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Author

*Unverified author*

R Software Module

rwasp_arimabackwardselection.wasp

Title produced by software

ARIMA Backward Selection

Date of computation

Mon, 08 Dec 2008 05:23:52 -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/2008/Dec/08/t1228739073ywo6gctmf7k3er7.htm/, Retrieved Thu, 16 May 2024 20:07:09 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=30427, Retrieved Thu, 16 May 2024 20:07:09 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

220

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   [Spectral Analysis] [Gilliam Schoorel] [2008-12-07 21:00:07] [a9b974cca921a7a5a84c6ce01f3dc8c2]
- RMP       [ARIMA Backward Selection] [Gilliam Schoorel] [2008-12-08 12:23:52] [4a7b7ae341cb1fe8993cedd56bcfa583] [Current]
- RMPD        [Standard Deviation-Mean Plot] [paper GS] [2008-12-09 09:08:37] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [Standard Deviation-Mean Plot] [Paper GS] [2008-12-09 09:12:31] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [Variance Reduction Matrix] [Gs Paper] [2008-12-09 09:16:24] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMP           [ARIMA Backward Selection] [Gilliam Schoorel] [2008-12-16 15:48:43] [74be16979710d4c4e7c6647856088456] 
-                 [ARIMA Backward Selection] [Toon Wouters] [2008-12-16 21:13:25] [810fefdbb91d48e1fca60d884166311f] 
- R                 [ARIMA Backward Selection] [S�ren Van Donink] [2008-12-17 07:00:37] [74be16979710d4c4e7c6647856088456] 
- RMPD        [Variance Reduction Matrix] [GS Paper] [2008-12-09 09:18:55] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [(Partial) Autocorrelation Function] [GS Paper] [2008-12-09 09:24:15] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [(Partial) Autocorrelation Function] [GS PAper] [2008-12-09 09:28:04] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [(Partial) Autocorrelation Function] [GS PAPER] [2008-12-09 09:30:03] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [(Partial) Autocorrelation Function] [GS Paper] [2008-12-09 09:31:54] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [(Partial) Autocorrelation Function] [gs Paper] [2008-12-09 09:33:56] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [(Partial) Autocorrelation Function] [GS paper] [2008-12-09 09:36:18] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [Spectral Analysis] [gs paper] [2008-12-09 09:39:31] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [Spectral Analysis] [gspaper] [2008-12-09 09:41:34] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
- RMPD        [Spectral Analysis] [gspaper] [2008-12-09 09:44:02] [a9b974cca921a7a5a84c6ce01f3dc8c2] 
-   PD          [Spectral Analysis] [gspaper] [2008-12-09 09:57:38] [74be16979710d4c4e7c6647856088456] 
-   PD          [Spectral Analysis] [gspaper] [2008-12-09 09:59:39] [74be16979710d4c4e7c6647856088456] 
-   PD          [Spectral Analysis] [gspaper] [2008-12-09 10:01:08] [74be16979710d4c4e7c6647856088456] 
- RMPD          [Kendall tau Correlation Matrix] [GSpaper] [2008-12-09 10:13:49] [666bda00bbd072dde5655a1423b1377b] 

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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	4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30427&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]4 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=30427&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30427&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	4 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 )	0.046	0.9813	-0.0327	0.9769	-0.6824	-0.4308	0.7666
(p-val)	(0.4158 )	(0 )	(0.5307 )	(0 )	(0 )	(0 )	(0 )
Estimates ( 2 )	0.1378	0.8066	0	0.873	0.6955	0.2998	-0.9339
(p-val)	(0.1697 )	(0 )	(NA )	(0 )	(0 )	(0 )	(0 )
Estimates ( 3 )	0	0.9682	0	1	0.6858	0.31	-0.9376
(p-val)	(NA )	(0 )	(NA )	(0 )	(0 )	(0 )	(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 ) & 0.046 & 0.9813 & -0.0327 & 0.9769 & -0.6824 & -0.4308 & 0.7666 \tabularnewline
(p-val) & (0.4158 ) & (0 ) & (0.5307 ) & (0 ) & (0 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.1378 & 0.8066 & 0 & 0.873 & 0.6955 & 0.2998 & -0.9339 \tabularnewline
(p-val) & (0.1697 ) & (0 ) & (NA ) & (0 ) & (0 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0 & 0.9682 & 0 & 1 & 0.6858 & 0.31 & -0.9376 \tabularnewline
(p-val) & (NA ) & (0 ) & (NA ) & (0 ) & (0 ) & (0 ) & (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=30427&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]0.046[/C][C]0.9813[/C][C]-0.0327[/C][C]0.9769[/C][C]-0.6824[/C][C]-0.4308[/C][C]0.7666[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4158 )[/C][C](0 )[/C][C](0.5307 )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.1378[/C][C]0.8066[/C][C]0[/C][C]0.873[/C][C]0.6955[/C][C]0.2998[/C][C]-0.9339[/C][/ROW]
[ROW][C](p-val)[/C][C](0.1697 )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0[/C][C]0.9682[/C][C]0[/C][C]1[/C][C]0.6858[/C][C]0.31[/C][C]-0.9376[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/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=30427&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30427&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 )	0.046	0.9813	-0.0327	0.9769	-0.6824	-0.4308	0.7666
(p-val)	(0.4158 )	(0 )	(0.5307 )	(0 )	(0 )	(0 )	(0 )
Estimates ( 2 )	0.1378	0.8066	0	0.873	0.6955	0.2998	-0.9339
(p-val)	(0.1697 )	(0 )	(NA )	(0 )	(0 )	(0 )	(0 )
Estimates ( 3 )	0	0.9682	0	1	0.6858	0.31	-0.9376
(p-val)	(NA )	(0 )	(NA )	(0 )	(0 )	(0 )	(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

22.3785064886151
40.9449854923112
-11.7150027486239
-15.1713694101681
-34.3192405750359
34.6631751993281
-2.5700365450742
-8.71495255139827
-18.7197199380602
-41.5946118328445
24.9125431097621
20.5749333157631
82.5417586414395
24.2163691190038
4.62749597678372
8.01689679873285
48.6340273815247
44.6048966919696
47.1904304522605
-24.8049727845385
-21.3029805312793
10.6098774215995
-6.62922807089883
21.6474287757540
86.2790415614575
3.29898528994313
-43.0114634321702
-36.6161689135998
-18.3718895838211
14.0785736665574
-27.6732402552926
-62.0918336579236
-33.2510892460472
-58.2897623151476
29.6124227123327
8.06447856680947
13.0251813681365
-44.1704696147069
-6.48022140887149
-14.8070855083806
-18.8694003575766
13.7481110553071
-9.39236542249925
-13.5127333135030
-7.51532769208293
-21.5986126264778
23.8843735429063
-3.9112570521293
39.1765261980725
-14.3497658608138
-22.4525668259679
1.38906596524935
-4.05314648410401
10.5301203574855
-1.86267892641382
3.54638847751916
-33.3781238177425
-33.1588764220521
13.729165209263
12.6854375108193
41.3224691204883
-39.0774706357962
-6.41378169116752
12.4167535249528
-17.9665881769272
2.19484627195182
-4.9131714361086
-0.442253734708405
-9.48862555319863
-10.7572951419594
44.4928343529694
48.7413692719579
111.959525653481
31.3669828326349
26.1937586166492
13.7006006280804
6.56818582516025
7.83339381526781
7.55496087423243
-2.98425607582805
-20.5845053349657
-56.2156557555713
22.7603298222581
3.04413403914670
54.3507822178595
-31.2361549820276
-14.9995226910323
2.49482523392631
-40.7524743218049
13.7889640103591
-29.6563757301657
4.89673867539702
-50.1295209619479
-2.03264673170375
16.8444685392781
23.0443833767602
26.4444989540797
-1.87217851794234
9.23334114424557
-17.3603588798825
10.2122489069995
34.2844818830984
-17.6952382797121
-41.567418407035
-30.5342905982939
-13.6886446391841
45.0073850510075
10.6072238234693
37.7837049156396
-24.2270549144156
-8.66491597432785
1.56670496758333
4.69437423389259
40.6459032378483
-35.822719640298
-14.4179091084217
-17.6118160593976
5.29407776417126
57.5689670685297
33.0452627610603
100.90745536152
58.6378727355779
29.8553364971637
22.7483122463651
-0.0208615653862098
55.6320179705152
-6.94868507239816
-22.3207533378475
-73.601179246201
-23.5693026537418
-0.0420882021667346
40.2537972896144
32.9859122879408
-11.1791199892917
-35.7622259541634
-40.5942831522767
-17.3565978835991
38.7503088349158
-27.6194796385694
-21.1403633794899
-36.3060306715591
8.25111057447377
35.734659816826
6.4353256493502
44.3489094404115
-32.6181384830482
44.4963999771848
-36.6472011621712
-12.4497949129337
60.2256701402524
-19.0469572318363
-5.60839183025759
-57.4226879517737
35.1358099493672
31.4587761608162
74.9891907109532
65.4246364769504
30.3004542607972
-7.84745073272175
-8.30445720313011
-3.9798851130882
63.8814575771785
-40.1280602686094
-34.8342986219901
-57.3539560553383
-10.9255640335868
-2.68663474049469
24.7060957030662
36.5885724761504
-33.1605601652453
-9.22927834976195
-16.4721848265453
17.7496899438851
6.76065208026216
-38.6841235745173
14.6871703483882
-47.8339360451761
-27.9395523924859
39.6100944427891
27.958458369271
66.5830298755682
3.72135529391909
-21.2697790528745
-19.4581815597893
11.6773425646361
53.2530727671757
-48.0961463925165
-23.4546760274113
-43.4324308144439
-4.98183481685973
38.0609408475339
7.92836949024416
54.2320661829685
-24.9063265395049
-1.10564018654323
-16.9671999073641
-18.7554640918991
73.9259534092603
-79.6821450122355
6.8537785907898
-54.6357640337714
5.4083245196717
-8.23205922849942
34.3817468754066
29.5923401021473
4.35599640065295
-36.1368178496713
9.17797035835814
-26.9730313003033
73.8037230426676
-70.452809894436
-1.90263248090317
-58.4316663529561
-7.43715978132497
5.43953056234499
8.3367150103227
22.1932226006127
-40.1728182986949
4.8018370027543
-7.8136256549169
8.75618820251726
51.7668101223151
-46.0839614068583
-5.75332415233752
-44.010396530341
-3.924523382771
5.26588057889939
24.8166909873333
27.5991602331611
-23.0653090331093
-5.8131969754846
-6.0423549204824
-17.0155073864105
92.8546513601721
-32.9853563161068
-12.7726320683495
-23.8083820056398
14.3440593272324
-3.83509896076848
-2.20211006781624
22.9008217341529
-5.78485452876235
-21.3651457335157
-25.7147708218192
-19.0820615807527
106.792565747689
-36.453053901185
-27.2684673908959
-34.7362515596583
-3.68954167946275
9.23618466290733
-1.65926455572803
30.9441435483516
-33.8422258526126
3.03289173462433
-1.15898144546924
-19.2283726819272
75.1383794014399
-9.9654949091202
-7.83702770223287
-7.87734529667658
-15.2994228799268
-1.96341188281787
9.43898719155064
72.0838776060879
-1.90505667036179
12.5505470498203
5.66117619109626
-1.13871465178613
104.370356813131
2.57579001127325
-0.78544339154057
-6.11413576630202
-0.936773765022123
51.1722628541877
25.6034007756113
75.648273267973
-34.6973908916322
10.2195137298779
-18.4841878881763
-10.8855763772888
74.1733331186918
-1.35848909607401
-6.45869271660725
-37.3148618362936
-30.0158535090323
40.2104108322034
11.6125619914086
65.0658329295034
-48.291086929222
12.3811129140797
-26.8423058546248
-16.3320042453507
70.6437612819805
-11.0435768256039
-12.0171715558186
-37.0535979061938
-22.4154288573814
-10.2135232215070
8.72223670055793
42.7357374916201
-25.2945605597388
-18.5584753079700
-7.20183932004707
-29.1160714591336
73.737129806159
-22.4524358694435
-8.05098551733854
-25.6150831694156
-45.9798873901229
38.0070685466039
20.8954464069369
87.3590284768338
-36.8433038018391
-6.00028478976395
-16.6828217950041
5.44520054551831
90.3490136325772
-2.75556800036073
-15.1188908141312
17.7559613696221
-16.4604846474355
82.8462211657965
64.1866081630914
212.141801606751
-27.5162871061304
59.0925851958034
-9.33494969992572
38.8452513534614
65.8918577392988
-3.48146105285567
-27.4368126741983
-32.2175370399562
-30.5667954444818
14.0696868915968
19.9566336806135
82.1444341841618
-52.5175605305678
-21.3237648151169
-28.2367619460549
-42.9761822181632
101.610245178895
-0.498610702839564
-0.079997811920639
-63.2860147148329
-7.55096938014204
42.8766809657915
26.9560965144466
61.0727678395122
-9.59667980466775
-23.8125051304427
-60.6272470502508
-30.6038087685784
104.704868679268
-48.3380132804681
-0.392843747144909
-62.6178787075433
-15.9593324342879
14.3216276827599
-8.43637649344316
77.2693447113406
-59.9668887155805
2.24656280965031
-55.0245021329475
-6.50577456560929
42.0329845100611
24.9639061093985
-32.3035835946257
-34.6340724681287
-48.0315549651446
38.9703539428678
33.97735328507

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
 \tabularnewline
22.3785064886151 \tabularnewline
40.9449854923112 \tabularnewline
-11.7150027486239 \tabularnewline
-15.1713694101681 \tabularnewline
-34.3192405750359 \tabularnewline
34.6631751993281 \tabularnewline
-2.5700365450742 \tabularnewline
-8.71495255139827 \tabularnewline
-18.7197199380602 \tabularnewline
-41.5946118328445 \tabularnewline
24.9125431097621 \tabularnewline
20.5749333157631 \tabularnewline
82.5417586414395 \tabularnewline
24.2163691190038 \tabularnewline
4.62749597678372 \tabularnewline
8.01689679873285 \tabularnewline
48.6340273815247 \tabularnewline
44.6048966919696 \tabularnewline
47.1904304522605 \tabularnewline
-24.8049727845385 \tabularnewline
-21.3029805312793 \tabularnewline
10.6098774215995 \tabularnewline
-6.62922807089883 \tabularnewline
21.6474287757540 \tabularnewline
86.2790415614575 \tabularnewline
3.29898528994313 \tabularnewline
-43.0114634321702 \tabularnewline
-36.6161689135998 \tabularnewline
-18.3718895838211 \tabularnewline
14.0785736665574 \tabularnewline
-27.6732402552926 \tabularnewline
-62.0918336579236 \tabularnewline
-33.2510892460472 \tabularnewline
-58.2897623151476 \tabularnewline
29.6124227123327 \tabularnewline
8.06447856680947 \tabularnewline
13.0251813681365 \tabularnewline
-44.1704696147069 \tabularnewline
-6.48022140887149 \tabularnewline
-14.8070855083806 \tabularnewline
-18.8694003575766 \tabularnewline
13.7481110553071 \tabularnewline
-9.39236542249925 \tabularnewline
-13.5127333135030 \tabularnewline
-7.51532769208293 \tabularnewline
-21.5986126264778 \tabularnewline
23.8843735429063 \tabularnewline
-3.9112570521293 \tabularnewline
39.1765261980725 \tabularnewline
-14.3497658608138 \tabularnewline
-22.4525668259679 \tabularnewline
1.38906596524935 \tabularnewline
-4.05314648410401 \tabularnewline
10.5301203574855 \tabularnewline
-1.86267892641382 \tabularnewline
3.54638847751916 \tabularnewline
-33.3781238177425 \tabularnewline
-33.1588764220521 \tabularnewline
13.729165209263 \tabularnewline
12.6854375108193 \tabularnewline
41.3224691204883 \tabularnewline
-39.0774706357962 \tabularnewline
-6.41378169116752 \tabularnewline
12.4167535249528 \tabularnewline
-17.9665881769272 \tabularnewline
2.19484627195182 \tabularnewline
-4.9131714361086 \tabularnewline
-0.442253734708405 \tabularnewline
-9.48862555319863 \tabularnewline
-10.7572951419594 \tabularnewline
44.4928343529694 \tabularnewline
48.7413692719579 \tabularnewline
111.959525653481 \tabularnewline
31.3669828326349 \tabularnewline
26.1937586166492 \tabularnewline
13.7006006280804 \tabularnewline
6.56818582516025 \tabularnewline
7.83339381526781 \tabularnewline
7.55496087423243 \tabularnewline
-2.98425607582805 \tabularnewline
-20.5845053349657 \tabularnewline
-56.2156557555713 \tabularnewline
22.7603298222581 \tabularnewline
3.04413403914670 \tabularnewline
54.3507822178595 \tabularnewline
-31.2361549820276 \tabularnewline
-14.9995226910323 \tabularnewline
2.49482523392631 \tabularnewline
-40.7524743218049 \tabularnewline
13.7889640103591 \tabularnewline
-29.6563757301657 \tabularnewline
4.89673867539702 \tabularnewline
-50.1295209619479 \tabularnewline
-2.03264673170375 \tabularnewline
16.8444685392781 \tabularnewline
23.0443833767602 \tabularnewline
26.4444989540797 \tabularnewline
-1.87217851794234 \tabularnewline
9.23334114424557 \tabularnewline
-17.3603588798825 \tabularnewline
10.2122489069995 \tabularnewline
34.2844818830984 \tabularnewline
-17.6952382797121 \tabularnewline
-41.567418407035 \tabularnewline
-30.5342905982939 \tabularnewline
-13.6886446391841 \tabularnewline
45.0073850510075 \tabularnewline
10.6072238234693 \tabularnewline
37.7837049156396 \tabularnewline
-24.2270549144156 \tabularnewline
-8.66491597432785 \tabularnewline
1.56670496758333 \tabularnewline
4.69437423389259 \tabularnewline
40.6459032378483 \tabularnewline
-35.822719640298 \tabularnewline
-14.4179091084217 \tabularnewline
-17.6118160593976 \tabularnewline
5.29407776417126 \tabularnewline
57.5689670685297 \tabularnewline
33.0452627610603 \tabularnewline
100.90745536152 \tabularnewline
58.6378727355779 \tabularnewline
29.8553364971637 \tabularnewline
22.7483122463651 \tabularnewline
-0.0208615653862098 \tabularnewline
55.6320179705152 \tabularnewline
-6.94868507239816 \tabularnewline
-22.3207533378475 \tabularnewline
-73.601179246201 \tabularnewline
-23.5693026537418 \tabularnewline
-0.0420882021667346 \tabularnewline
40.2537972896144 \tabularnewline
32.9859122879408 \tabularnewline
-11.1791199892917 \tabularnewline
-35.7622259541634 \tabularnewline
-40.5942831522767 \tabularnewline
-17.3565978835991 \tabularnewline
38.7503088349158 \tabularnewline
-27.6194796385694 \tabularnewline
-21.1403633794899 \tabularnewline
-36.3060306715591 \tabularnewline
8.25111057447377 \tabularnewline
35.734659816826 \tabularnewline
6.4353256493502 \tabularnewline
44.3489094404115 \tabularnewline
-32.6181384830482 \tabularnewline
44.4963999771848 \tabularnewline
-36.6472011621712 \tabularnewline
-12.4497949129337 \tabularnewline
60.2256701402524 \tabularnewline
-19.0469572318363 \tabularnewline
-5.60839183025759 \tabularnewline
-57.4226879517737 \tabularnewline
35.1358099493672 \tabularnewline
31.4587761608162 \tabularnewline
74.9891907109532 \tabularnewline
65.4246364769504 \tabularnewline
30.3004542607972 \tabularnewline
-7.84745073272175 \tabularnewline
-8.30445720313011 \tabularnewline
-3.9798851130882 \tabularnewline
63.8814575771785 \tabularnewline
-40.1280602686094 \tabularnewline
-34.8342986219901 \tabularnewline
-57.3539560553383 \tabularnewline
-10.9255640335868 \tabularnewline
-2.68663474049469 \tabularnewline
24.7060957030662 \tabularnewline
36.5885724761504 \tabularnewline
-33.1605601652453 \tabularnewline
-9.22927834976195 \tabularnewline
-16.4721848265453 \tabularnewline
17.7496899438851 \tabularnewline
6.76065208026216 \tabularnewline
-38.6841235745173 \tabularnewline
14.6871703483882 \tabularnewline
-47.8339360451761 \tabularnewline
-27.9395523924859 \tabularnewline
39.6100944427891 \tabularnewline
27.958458369271 \tabularnewline
66.5830298755682 \tabularnewline
3.72135529391909 \tabularnewline
-21.2697790528745 \tabularnewline
-19.4581815597893 \tabularnewline
11.6773425646361 \tabularnewline
53.2530727671757 \tabularnewline
-48.0961463925165 \tabularnewline
-23.4546760274113 \tabularnewline
-43.4324308144439 \tabularnewline
-4.98183481685973 \tabularnewline
38.0609408475339 \tabularnewline
7.92836949024416 \tabularnewline
54.2320661829685 \tabularnewline
-24.9063265395049 \tabularnewline
-1.10564018654323 \tabularnewline
-16.9671999073641 \tabularnewline
-18.7554640918991 \tabularnewline
73.9259534092603 \tabularnewline
-79.6821450122355 \tabularnewline
6.8537785907898 \tabularnewline
-54.6357640337714 \tabularnewline
5.4083245196717 \tabularnewline
-8.23205922849942 \tabularnewline
34.3817468754066 \tabularnewline
29.5923401021473 \tabularnewline
4.35599640065295 \tabularnewline
-36.1368178496713 \tabularnewline
9.17797035835814 \tabularnewline
-26.9730313003033 \tabularnewline
73.8037230426676 \tabularnewline
-70.452809894436 \tabularnewline
-1.90263248090317 \tabularnewline
-58.4316663529561 \tabularnewline
-7.43715978132497 \tabularnewline
5.43953056234499 \tabularnewline
8.3367150103227 \tabularnewline
22.1932226006127 \tabularnewline
-40.1728182986949 \tabularnewline
4.8018370027543 \tabularnewline
-7.8136256549169 \tabularnewline
8.75618820251726 \tabularnewline
51.7668101223151 \tabularnewline
-46.0839614068583 \tabularnewline
-5.75332415233752 \tabularnewline
-44.010396530341 \tabularnewline
-3.924523382771 \tabularnewline
5.26588057889939 \tabularnewline
24.8166909873333 \tabularnewline
27.5991602331611 \tabularnewline
-23.0653090331093 \tabularnewline
-5.8131969754846 \tabularnewline
-6.0423549204824 \tabularnewline
-17.0155073864105 \tabularnewline
92.8546513601721 \tabularnewline
-32.9853563161068 \tabularnewline
-12.7726320683495 \tabularnewline
-23.8083820056398 \tabularnewline
14.3440593272324 \tabularnewline
-3.83509896076848 \tabularnewline
-2.20211006781624 \tabularnewline
22.9008217341529 \tabularnewline
-5.78485452876235 \tabularnewline
-21.3651457335157 \tabularnewline
-25.7147708218192 \tabularnewline
-19.0820615807527 \tabularnewline
106.792565747689 \tabularnewline
-36.453053901185 \tabularnewline
-27.2684673908959 \tabularnewline
-34.7362515596583 \tabularnewline
-3.68954167946275 \tabularnewline
9.23618466290733 \tabularnewline
-1.65926455572803 \tabularnewline
30.9441435483516 \tabularnewline
-33.8422258526126 \tabularnewline
3.03289173462433 \tabularnewline
-1.15898144546924 \tabularnewline
-19.2283726819272 \tabularnewline
75.1383794014399 \tabularnewline
-9.9654949091202 \tabularnewline
-7.83702770223287 \tabularnewline
-7.87734529667658 \tabularnewline
-15.2994228799268 \tabularnewline
-1.96341188281787 \tabularnewline
9.43898719155064 \tabularnewline
72.0838776060879 \tabularnewline
-1.90505667036179 \tabularnewline
12.5505470498203 \tabularnewline
5.66117619109626 \tabularnewline
-1.13871465178613 \tabularnewline
104.370356813131 \tabularnewline
2.57579001127325 \tabularnewline
-0.78544339154057 \tabularnewline
-6.11413576630202 \tabularnewline
-0.936773765022123 \tabularnewline
51.1722628541877 \tabularnewline
25.6034007756113 \tabularnewline
75.648273267973 \tabularnewline
-34.6973908916322 \tabularnewline
10.2195137298779 \tabularnewline
-18.4841878881763 \tabularnewline
-10.8855763772888 \tabularnewline
74.1733331186918 \tabularnewline
-1.35848909607401 \tabularnewline
-6.45869271660725 \tabularnewline
-37.3148618362936 \tabularnewline
-30.0158535090323 \tabularnewline
40.2104108322034 \tabularnewline
11.6125619914086 \tabularnewline
65.0658329295034 \tabularnewline
-48.291086929222 \tabularnewline
12.3811129140797 \tabularnewline
-26.8423058546248 \tabularnewline
-16.3320042453507 \tabularnewline
70.6437612819805 \tabularnewline
-11.0435768256039 \tabularnewline
-12.0171715558186 \tabularnewline
-37.0535979061938 \tabularnewline
-22.4154288573814 \tabularnewline
-10.2135232215070 \tabularnewline
8.72223670055793 \tabularnewline
42.7357374916201 \tabularnewline
-25.2945605597388 \tabularnewline
-18.5584753079700 \tabularnewline
-7.20183932004707 \tabularnewline
-29.1160714591336 \tabularnewline
73.737129806159 \tabularnewline
-22.4524358694435 \tabularnewline
-8.05098551733854 \tabularnewline
-25.6150831694156 \tabularnewline
-45.9798873901229 \tabularnewline
38.0070685466039 \tabularnewline
20.8954464069369 \tabularnewline
87.3590284768338 \tabularnewline
-36.8433038018391 \tabularnewline
-6.00028478976395 \tabularnewline
-16.6828217950041 \tabularnewline
5.44520054551831 \tabularnewline
90.3490136325772 \tabularnewline
-2.75556800036073 \tabularnewline
-15.1188908141312 \tabularnewline
17.7559613696221 \tabularnewline
-16.4604846474355 \tabularnewline
82.8462211657965 \tabularnewline
64.1866081630914 \tabularnewline
212.141801606751 \tabularnewline
-27.5162871061304 \tabularnewline
59.0925851958034 \tabularnewline
-9.33494969992572 \tabularnewline
38.8452513534614 \tabularnewline
65.8918577392988 \tabularnewline
-3.48146105285567 \tabularnewline
-27.4368126741983 \tabularnewline
-32.2175370399562 \tabularnewline
-30.5667954444818 \tabularnewline
14.0696868915968 \tabularnewline
19.9566336806135 \tabularnewline
82.1444341841618 \tabularnewline
-52.5175605305678 \tabularnewline
-21.3237648151169 \tabularnewline
-28.2367619460549 \tabularnewline
-42.9761822181632 \tabularnewline
101.610245178895 \tabularnewline
-0.498610702839564 \tabularnewline
-0.079997811920639 \tabularnewline
-63.2860147148329 \tabularnewline
-7.55096938014204 \tabularnewline
42.8766809657915 \tabularnewline
26.9560965144466 \tabularnewline
61.0727678395122 \tabularnewline
-9.59667980466775 \tabularnewline
-23.8125051304427 \tabularnewline
-60.6272470502508 \tabularnewline
-30.6038087685784 \tabularnewline
104.704868679268 \tabularnewline
-48.3380132804681 \tabularnewline
-0.392843747144909 \tabularnewline
-62.6178787075433 \tabularnewline
-15.9593324342879 \tabularnewline
14.3216276827599 \tabularnewline
-8.43637649344316 \tabularnewline
77.2693447113406 \tabularnewline
-59.9668887155805 \tabularnewline
2.24656280965031 \tabularnewline
-55.0245021329475 \tabularnewline
-6.50577456560929 \tabularnewline
42.0329845100611 \tabularnewline
24.9639061093985 \tabularnewline
-32.3035835946257 \tabularnewline
-34.6340724681287 \tabularnewline
-48.0315549651446 \tabularnewline
38.9703539428678 \tabularnewline
33.97735328507 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=30427&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C][/C][/ROW]
[ROW][C]22.3785064886151[/C][/ROW]
[ROW][C]40.9449854923112[/C][/ROW]
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[ROW][C]-34.6340724681287[/C][/ROW]
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[ROW][C]38.9703539428678[/C][/ROW]
[ROW][C]33.97735328507[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=30427&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=30427&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

22.3785064886151
40.9449854923112
-11.7150027486239
-15.1713694101681
-34.3192405750359
34.6631751993281
-2.5700365450742
-8.71495255139827
-18.7197199380602
-41.5946118328445
24.9125431097621
20.5749333157631
82.5417586414395
24.2163691190038
4.62749597678372
8.01689679873285
48.6340273815247
44.6048966919696
47.1904304522605
-24.8049727845385
-21.3029805312793
10.6098774215995
-6.62922807089883
21.6474287757540
86.2790415614575
3.29898528994313
-43.0114634321702
-36.6161689135998
-18.3718895838211
14.0785736665574
-27.6732402552926
-62.0918336579236
-33.2510892460472
-58.2897623151476
29.6124227123327
8.06447856680947
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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 = 0 ; par4 = 0 ; par5 = 4 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 4 ; 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