574687712668
-8.74106937035226
-14.4716529247682
7.89289529025737
2.55704276009891
-1.12267051719415
-2.90057413222877
-4.58190220187591
-2.53363606566828
-7.26897611583327
-6.00705181747068
2.64109706329254
-3.26101731942921
-7.92632724347435
7.96483178670047
4.00146352678407
-13.6946064012130
2.8358574496412
9.31838147415543
-7.75740740275121
-4.93474062568563
10.5300537220615
-6.10460178775547
0.478325079015789
5.89851351973811
-6.19532720175236
4.17217359734448
0.214884498245079
3.41992934169412
1.42850058200830
2.63791355728564
3.75813112294628
-4.05093410530075
9.55955069097943
0.584823390755441
-0.84609369463096
5.52216318329243
-7.11437981640872
-3.89918448184312
-3.75535414167073
-9.8169202873852
9.46609377229282
-5.59428783689482
4.95656906900365
-9.92596800001966
4.80136941933775
3.9341658856551
2.69578613885881
5.21710027881483
-4.66737674628199
1.26821045123126
-0.268232283381645
-8.21594930846046
3.79242984764805
-1.27416664306343
1.02681700852295
-8.48967638083803
7.17702588148928
-6.95187743405821
2.29514045852033
-7.27397071937588
-2.86537653397430
-0.569932529838176
3.2052626309105
-4.03097085893049
-2.77265275116443
5.08814250442136
-1.92661151173972
-5.11666909832062
0.548680135960278
5.04919614917613
-5.76137329200494
4.14981931148726
-4.7821089575132
-4.62049100120712
-2.03070581161469
-6.85384251473393
1.09818058417373
-0.909764908286356
8.90902092825634
-2.72822062524603
1.71936538476131
2.49676946454193
0.630632229002130
0.275279926422537
-3.97455936891113
1.51451507305542
-8.15921358813922
6.75559136295738
2.46988393467057
3.12793414200261
-4.23715779809308
1.06070925786110
-0.516499652810004
2.46221833558508
5.03403027829766
71.4844071027844
79.0524206915954
-49.0912847618688
6.3061687863089
-0.404029568809419
-7.52779045968901
2.94873544948417
-17.7252793101866
-3.9367082690942
1.95208754732007
-0.946582111483964
0.221127338808001
-1.48765632455776
7.3704127489538
-8.14004475951385
-9.38316791053028
0.919923280592839
-4.85945297235531
-15.1119414044598
7.40325396687636
-4.93474520745709
0.539207785802948
-8.83637810588702
4.18458057985340
-10.5654982625789
3.06260802242072
-5.79749140225809
5.05702198886742
-7.7509878098133
6.90506631841106
-4.94251205026703
3.57745453770462
18.3956275381001
29.5857059354573
3.64454779079285
24.8295636711322
1.24995476313783
-3.59675650830131
-6.06052139569593
-18.2852934783964
0.941968239707111
-10.8262360030294
-6.65512589304183
-0.968305795186647
10.1893616925434
-10.5228260642072
-2.98075115994525
-6.6982230661012
1.67419643131223
-9.39138350750968
-2.09333431367271
-9.6131907284719
4.8516441401251
-4.62584118542127
1.77126222962256
-2.31484236222084
6.73380530434685
-6.16260747486601
4.66006622690154
-11.4865003924453
3.0864620894709
-2.77267095044367
-0.255473337782472
10.0798414557549
-17.4909503149318
8.92339374265446
17.1597224651170
5.37638191852108
-10.178738036498
-2.85804248093819
-3.83735850666763
-8.0443496136725
3.87439732720614
-2.31391651363385
-7.94294868783184
3.86711687746704
-7.22045107676958
1.66341066697305
2.23349604388548
-1.04449094038480
-1.71731520709866
-7.60372080219845
0.265881681837865
6.28469584883362
-10.0554251334973
-2.51838912707073
0.86490142640502
0.922695872626507
-1.72368045898379
-5.85062092522044
5.0276071735365
-1.29331299868633
-8.35173971486375
14.9023761480082
-2.69437853902643
-6.90705612123262
4.55555896159441
-7.30487073786164
3.8970499981251
-5.04464358139872
5.23182681270826
-6.12836660918695
18.9383115228733
-17.2644801841216
7.5228648200599
4.11457624035634
-7.96523517509913
1.08176293248765
1.07778685272949
-3.85743187651715
3.06172624623370
-9.75823044648081
1.74750899634642
10.4761688095238
-2.44570328924164
-4.7424507371755
9.86297676006549
-6.96816752037334
-5.44475211454933
-5.33941934110629
15.7570757234023
-7.39645025410211
3.23546877599713
-4.8305810414608
-2.24966418478505
1.98738198031045
26.5680157175503
-24.4887809758514
16.1604048694183
14.6525010597846
2.15851354610615
3.53020999255791
-11.4191262136630
18.3851847266904
-10.3113940342463
4.66302463064978
-17.0357801855911
3.51383252006056
-1.01283815317372
-3.89651490897625
14.0786845186421
-6.47027754147334
7.82752479280979
-22.4278202700132
8.47752886542935
1.90459653732819
-1.17137243113781
-3.03898016895413
5.05056444082401
-6.76831985616553

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.254999837514634 \tabularnewline
22.3226561141259 \tabularnewline
8.4237121927371 \tabularnewline
31.4840967891282 \tabularnewline
16.5161163829024 \tabularnewline
6.10629223042067 \tabularnewline
-12.7918432975015 \tabularnewline
-3.8888687922972 \tabularnewline
-4.5147400268326 \tabularnewline
-2.49899167140276 \tabularnewline
-9.65172800263859 \tabularnewline
0.630780336137417 \tabularnewline
-4.29202525286443 \tabularnewline
0.144093452694747 \tabularnewline
-1.80994365970992 \tabularnewline
-9.54016368517427 \tabularnewline
0.0709092051393568 \tabularnewline
-6.60695825215738 \tabularnewline
-1.44072814974947 \tabularnewline
-13.2867838967999 \tabularnewline
-1.04914047601546 \tabularnewline
-2.20174739282080 \tabularnewline
1.48095864724017 \tabularnewline
-2.08000212982048 \tabularnewline
-3.50015185059558 \tabularnewline
-3.61431620744694 \tabularnewline
-5.35375790479499 \tabularnewline
-2.28735695644625 \tabularnewline
-1.35003115372179 \tabularnewline
5.10303935720208 \tabularnewline
20.0833965881239 \tabularnewline
16.4033902060373 \tabularnewline
-0.0212652321290263 \tabularnewline
1.14865841876686 \tabularnewline
2.37164085497488 \tabularnewline
0.260574971410166 \tabularnewline
-13.0376189881084 \tabularnewline
10.7361767260307 \tabularnewline
-11.5669480927834 \tabularnewline
-0.786199330695074 \tabularnewline
1.61312176254781 \tabularnewline
-1.80558096741588 \tabularnewline
8.18005223910157 \tabularnewline
-4.93724873583824 \tabularnewline
-8.78531748551356 \tabularnewline
-1.04392609510290 \tabularnewline
-11.2962438947210 \tabularnewline
-3.18048133791269 \tabularnewline
-0.302304985521730 \tabularnewline
-14.1610036518591 \tabularnewline
1.45262072957604 \tabularnewline
-1.66493215893700 \tabularnewline
-1.40312765594212 \tabularnewline
1.44044165697868 \tabularnewline
-3.94174676213769 \tabularnewline
-0.257042482620874 \tabularnewline
-0.462315180358159 \tabularnewline
1.01225225939854 \tabularnewline
-3.7239133363085 \tabularnewline
10.6454102952348 \tabularnewline
-6.15682156664977 \tabularnewline
12.4414513125185 \tabularnewline
-5.28648924851091 \tabularnewline
1.66226586866823 \tabularnewline
-2.05037777282935 \tabularnewline
-0.537310887205521 \tabularnewline
-3.97973775485266 \tabularnewline
3.65961394085734 \tabularnewline
-2.43056190259458 \tabularnewline
-0.516016129310074 \tabularnewline
-0.161820894523965 \tabularnewline
1.78860855446208 \tabularnewline
1.27931309839162 \tabularnewline
1.35584501959290 \tabularnewline
-0.521915300599316 \tabularnewline
-4.20548225481622 \tabularnewline
0.919568715268673 \tabularnewline
-1.02446830651914 \tabularnewline
-1.22627907305345 \tabularnewline
2.14453507119079 \tabularnewline
-2.47221940864232 \tabularnewline
2.29255357486522 \tabularnewline
2.19054966368731 \tabularnewline
-3.28711713368396 \tabularnewline
6.12371463600336 \tabularnewline
2.41261011582007 \tabularnewline
-3.66397984638235 \tabularnewline
1.64802758920126 \tabularnewline
0.23925091815541 \tabularnewline
3.13555609634777 \tabularnewline
0.29962961369995 \tabularnewline
-1.30597505012642 \tabularnewline
1.95748824814583 \tabularnewline
0.476178838572573 \tabularnewline
-0.799517469300704 \tabularnewline
1.10947279764473 \tabularnewline
0.0442829008006028 \tabularnewline
1.60979309656664 \tabularnewline
-0.355706999776107 \tabularnewline
-0.747608782537832 \tabularnewline
-0.953871088989577 \tabularnewline
1.05702558257425 \tabularnewline
-0.270478963253514 \tabularnewline
-5.25857208708511 \tabularnewline
1.82064767825495 \tabularnewline
1.20474253524657 \tabularnewline
-0.98644112184596 \tabularnewline
-7.4837703860611 \tabularnewline
24.2434463672049 \tabularnewline
72.4526493311843 \tabularnewline
-16.960601635877 \tabularnewline
-9.81037132629332 \tabularnewline
-4.35922956655178 \tabularnewline
0.609716438537836 \tabularnewline
-7.99574687712668 \tabularnewline
-8.74106937035226 \tabularnewline
-14.4716529247682 \tabularnewline
7.89289529025737 \tabularnewline
2.55704276009891 \tabularnewline
-1.12267051719415 \tabularnewline
-2.90057413222877 \tabularnewline
-4.58190220187591 \tabularnewline
-2.53363606566828 \tabularnewline
-7.26897611583327 \tabularnewline
-6.00705181747068 \tabularnewline
2.64109706329254 \tabularnewline
-3.26101731942921 \tabularnewline
-7.92632724347435 \tabularnewline
7.96483178670047 \tabularnewline
4.00146352678407 \tabularnewline
-13.6946064012130 \tabularnewline
2.8358574496412 \tabularnewline
9.31838147415543 \tabularnewline
-7.75740740275121 \tabularnewline
-4.93474062568563 \tabularnewline
10.5300537220615 \tabularnewline
-6.10460178775547 \tabularnewline
0.478325079015789 \tabularnewline
5.89851351973811 \tabularnewline
-6.19532720175236 \tabularnewline
4.17217359734448 \tabularnewline
0.214884498245079 \tabularnewline
3.41992934169412 \tabularnewline
1.42850058200830 \tabularnewline
2.63791355728564 \tabularnewline
3.75813112294628 \tabularnewline
-4.05093410530075 \tabularnewline
9.55955069097943 \tabularnewline
0.584823390755441 \tabularnewline
-0.84609369463096 \tabularnewline
5.52216318329243 \tabularnewline
-7.11437981640872 \tabularnewline
-3.89918448184312 \tabularnewline
-3.75535414167073 \tabularnewline
-9.8169202873852 \tabularnewline
9.46609377229282 \tabularnewline
-5.59428783689482 \tabularnewline
4.95656906900365 \tabularnewline
-9.92596800001966 \tabularnewline
4.80136941933775 \tabularnewline
3.9341658856551 \tabularnewline
2.69578613885881 \tabularnewline
5.21710027881483 \tabularnewline
-4.66737674628199 \tabularnewline
1.26821045123126 \tabularnewline
-0.268232283381645 \tabularnewline
-8.21594930846046 \tabularnewline
3.79242984764805 \tabularnewline
-1.27416664306343 \tabularnewline
1.02681700852295 \tabularnewline
-8.48967638083803 \tabularnewline
7.17702588148928 \tabularnewline
-6.95187743405821 \tabularnewline
2.29514045852033 \tabularnewline
-7.27397071937588 \tabularnewline
-2.86537653397430 \tabularnewline
-0.569932529838176 \tabularnewline
3.2052626309105 \tabularnewline
-4.03097085893049 \tabularnewline
-2.77265275116443 \tabularnewline
5.08814250442136 \tabularnewline
-1.92661151173972 \tabularnewline
-5.11666909832062 \tabularnewline
0.548680135960278 \tabularnewline
5.04919614917613 \tabularnewline
-5.76137329200494 \tabularnewline
4.14981931148726 \tabularnewline
-4.7821089575132 \tabularnewline
-4.62049100120712 \tabularnewline
-2.03070581161469 \tabularnewline
-6.85384251473393 \tabularnewline
1.09818058417373 \tabularnewline
-0.909764908286356 \tabularnewline
8.90902092825634 \tabularnewline
-2.72822062524603 \tabularnewline
1.71936538476131 \tabularnewline
2.49676946454193 \tabularnewline
0.630632229002130 \tabularnewline
0.275279926422537 \tabularnewline
-3.97455936891113 \tabularnewline
1.51451507305542 \tabularnewline
-8.15921358813922 \tabularnewline
6.75559136295738 \tabularnewline
2.46988393467057 \tabularnewline
3.12793414200261 \tabularnewline
-4.23715779809308 \tabularnewline
1.06070925786110 \tabularnewline
-0.516499652810004 \tabularnewline
2.46221833558508 \tabularnewline
5.03403027829766 \tabularnewline
71.4844071027844 \tabularnewline
79.0524206915954 \tabularnewline
-49.0912847618688 \tabularnewline
6.3061687863089 \tabularnewline
-0.404029568809419 \tabularnewline
-7.52779045968901 \tabularnewline
2.94873544948417 \tabularnewline
-17.7252793101866 \tabularnewline
-3.9367082690942 \tabularnewline
1.95208754732007 \tabularnewline
-0.946582111483964 \tabularnewline
0.221127338808001 \tabularnewline
-1.48765632455776 \tabularnewline
7.3704127489538 \tabularnewline
-8.14004475951385 \tabularnewline
-9.38316791053028 \tabularnewline
0.919923280592839 \tabularnewline
-4.85945297235531 \tabularnewline
-15.1119414044598 \tabularnewline
7.40325396687636 \tabularnewline
-4.93474520745709 \tabularnewline
0.539207785802948 \tabularnewline
-8.83637810588702 \tabularnewline
4.18458057985340 \tabularnewline
-10.5654982625789 \tabularnewline
3.06260802242072 \tabularnewline
-5.79749140225809 \tabularnewline
5.05702198886742 \tabularnewline
-7.7509878098133 \tabularnewline
6.90506631841106 \tabularnewline
-4.94251205026703 \tabularnewline
3.57745453770462 \tabularnewline
18.3956275381001 \tabularnewline
29.5857059354573 \tabularnewline
3.64454779079285 \tabularnewline
24.8295636711322 \tabularnewline
1.24995476313783 \tabularnewline
-3.59675650830131 \tabularnewline
-6.06052139569593 \tabularnewline
-18.2852934783964 \tabularnewline
0.941968239707111 \tabularnewline
-10.8262360030294 \tabularnewline
-6.65512589304183 \tabularnewline
-0.968305795186647 \tabularnewline
10.1893616925434 \tabularnewline
-10.5228260642072 \tabularnewline
-2.98075115994525 \tabularnewline
-6.6982230661012 \tabularnewline
1.67419643131223 \tabularnewline
-9.39138350750968 \tabularnewline
-2.09333431367271 \tabularnewline
-9.6131907284719 \tabularnewline
4.8516441401251 \tabularnewline
-4.62584118542127 \tabularnewline
1.77126222962256 \tabularnewline
-2.31484236222084 \tabularnewline
6.73380530434685 \tabularnewline
-6.16260747486601 \tabularnewline
4.66006622690154 \tabularnewline
-11.4865003924453 \tabularnewline
3.0864620894709 \tabularnewline
-2.77267095044367 \tabularnewline
-0.255473337782472 \tabularnewline
10.0798414557549 \tabularnewline
-17.4909503149318 \tabularnewline
8.92339374265446 \tabularnewline
17.1597224651170 \tabularnewline
5.37638191852108 \tabularnewline
-10.178738036498 \tabularnewline
-2.85804248093819 \tabularnewline
-3.83735850666763 \tabularnewline
-8.0443496136725 \tabularnewline
3.87439732720614 \tabularnewline
-2.31391651363385 \tabularnewline
-7.94294868783184 \tabularnewline
3.86711687746704 \tabularnewline
-7.22045107676958 \tabularnewline
1.66341066697305 \tabularnewline
2.23349604388548 \tabularnewline
-1.04449094038480 \tabularnewline
-1.71731520709866 \tabularnewline
-7.60372080219845 \tabularnewline
0.265881681837865 \tabularnewline
6.28469584883362 \tabularnewline
-10.0554251334973 \tabularnewline
-2.51838912707073 \tabularnewline
0.86490142640502 \tabularnewline
0.922695872626507 \tabularnewline
-1.72368045898379 \tabularnewline
-5.85062092522044 \tabularnewline
5.0276071735365 \tabularnewline
-1.29331299868633 \tabularnewline
-8.35173971486375 \tabularnewline
14.9023761480082 \tabularnewline
-2.69437853902643 \tabularnewline
-6.90705612123262 \tabularnewline
4.55555896159441 \tabularnewline
-7.30487073786164 \tabularnewline
3.8970499981251 \tabularnewline
-5.04464358139872 \tabularnewline
5.23182681270826 \tabularnewline
-6.12836660918695 \tabularnewline
18.9383115228733 \tabularnewline
-17.2644801841216 \tabularnewline
7.5228648200599 \tabularnewline
4.11457624035634 \tabularnewline
-7.96523517509913 \tabularnewline
1.08176293248765 \tabularnewline
1.07778685272949 \tabularnewline
-3.85743187651715 \tabularnewline
3.06172624623370 \tabularnewline
-9.75823044648081 \tabularnewline
1.74750899634642 \tabularnewline
10.4761688095238 \tabularnewline
-2.44570328924164 \tabularnewline
-4.7424507371755 \tabularnewline
9.86297676006549 \tabularnewline
-6.96816752037334 \tabularnewline
-5.44475211454933 \tabularnewline
-5.33941934110629 \tabularnewline
15.7570757234023 \tabularnewline
-7.39645025410211 \tabularnewline
3.23546877599713 \tabularnewline
-4.8305810414608 \tabularnewline
-2.24966418478505 \tabularnewline
1.98738198031045 \tabularnewline
26.5680157175503 \tabularnewline
-24.4887809758514 \tabularnewline
16.1604048694183 \tabularnewline
14.6525010597846 \tabularnewline
2.15851354610615 \tabularnewline
3.53020999255791 \tabularnewline
-11.4191262136630 \tabularnewline
18.3851847266904 \tabularnewline
-10.3113940342463 \tabularnewline
4.66302463064978 \tabularnewline
-17.0357801855911 \tabularnewline
3.51383252006056 \tabularnewline
-1.01283815317372 \tabularnewline
-3.89651490897625 \tabularnewline
14.0786845186421 \tabularnewline
-6.47027754147334 \tabularnewline
7.82752479280979 \tabularnewline
-22.4278202700132 \tabularnewline
8.47752886542935 \tabularnewline
1.90459653732819 \tabularnewline
-1.17137243113781 \tabularnewline
-3.03898016895413 \tabularnewline
5.05056444082401 \tabularnewline
-6.76831985616553 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=66902&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.254999837514634[/C][/ROW]
[ROW][C]22.3226561141259[/C][/ROW]
[ROW][C]8.4237121927371[/C][/ROW]
[ROW][C]31.4840967891282[/C][/ROW]
[ROW][C]16.5161163829024[/C][/ROW]
[ROW][C]6.10629223042067[/C][/ROW]
[ROW][C]-12.7918432975015[/C][/ROW]
[ROW][C]-3.8888687922972[/C][/ROW]
[ROW][C]-4.5147400268326[/C][/ROW]
[ROW][C]-2.49899167140276[/C][/ROW]
[ROW][C]-9.65172800263859[/C][/ROW]
[ROW][C]0.630780336137417[/C][/ROW]
[ROW][C]-4.29202525286443[/C][/ROW]
[ROW][C]0.144093452694747[/C][/ROW]
[ROW][C]-1.80994365970992[/C][/ROW]
[ROW][C]-9.54016368517427[/C][/ROW]
[ROW][C]0.0709092051393568[/C][/ROW]
[ROW][C]-6.60695825215738[/C][/ROW]
[ROW][C]-1.44072814974947[/C][/ROW]
[ROW][C]-13.2867838967999[/C][/ROW]
[ROW][C]-1.04914047601546[/C][/ROW]
[ROW][C]-2.20174739282080[/C][/ROW]
[ROW][C]1.48095864724017[/C][/ROW]
[ROW][C]-2.08000212982048[/C][/ROW]
[ROW][C]-3.50015185059558[/C][/ROW]
[ROW][C]-3.61431620744694[/C][/ROW]
[ROW][C]-5.35375790479499[/C][/ROW]
[ROW][C]-2.28735695644625[/C][/ROW]
[ROW][C]-1.35003115372179[/C][/ROW]
[ROW][C]5.10303935720208[/C][/ROW]
[ROW][C]20.0833965881239[/C][/ROW]
[ROW][C]16.4033902060373[/C][/ROW]
[ROW][C]-0.0212652321290263[/C][/ROW]
[ROW][C]1.14865841876686[/C][/ROW]
[ROW][C]2.37164085497488[/C][/ROW]
[ROW][C]0.260574971410166[/C][/ROW]
[ROW][C]-13.0376189881084[/C][/ROW]
[ROW][C]10.7361767260307[/C][/ROW]
[ROW][C]-11.5669480927834[/C][/ROW]
[ROW][C]-0.786199330695074[/C][/ROW]
[ROW][C]1.61312176254781[/C][/ROW]
[ROW][C]-1.80558096741588[/C][/ROW]
[ROW][C]8.18005223910157[/C][/ROW]
[ROW][C]-4.93724873583824[/C][/ROW]
[ROW][C]-8.78531748551356[/C][/ROW]
[ROW][C]-1.04392609510290[/C][/ROW]
[ROW][C]-11.2962438947210[/C][/ROW]
[ROW][C]-3.18048133791269[/C][/ROW]
[ROW][C]-0.302304985521730[/C][/ROW]
[ROW][C]-14.1610036518591[/C][/ROW]
[ROW][C]1.45262072957604[/C][/ROW]
[ROW][C]-1.66493215893700[/C][/ROW]
[ROW][C]-1.40312765594212[/C][/ROW]
[ROW][C]1.44044165697868[/C][/ROW]
[ROW][C]-3.94174676213769[/C][/ROW]
[ROW][C]-0.257042482620874[/C][/ROW]
[ROW][C]-0.462315180358159[/C][/ROW]
[ROW][C]1.01225225939854[/C][/ROW]
[ROW][C]-3.7239133363085[/C][/ROW]
[ROW][C]10.6454102952348[/C][/ROW]
[ROW][C]-6.15682156664977[/C][/ROW]
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[ROW][C]5.05056444082401[/C][/ROW]
[ROW][C]-6.76831985616553[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=66902&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=66902&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
0.254999837514634
22.3226561141259
8.4237121927371
31.4840967891282
16.5161163829024
6.10629223042067
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0.144093452694747
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0.0709092051393568
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-13.2867838967999
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1.48095864724017
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-1.35003115372179
5.10303935720208
20.0833965881239
16.4033902060373
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1.14865841876686
2.37164085497488
0.260574971410166
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10.7361767260307
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1.61312176254781
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8.18005223910157
-4.93724873583824
-8.78531748551356
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-0.302304985521730
-14.1610036518591
1.45262072957604
-1.66493215893700
-1.40312765594212
1.44044165697868
-3.94174676213769
-0.257042482620874
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1.01225225939854
-3.7239133363085
10.6454102952348
-6.15682156664977
12.4414513125185
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1.66226586866823
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1.27931309839162
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2.14453507119079
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6.12371463600336
2.41261011582007
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1.64802758920126
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3.13555609634777
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2.64109706329254
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4.00146352678407
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2.8358574496412
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10.5300537220615
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3.41992934169412
1.42850058200830
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4.80136941933775
3.9341658856551
2.69578613885881
5.21710027881483
-4.66737674628199
1.26821045123126
-0.268232283381645
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3.79242984764805
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Figure 1

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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 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code