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R Software Module

rwasp_arimabackwardselection.wasp

Title produced by software

ARIMA Backward Selection

Date of computation

Sun, 18 Dec 2011 07:40:22 -0500

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/2011/Dec/18/t13242120658qblfvw7x6bi8wy.htm/, Retrieved Sun, 05 May 2024 08:57:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=156778, Retrieved Sun, 05 May 2024 08:57:37 +0000

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IsPrivate?

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User-defined keywords

Estimated Impact

110

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

-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMP         [Standard Deviation-Mean Plot] [Births] [2010-11-29 10:52:49] [b98453cac15ba1066b407e146608df68]
- RMP           [ARIMA Backward Selection] [Births] [2010-11-29 17:42:52] [b98453cac15ba1066b407e146608df68]
- R PD              [ARIMA Backward Selection] [ARIMA backward se...] [2011-12-18 12:40:22] [e7912d585babb6fa20e6bf5178c462ce] [Current]

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Boxplots

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	17 seconds
R Server	'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 17 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156778&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]17 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156778&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156778&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	17 seconds
R Server	'AstonUniversity' @ aston.wessa.net

ARIMA Parameter Estimation and Backward Selection
Iteration	ar1	ar2	ar3	ma1	sar1	sar2	sma1
Estimates ( 1 )	0.0561	0.0422	0.0346	-0.9261	-0.0039	-0.0165	-0.9693
(p-val)	(0.3699 )	(0.4868 )	(0.56 )	(0 )	(0.9484 )	(0.7744 )	(0 )
Estimates ( 2 )	0.0566	0.0426	0.035	-0.9266	0	-0.0156	-0.9715
(p-val)	(0.3606 )	(0.4787 )	(0.5523 )	(0 )	(NA )	(0.7807 )	(0 )
Estimates ( 3 )	0.0575	0.0432	0.0355	-0.927	0	0	-0.9796
(p-val)	(0.3536 )	(0.4731 )	(0.5468 )	(0 )	(NA )	(NA )	(0 )
Estimates ( 4 )	0.049	0.036	0	-0.917	0	0	-1.0196
(p-val)	(0.4325 )	(0.5527 )	(NA )	(0 )	(NA )	(NA )	(0 )
Estimates ( 5 )	0.0394	0	0	-0.906	0	0	-1.0184
(p-val)	(0.5255 )	(NA )	(NA )	(0 )	(NA )	(NA )	(0 )
Estimates ( 6 )	0	0	0	-1.1185	0	0	-0.9817
(p-val)	(NA )	(NA )	(NA )	(0 )	(NA )	(NA )	(0 )
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 ) & 0.0561 & 0.0422 & 0.0346 & -0.9261 & -0.0039 & -0.0165 & -0.9693 \tabularnewline
(p-val) & (0.3699 ) & (0.4868 ) & (0.56 ) & (0 ) & (0.9484 ) & (0.7744 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0.0566 & 0.0426 & 0.035 & -0.9266 & 0 & -0.0156 & -0.9715 \tabularnewline
(p-val) & (0.3606 ) & (0.4787 ) & (0.5523 ) & (0 ) & (NA ) & (0.7807 ) & (0 ) \tabularnewline
Estimates ( 3 ) & 0.0575 & 0.0432 & 0.0355 & -0.927 & 0 & 0 & -0.9796 \tabularnewline
(p-val) & (0.3536 ) & (0.4731 ) & (0.5468 ) & (0 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 4 ) & 0.049 & 0.036 & 0 & -0.917 & 0 & 0 & -1.0196 \tabularnewline
(p-val) & (0.4325 ) & (0.5527 ) & (NA ) & (0 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 5 ) & 0.0394 & 0 & 0 & -0.906 & 0 & 0 & -1.0184 \tabularnewline
(p-val) & (0.5255 ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (0 ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & -1.1185 & 0 & 0 & -0.9817 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (0 ) & (NA ) & (NA ) & (0 ) \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=156778&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]0.0561[/C][C]0.0422[/C][C]0.0346[/C][C]-0.9261[/C][C]-0.0039[/C][C]-0.0165[/C][C]-0.9693[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3699 )[/C][C](0.4868 )[/C][C](0.56 )[/C][C](0 )[/C][C](0.9484 )[/C][C](0.7744 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.0566[/C][C]0.0426[/C][C]0.035[/C][C]-0.9266[/C][C]0[/C][C]-0.0156[/C][C]-0.9715[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3606 )[/C][C](0.4787 )[/C][C](0.5523 )[/C][C](0 )[/C][C](NA )[/C][C](0.7807 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.0575[/C][C]0.0432[/C][C]0.0355[/C][C]-0.927[/C][C]0[/C][C]0[/C][C]-0.9796[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3536 )[/C][C](0.4731 )[/C][C](0.5468 )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.049[/C][C]0.036[/C][C]0[/C][C]-0.917[/C][C]0[/C][C]0[/C][C]-1.0196[/C][/ROW]
[ROW][C](p-val)[/C][C](0.4325 )[/C][C](0.5527 )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0.0394[/C][C]0[/C][C]0[/C][C]-0.906[/C][C]0[/C][C]0[/C][C]-1.0184[/C][/ROW]
[ROW][C](p-val)[/C][C](0.5255 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]-1.1185[/C][C]0[/C][C]0[/C][C]-0.9817[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0 )[/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=156778&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156778&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 )	0.0561	0.0422	0.0346	-0.9261	-0.0039	-0.0165	-0.9693
(p-val)	(0.3699 )	(0.4868 )	(0.56 )	(0 )	(0.9484 )	(0.7744 )	(0 )
Estimates ( 2 )	0.0566	0.0426	0.035	-0.9266	0	-0.0156	-0.9715
(p-val)	(0.3606 )	(0.4787 )	(0.5523 )	(0 )	(NA )	(0.7807 )	(0 )
Estimates ( 3 )	0.0575	0.0432	0.0355	-0.927	0	0	-0.9796
(p-val)	(0.3536 )	(0.4731 )	(0.5468 )	(0 )	(NA )	(NA )	(0 )
Estimates ( 4 )	0.049	0.036	0	-0.917	0	0	-1.0196
(p-val)	(0.4325 )	(0.5527 )	(NA )	(0 )	(NA )	(NA )	(0 )
Estimates ( 5 )	0.0394	0	0	-0.906	0	0	-1.0184
(p-val)	(0.5255 )	(NA )	(NA )	(0 )	(NA )	(NA )	(0 )
Estimates ( 6 )	0	0	0	-1.1185	0	0	-0.9817
(p-val)	(NA )	(NA )	(NA )	(0 )	(NA )	(NA )	(0 )
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
-0.535840518062627
1.21549511201723
4.87832398035912
11.7321174421224
50.0637236753925
42.4264575549929
63.3908498488483
35.0172807310399
20.9852710155505
50.809680559482
8.47459798085137
4.42990506399312
-5.75079610580165
-29.0431563046023
-50.2305958936168
-78.512208094887
-91.4510184406708
-88.9380456719562
-113.090954277523
-130.290186841965
-104.121894017404
-121.539799280519
-85.9460569682812
-84.7825347172502
-117.433285884726
-138.391300949184
-116.301648387101
-106.602914399674
-92.4232401157112
-83.3484519943961
-84.688227735252
-60.7617004646679
-30.5112659934986
-22.6598260116227
-10.9161770638221
-35.9893407303682
-32.378978306745
-42.3955272650928
-45.9282616878999
-24.8161157457199
-11.5958097493594
-21.7078168018189
-17.2335528963209
4.43836297694243
-1.63984371189186
-7.11971175732917
-9.46991890978764
-15.534334377159
-2.03582586596016
-36.1503952488456
-20.1205685065517
3.57188028117465
-4.18717985640402
-17.2749382950526
-20.2937647150517
-4.54097581683334
18.2197303573307
28.9372758873023
52.8397204964385
76.6625502320767
134.066888686668
142.586664512821
153.541280553825
144.04890689971
127.575985929128
87.6102721672665
77.5021013609627
80.527608316001
78.4668496324115
40.1299810738001
36.7584956465191
16.9960554689216
19.4033908761152
-9.10844012026937
-14.2864818921655
-1.33070632351048
-29.5181022264589
-32.683023725702
-52.7120310696705
-29.993610943284
-37.3854842843335
-18.2231777351929
-11.6130675526072
-8.94829002918059
-22.6101696291972
-31.341564882405
-8.46352925469939
-21.7747789147224
8.60396300055804
16.8991382208816
-2.45502917463113
-23.281859996935
-28.2962757600327
-23.8242403221159
7.10042661615839
6.4890495296025
0.803467553246508
-23.1917033486332
-27.6445157273169
-18.7931437314026
-0.0224022858323623
19.3246752465319
-7.53089190600708
-6.51128612057661
2.96050549175181
17.1617759527487
55.0816962364789
62.8824472830373
107.282401812255
145.890022862777
151.310064092016
151.010607723257
128.832859866994
124.992663430073
106.258828369845
79.4032740663447
30.8691659071694
17.5028998692329
-6.60578290302349
15.4381790755072
8.69737802263213
-5.22902966333075
-26.1260612718546
-70.4509029334429
-68.2237384816108
-48.1317670832428
-54.7523404528753
-42.5677424929688
-39.164526521747
-13.8580763731038
0.767072010889928
-18.8676337456012
-21.1068910569086
-55.1101747527249
-4.09369090590178
-31.8734147950864
-33.2797066041624
14.5939790691817
-9.09397117245895
6.6981902150271
-18.0398615006073
22.118156662757
33.9431205426392
71.4640580098834
84.3203413923216
94.5080935947169
77.1524187284615
47.6686446953495
39.9981148025591
63.1921343683515
31.0440061133119
8.95533634567676
-13.8210871239041
-14.4196725619283
-32.2994473733775
-28.7292402332288
-33.8067154630694
-56.3548148126668
-50.006803219038
-58.8310851357214
-27.2868564690324
-36.6210229193365
-59.6932473855509
-20.6402079429824
-39.2731753238226
-42.1902691962276
-16.3097310535908
-22.1508675237836
0.670248085973016
12.1218169907732
-15.4393489423874
-24.5826927897613
-10.7947370665567
11.9901259532122
-17.5058164611295
-22.7062707332128
-28.5035649552642
-18.4874067150332
0.477118717991074
-16.319093562972
-8.09702968829098
-24.8059184798556
-27.6438366882126
-30.4173413018519
-41.8692588651898
14.207112166443
-49.8971844772995
-25.170666757631
-31.6146185568266
-20.5950086231279
-35.5880250371749
-31.1717176229487
-41.4878257443493
-25.5510964504321
-51.8996556004019
-33.1756310586446
-41.8295430621051
3.18772175436924
-41.4374131495072
-32.7272643635439
-40.6793596771511
-35.7887837819744
-42.4149087539708
-56.0141218744194
-69.7309415102428
-86.749965861772
-70.202986988482
-56.972373933156
-29.6690992658474
6.36601423507113
-27.6275040984434
-17.4449536349405
-25.8725846231925
-13.3036599890114
-28.1126865545631
-23.9054462181876
-36.5474727706896
-40.1849140384862
-39.8142293121587
-32.2855435220969
-34.3194926029474
37.1949871852952
16.3842530251182
13.8672250110387
29.465387218385
44.4143354647073
8.93773192343325
-15.7604943471265
-40.8943748607664
-25.0759207764326
-39.0404671928057
-45.583985609817
-43.7059419471845
38.7657786533408
16.3159894014722
0.598627628879926
5.71550823003936
7.57484055994859
-11.1450165197534
-30.7622767758219
-43.2864739534594
-44.1701356667254
-37.3101891839759
-21.6881875731433
-28.1561620791176
27.8727785022722
26.5806498229583
22.7224527612518
49.4595583499888
44.0868361814579
3.93383410908303
-8.09825110654758
8.83042997230058
34.914725710898
44.0009429661525
53.2593628119167
47.1852150691449
112.202788814582
106.897281250275
97.4770704316877
114.190429142804
111.044663883534
112.213778323025
99.5422140669102
104.892354752612
85.8181061906891
70.9605363608139
48.799392798534
31.5542631435379
77.1193739624436
75.5110364095776
71.0584698450375
60.6316447467535
40.3472991145502
38.5767781154569
18.888150135374
29.130667225995
11.6213056761877
11.0950491148439
-8.59825380171768
-24.8773535167793
22.7506439784219
17.0584555952409
13.7964213326318
4075.87386579611
-538.928204522335
-369.789720937534
-351.563839614545
-326.30421469803
-288.935342319811
-275.083973026625
-249.010411123427
-244.727672490351
-181.48769548871
-171.928080577486
-159.675771587916
-303.71730561552
-122.377049097245
-108.798744582355
-101.587971151011
-61.8559488226977
-54.9531789159324
-67.5568458905434
-72.1342366000019
-62.1454781942648
-0.321916292969715
8.94018507146619
0.435351367392202
-117.164223577251
62.7468182396018
91.6746197533755
119.494713049771
244.862834081308
218.150304252403
222.741838438762
181.290213687121
163.704841126946
174.032977706168
144.304960782514
110.866391863628
-59.6981630604458
98.406867064136
59.5305054968732
47.0474613139442
69.0856707253691
37.0774467462074
4.59376666199421
-22.700429192587
-58.4865316649366
12.6593032416754
24.4993075718386
27.0969537464735
-142.343257769662
26.4733218541094
21.9016215776778
8.57188753737274
18.5352615923312
31.9254417094602
-4.92000968824316
-64.4865017199168
-77.1430586156083
-12.0790146094417
-40.416903706264
-23.317666193187
-184.45555284072
-19.5620179379602
-32.9627720331977
-78.4131856125704
-40.518192429105
-63.6039893609036
-60.1052393009666
-94.2682615010123
-86.1026001215529
-65.1235865595885
-25.0202825536505
-43.6772705264009
-177.69018209218
-31.2241443042801
-38.2334139119461
-26.9152837431824

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
-0.535840518062627 \tabularnewline
1.21549511201723 \tabularnewline
4.87832398035912 \tabularnewline
11.7321174421224 \tabularnewline
50.0637236753925 \tabularnewline
42.4264575549929 \tabularnewline
63.3908498488483 \tabularnewline
35.0172807310399 \tabularnewline
20.9852710155505 \tabularnewline
50.809680559482 \tabularnewline
8.47459798085137 \tabularnewline
4.42990506399312 \tabularnewline
-5.75079610580165 \tabularnewline
-29.0431563046023 \tabularnewline
-50.2305958936168 \tabularnewline
-78.512208094887 \tabularnewline
-91.4510184406708 \tabularnewline
-88.9380456719562 \tabularnewline
-113.090954277523 \tabularnewline
-130.290186841965 \tabularnewline
-104.121894017404 \tabularnewline
-121.539799280519 \tabularnewline
-85.9460569682812 \tabularnewline
-84.7825347172502 \tabularnewline
-117.433285884726 \tabularnewline
-138.391300949184 \tabularnewline
-116.301648387101 \tabularnewline
-106.602914399674 \tabularnewline
-92.4232401157112 \tabularnewline
-83.3484519943961 \tabularnewline
-84.688227735252 \tabularnewline
-60.7617004646679 \tabularnewline
-30.5112659934986 \tabularnewline
-22.6598260116227 \tabularnewline
-10.9161770638221 \tabularnewline
-35.9893407303682 \tabularnewline
-32.378978306745 \tabularnewline
-42.3955272650928 \tabularnewline
-45.9282616878999 \tabularnewline
-24.8161157457199 \tabularnewline
-11.5958097493594 \tabularnewline
-21.7078168018189 \tabularnewline
-17.2335528963209 \tabularnewline
4.43836297694243 \tabularnewline
-1.63984371189186 \tabularnewline
-7.11971175732917 \tabularnewline
-9.46991890978764 \tabularnewline
-15.534334377159 \tabularnewline
-2.03582586596016 \tabularnewline
-36.1503952488456 \tabularnewline
-20.1205685065517 \tabularnewline
3.57188028117465 \tabularnewline
-4.18717985640402 \tabularnewline
-17.2749382950526 \tabularnewline
-20.2937647150517 \tabularnewline
-4.54097581683334 \tabularnewline
18.2197303573307 \tabularnewline
28.9372758873023 \tabularnewline
52.8397204964385 \tabularnewline
76.6625502320767 \tabularnewline
134.066888686668 \tabularnewline
142.586664512821 \tabularnewline
153.541280553825 \tabularnewline
144.04890689971 \tabularnewline
127.575985929128 \tabularnewline
87.6102721672665 \tabularnewline
77.5021013609627 \tabularnewline
80.527608316001 \tabularnewline
78.4668496324115 \tabularnewline
40.1299810738001 \tabularnewline
36.7584956465191 \tabularnewline
16.9960554689216 \tabularnewline
19.4033908761152 \tabularnewline
-9.10844012026937 \tabularnewline
-14.2864818921655 \tabularnewline
-1.33070632351048 \tabularnewline
-29.5181022264589 \tabularnewline
-32.683023725702 \tabularnewline
-52.7120310696705 \tabularnewline
-29.993610943284 \tabularnewline
-37.3854842843335 \tabularnewline
-18.2231777351929 \tabularnewline
-11.6130675526072 \tabularnewline
-8.94829002918059 \tabularnewline
-22.6101696291972 \tabularnewline
-31.341564882405 \tabularnewline
-8.46352925469939 \tabularnewline
-21.7747789147224 \tabularnewline
8.60396300055804 \tabularnewline
16.8991382208816 \tabularnewline
-2.45502917463113 \tabularnewline
-23.281859996935 \tabularnewline
-28.2962757600327 \tabularnewline
-23.8242403221159 \tabularnewline
7.10042661615839 \tabularnewline
6.4890495296025 \tabularnewline
0.803467553246508 \tabularnewline
-23.1917033486332 \tabularnewline
-27.6445157273169 \tabularnewline
-18.7931437314026 \tabularnewline
-0.0224022858323623 \tabularnewline
19.3246752465319 \tabularnewline
-7.53089190600708 \tabularnewline
-6.51128612057661 \tabularnewline
2.96050549175181 \tabularnewline
17.1617759527487 \tabularnewline
55.0816962364789 \tabularnewline
62.8824472830373 \tabularnewline
107.282401812255 \tabularnewline
145.890022862777 \tabularnewline
151.310064092016 \tabularnewline
151.010607723257 \tabularnewline
128.832859866994 \tabularnewline
124.992663430073 \tabularnewline
106.258828369845 \tabularnewline
79.4032740663447 \tabularnewline
30.8691659071694 \tabularnewline
17.5028998692329 \tabularnewline
-6.60578290302349 \tabularnewline
15.4381790755072 \tabularnewline
8.69737802263213 \tabularnewline
-5.22902966333075 \tabularnewline
-26.1260612718546 \tabularnewline
-70.4509029334429 \tabularnewline
-68.2237384816108 \tabularnewline
-48.1317670832428 \tabularnewline
-54.7523404528753 \tabularnewline
-42.5677424929688 \tabularnewline
-39.164526521747 \tabularnewline
-13.8580763731038 \tabularnewline
0.767072010889928 \tabularnewline
-18.8676337456012 \tabularnewline
-21.1068910569086 \tabularnewline
-55.1101747527249 \tabularnewline
-4.09369090590178 \tabularnewline
-31.8734147950864 \tabularnewline
-33.2797066041624 \tabularnewline
14.5939790691817 \tabularnewline
-9.09397117245895 \tabularnewline
6.6981902150271 \tabularnewline
-18.0398615006073 \tabularnewline
22.118156662757 \tabularnewline
33.9431205426392 \tabularnewline
71.4640580098834 \tabularnewline
84.3203413923216 \tabularnewline
94.5080935947169 \tabularnewline
77.1524187284615 \tabularnewline
47.6686446953495 \tabularnewline
39.9981148025591 \tabularnewline
63.1921343683515 \tabularnewline
31.0440061133119 \tabularnewline
8.95533634567676 \tabularnewline
-13.8210871239041 \tabularnewline
-14.4196725619283 \tabularnewline
-32.2994473733775 \tabularnewline
-28.7292402332288 \tabularnewline
-33.8067154630694 \tabularnewline
-56.3548148126668 \tabularnewline
-50.006803219038 \tabularnewline
-58.8310851357214 \tabularnewline
-27.2868564690324 \tabularnewline
-36.6210229193365 \tabularnewline
-59.6932473855509 \tabularnewline
-20.6402079429824 \tabularnewline
-39.2731753238226 \tabularnewline
-42.1902691962276 \tabularnewline
-16.3097310535908 \tabularnewline
-22.1508675237836 \tabularnewline
0.670248085973016 \tabularnewline
12.1218169907732 \tabularnewline
-15.4393489423874 \tabularnewline
-24.5826927897613 \tabularnewline
-10.7947370665567 \tabularnewline
11.9901259532122 \tabularnewline
-17.5058164611295 \tabularnewline
-22.7062707332128 \tabularnewline
-28.5035649552642 \tabularnewline
-18.4874067150332 \tabularnewline
0.477118717991074 \tabularnewline
-16.319093562972 \tabularnewline
-8.09702968829098 \tabularnewline
-24.8059184798556 \tabularnewline
-27.6438366882126 \tabularnewline
-30.4173413018519 \tabularnewline
-41.8692588651898 \tabularnewline
14.207112166443 \tabularnewline
-49.8971844772995 \tabularnewline
-25.170666757631 \tabularnewline
-31.6146185568266 \tabularnewline
-20.5950086231279 \tabularnewline
-35.5880250371749 \tabularnewline
-31.1717176229487 \tabularnewline
-41.4878257443493 \tabularnewline
-25.5510964504321 \tabularnewline
-51.8996556004019 \tabularnewline
-33.1756310586446 \tabularnewline
-41.8295430621051 \tabularnewline
3.18772175436924 \tabularnewline
-41.4374131495072 \tabularnewline
-32.7272643635439 \tabularnewline
-40.6793596771511 \tabularnewline
-35.7887837819744 \tabularnewline
-42.4149087539708 \tabularnewline
-56.0141218744194 \tabularnewline
-69.7309415102428 \tabularnewline
-86.749965861772 \tabularnewline
-70.202986988482 \tabularnewline
-56.972373933156 \tabularnewline
-29.6690992658474 \tabularnewline
6.36601423507113 \tabularnewline
-27.6275040984434 \tabularnewline
-17.4449536349405 \tabularnewline
-25.8725846231925 \tabularnewline
-13.3036599890114 \tabularnewline
-28.1126865545631 \tabularnewline
-23.9054462181876 \tabularnewline
-36.5474727706896 \tabularnewline
-40.1849140384862 \tabularnewline
-39.8142293121587 \tabularnewline
-32.2855435220969 \tabularnewline
-34.3194926029474 \tabularnewline
37.1949871852952 \tabularnewline
16.3842530251182 \tabularnewline
13.8672250110387 \tabularnewline
29.465387218385 \tabularnewline
44.4143354647073 \tabularnewline
8.93773192343325 \tabularnewline
-15.7604943471265 \tabularnewline
-40.8943748607664 \tabularnewline
-25.0759207764326 \tabularnewline
-39.0404671928057 \tabularnewline
-45.583985609817 \tabularnewline
-43.7059419471845 \tabularnewline
38.7657786533408 \tabularnewline
16.3159894014722 \tabularnewline
0.598627628879926 \tabularnewline
5.71550823003936 \tabularnewline
7.57484055994859 \tabularnewline
-11.1450165197534 \tabularnewline
-30.7622767758219 \tabularnewline
-43.2864739534594 \tabularnewline
-44.1701356667254 \tabularnewline
-37.3101891839759 \tabularnewline
-21.6881875731433 \tabularnewline
-28.1561620791176 \tabularnewline
27.8727785022722 \tabularnewline
26.5806498229583 \tabularnewline
22.7224527612518 \tabularnewline
49.4595583499888 \tabularnewline
44.0868361814579 \tabularnewline
3.93383410908303 \tabularnewline
-8.09825110654758 \tabularnewline
8.83042997230058 \tabularnewline
34.914725710898 \tabularnewline
44.0009429661525 \tabularnewline
53.2593628119167 \tabularnewline
47.1852150691449 \tabularnewline
112.202788814582 \tabularnewline
106.897281250275 \tabularnewline
97.4770704316877 \tabularnewline
114.190429142804 \tabularnewline
111.044663883534 \tabularnewline
112.213778323025 \tabularnewline
99.5422140669102 \tabularnewline
104.892354752612 \tabularnewline
85.8181061906891 \tabularnewline
70.9605363608139 \tabularnewline
48.799392798534 \tabularnewline
31.5542631435379 \tabularnewline
77.1193739624436 \tabularnewline
75.5110364095776 \tabularnewline
71.0584698450375 \tabularnewline
60.6316447467535 \tabularnewline
40.3472991145502 \tabularnewline
38.5767781154569 \tabularnewline
18.888150135374 \tabularnewline
29.130667225995 \tabularnewline
11.6213056761877 \tabularnewline
11.0950491148439 \tabularnewline
-8.59825380171768 \tabularnewline
-24.8773535167793 \tabularnewline
22.7506439784219 \tabularnewline
17.0584555952409 \tabularnewline
13.7964213326318 \tabularnewline
4075.87386579611 \tabularnewline
-538.928204522335 \tabularnewline
-369.789720937534 \tabularnewline
-351.563839614545 \tabularnewline
-326.30421469803 \tabularnewline
-288.935342319811 \tabularnewline
-275.083973026625 \tabularnewline
-249.010411123427 \tabularnewline
-244.727672490351 \tabularnewline
-181.48769548871 \tabularnewline
-171.928080577486 \tabularnewline
-159.675771587916 \tabularnewline
-303.71730561552 \tabularnewline
-122.377049097245 \tabularnewline
-108.798744582355 \tabularnewline
-101.587971151011 \tabularnewline
-61.8559488226977 \tabularnewline
-54.9531789159324 \tabularnewline
-67.5568458905434 \tabularnewline
-72.1342366000019 \tabularnewline
-62.1454781942648 \tabularnewline
-0.321916292969715 \tabularnewline
8.94018507146619 \tabularnewline
0.435351367392202 \tabularnewline
-117.164223577251 \tabularnewline
62.7468182396018 \tabularnewline
91.6746197533755 \tabularnewline
119.494713049771 \tabularnewline
244.862834081308 \tabularnewline
218.150304252403 \tabularnewline
222.741838438762 \tabularnewline
181.290213687121 \tabularnewline
163.704841126946 \tabularnewline
174.032977706168 \tabularnewline
144.304960782514 \tabularnewline
110.866391863628 \tabularnewline
-59.6981630604458 \tabularnewline
98.406867064136 \tabularnewline
59.5305054968732 \tabularnewline
47.0474613139442 \tabularnewline
69.0856707253691 \tabularnewline
37.0774467462074 \tabularnewline
4.59376666199421 \tabularnewline
-22.700429192587 \tabularnewline
-58.4865316649366 \tabularnewline
12.6593032416754 \tabularnewline
24.4993075718386 \tabularnewline
27.0969537464735 \tabularnewline
-142.343257769662 \tabularnewline
26.4733218541094 \tabularnewline
21.9016215776778 \tabularnewline
8.57188753737274 \tabularnewline
18.5352615923312 \tabularnewline
31.9254417094602 \tabularnewline
-4.92000968824316 \tabularnewline
-64.4865017199168 \tabularnewline
-77.1430586156083 \tabularnewline
-12.0790146094417 \tabularnewline
-40.416903706264 \tabularnewline
-23.317666193187 \tabularnewline
-184.45555284072 \tabularnewline
-19.5620179379602 \tabularnewline
-32.9627720331977 \tabularnewline
-78.4131856125704 \tabularnewline
-40.518192429105 \tabularnewline
-63.6039893609036 \tabularnewline
-60.1052393009666 \tabularnewline
-94.2682615010123 \tabularnewline
-86.1026001215529 \tabularnewline
-65.1235865595885 \tabularnewline
-25.0202825536505 \tabularnewline
-43.6772705264009 \tabularnewline
-177.69018209218 \tabularnewline
-31.2241443042801 \tabularnewline
-38.2334139119461 \tabularnewline
-26.9152837431824 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=156778&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]-0.535840518062627[/C][/ROW]
[ROW][C]1.21549511201723[/C][/ROW]
[ROW][C]4.87832398035912[/C][/ROW]
[ROW][C]11.7321174421224[/C][/ROW]
[ROW][C]50.0637236753925[/C][/ROW]
[ROW][C]42.4264575549929[/C][/ROW]
[ROW][C]63.3908498488483[/C][/ROW]
[ROW][C]35.0172807310399[/C][/ROW]
[ROW][C]20.9852710155505[/C][/ROW]
[ROW][C]50.809680559482[/C][/ROW]
[ROW][C]8.47459798085137[/C][/ROW]
[ROW][C]4.42990506399312[/C][/ROW]
[ROW][C]-5.75079610580165[/C][/ROW]
[ROW][C]-29.0431563046023[/C][/ROW]
[ROW][C]-50.2305958936168[/C][/ROW]
[ROW][C]-78.512208094887[/C][/ROW]
[ROW][C]-91.4510184406708[/C][/ROW]
[ROW][C]-88.9380456719562[/C][/ROW]
[ROW][C]-113.090954277523[/C][/ROW]
[ROW][C]-130.290186841965[/C][/ROW]
[ROW][C]-104.121894017404[/C][/ROW]
[ROW][C]-121.539799280519[/C][/ROW]
[ROW][C]-85.9460569682812[/C][/ROW]
[ROW][C]-84.7825347172502[/C][/ROW]
[ROW][C]-117.433285884726[/C][/ROW]
[ROW][C]-138.391300949184[/C][/ROW]
[ROW][C]-116.301648387101[/C][/ROW]
[ROW][C]-106.602914399674[/C][/ROW]
[ROW][C]-92.4232401157112[/C][/ROW]
[ROW][C]-83.3484519943961[/C][/ROW]
[ROW][C]-84.688227735252[/C][/ROW]
[ROW][C]-60.7617004646679[/C][/ROW]
[ROW][C]-30.5112659934986[/C][/ROW]
[ROW][C]-22.6598260116227[/C][/ROW]
[ROW][C]-10.9161770638221[/C][/ROW]
[ROW][C]-35.9893407303682[/C][/ROW]
[ROW][C]-32.378978306745[/C][/ROW]
[ROW][C]-42.3955272650928[/C][/ROW]
[ROW][C]-45.9282616878999[/C][/ROW]
[ROW][C]-24.8161157457199[/C][/ROW]
[ROW][C]-11.5958097493594[/C][/ROW]
[ROW][C]-21.7078168018189[/C][/ROW]
[ROW][C]-17.2335528963209[/C][/ROW]
[ROW][C]4.43836297694243[/C][/ROW]
[ROW][C]-1.63984371189186[/C][/ROW]
[ROW][C]-7.11971175732917[/C][/ROW]
[ROW][C]-9.46991890978764[/C][/ROW]
[ROW][C]-15.534334377159[/C][/ROW]
[ROW][C]-2.03582586596016[/C][/ROW]
[ROW][C]-36.1503952488456[/C][/ROW]
[ROW][C]-20.1205685065517[/C][/ROW]
[ROW][C]3.57188028117465[/C][/ROW]
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[ROW][C]-38.2334139119461[/C][/ROW]
[ROW][C]-26.9152837431824[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=156778&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=156778&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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1.21549511201723
4.87832398035912
11.7321174421224
50.0637236753925
42.4264575549929
63.3908498488483
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-91.4510184406708
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-113.090954277523
-130.290186841965
-104.121894017404
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-42.3955272650928
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-11.5958097493594
-21.7078168018189
-17.2335528963209
4.43836297694243
-1.63984371189186
-7.11971175732917
-9.46991890978764
-15.534334377159
-2.03582586596016
-36.1503952488456
-20.1205685065517
3.57188028117465
-4.18717985640402
-17.2749382950526
-20.2937647150517
-4.54097581683334
18.2197303573307
28.9372758873023
52.8397204964385
76.6625502320767
134.066888686668
142.586664512821
153.541280553825
144.04890689971
127.575985929128
87.6102721672665
77.5021013609627
80.527608316001
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40.1299810738001
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19.4033908761152
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151.010607723257
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0.767072010889928
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14.5939790691817
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6.6981902150271
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22.118156662757
33.9431205426392
71.4640580098834
84.3203413923216
94.5080935947169
77.1524187284615
47.6686446953495
39.9981148025591
63.1921343683515
31.0440061133119
8.95533634567676
-13.8210871239041
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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 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

Parameters (R input):

par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

R code (references can be found in the software module):

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code