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of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_arimabackwardselection.wasp
Title produced by softwareARIMA Backward Selection
Date of computationWed, 17 Dec 2014 13:39:11 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/17/t1418823611rd45be1tu3nlz44.htm/, Retrieved Thu, 16 May 2024 18:10:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=270241, Retrieved Thu, 16 May 2024 18:10:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact56
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [ARIMA Backward Selection] [arima backward] [2014-12-17 13:39:11] [ec1b40d1a9751af99658fe8fca4f9eca] [Current]
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Dataseries X:
12.9
12.2
12.8
7.4
6.7
12.6
14.8
13.3
11.1
8.2
11.4
6.4
10.6
12
6.3
11.3
11.9
9.3
9.6
10
6.4
13.8
10.8
13.8
11.7
10.9
16.1
13.4
9.9
11.5
8.3
11.7
9
9.7
10.8
10.3
10.4
12.7
9.3
11.8
5.9
11.4
13
10.8
12.3
11.3
11.8
7.9
12.7
12.3
11.6
6.7
10.9
12.1
13.3
10.1
5.7
14.3
8
13.3
9.3
12.5
7.6
15.9
9.2
9.1
11.1
13
14.5
12.2
12.3
11.4
8.8
14.6
12.6
13
12.6
13.2
9.9
7.7
10.5
13.4
10.9
4.3
10.3
11.8
11.2
11.4
8.6
13.2
12.6
5.6
9.9
8.8
7.7
9
7.3
11.4
13.6
7.9
10.7
10.3
8.3
9.6
14.2
8.5
13.5
4.9
6.4
9.6
11.6
11.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270241&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270241&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270241&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1
Estimates ( 1 )-0.50830.9920.51621
(p-val)(0 )(0 )(0 )(0 )
Estimates ( 2 )00.57450.39790.3351
(p-val)(NA )(0 )(0 )(0.0028 )
Estimates ( 3 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANA
(p-val)(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 \tabularnewline
Estimates ( 1 ) & -0.5083 & 0.992 & 0.5162 & 1 \tabularnewline
(p-val) & (0 ) & (0 ) & (0 ) & (0 ) \tabularnewline
Estimates ( 2 ) & 0 & 0.5745 & 0.3979 & 0.3351 \tabularnewline
(p-val) & (NA ) & (0 ) & (0 ) & (0.0028 ) \tabularnewline
Estimates ( 3 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 4 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270241&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][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.5083[/C][C]0.992[/C][C]0.5162[/C][C]1[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][C](0 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0[/C][C]0.5745[/C][C]0.3979[/C][C]0.3351[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](0.0028 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/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][/ROW]
[ROW][C]Estimates ( 4 )[/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][/ROW]
[ROW][C]Estimates ( 5 )[/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][/ROW]
[ROW][C]Estimates ( 6 )[/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][/ROW]
[ROW][C]Estimates ( 7 )[/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][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270241&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270241&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
Iterationar1ar2ar3ma1
Estimates ( 1 )-0.50830.9920.51621
(p-val)(0 )(0 )(0 )(0 )
Estimates ( 2 )00.57450.39790.3351
(p-val)(NA )(0 )(0 )(0.0028 )
Estimates ( 3 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 4 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 5 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANA
(p-val)(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANA
(p-val)(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.101047520244773
-0.598290538008299
0.240717559766546
-5.07502396672215
-3.46596474175262
5.48728224506136
5.25147772935666
-0.365129088048725
-2.9509948537149
-4.03678474699818
1.70554353557883
-3.35637913814204
1.64831708428995
3.49883421297039
-4.88757297772448
1.98809967426909
3.20680789763763
-2.3003606870837
-1.01562436203127
0.520872654357301
-3.348523959078
5.4942987413787
0.825368607448018
1.46792072290071
-0.584099559877515
-1.82987354838692
4.71734872304972
0.0291184758948047
-4.90001535632311
-0.187922549041463
-2.39774436934053
1.78379951897679
-0.997986024162058
-0.620082903601443
1.37606433236041
0.148720902124692
-0.233389711273385
2.41811375123081
-2.28218479391573
0.832012027947789
-4.69873555403637
2.56831716357019
4.27668193236512
-1.20714344816596
0.211048540694087
-0.0829231146347885
-0.150366982715922
-3.50121813426095
2.65921161541432
2.1691538672451
-0.984981090401227
-5.16460042097535
1.59357131797001
3.40691279597887
1.77613777872188
-2.53399934816038
-6.0625246893897
6.33974865135055
-1.91641802897427
2.1427610878425
-1.39076088445121
1.28661063768256
-3.41162157893602
5.94785766532979
-2.63489845748288
-3.28380251192823
1.66162205099291
3.1995774044176
2.20163175968498
-1.24882663664644
-1.34508296991178
-0.592798024822048
-3.30548286480658
4.69916339011195
0.717352736444453
-0.336727365214928
-0.491429981862198
0.692120915697467
-3.28310081438428
-3.58321752781721
1.34969011429344
4.63003832972732
-1.2956454234843
-7.56216482225701
2.2934184020231
4.84336359479808
-0.0714959106085093
0.140776420935931
-2.94098692272781
3.40636301558854
1.4912819575459
-7.00117716338345
0.416595445749149
1.35370292134219
-1.88524352362777
0.952104932306015
-1.25374459224973
3.45027153650374
4.05505663866231
-4.30099112871876
-0.368925292936352
1.24649715431094
-2.3958617261413
0.466848289588546
5.05166709080528
-3.12404785138481
1.8908554023434
-5.87434875124186
-3.02157107254867
4.02961089847573
3.57240487931667
0.602746695669408

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
 \tabularnewline
0.101047520244773 \tabularnewline
-0.598290538008299 \tabularnewline
0.240717559766546 \tabularnewline
-5.07502396672215 \tabularnewline
-3.46596474175262 \tabularnewline
5.48728224506136 \tabularnewline
5.25147772935666 \tabularnewline
-0.365129088048725 \tabularnewline
-2.9509948537149 \tabularnewline
-4.03678474699818 \tabularnewline
1.70554353557883 \tabularnewline
-3.35637913814204 \tabularnewline
1.64831708428995 \tabularnewline
3.49883421297039 \tabularnewline
-4.88757297772448 \tabularnewline
1.98809967426909 \tabularnewline
3.20680789763763 \tabularnewline
-2.3003606870837 \tabularnewline
-1.01562436203127 \tabularnewline
0.520872654357301 \tabularnewline
-3.348523959078 \tabularnewline
5.4942987413787 \tabularnewline
0.825368607448018 \tabularnewline
1.46792072290071 \tabularnewline
-0.584099559877515 \tabularnewline
-1.82987354838692 \tabularnewline
4.71734872304972 \tabularnewline
0.0291184758948047 \tabularnewline
-4.90001535632311 \tabularnewline
-0.187922549041463 \tabularnewline
-2.39774436934053 \tabularnewline
1.78379951897679 \tabularnewline
-0.997986024162058 \tabularnewline
-0.620082903601443 \tabularnewline
1.37606433236041 \tabularnewline
0.148720902124692 \tabularnewline
-0.233389711273385 \tabularnewline
2.41811375123081 \tabularnewline
-2.28218479391573 \tabularnewline
0.832012027947789 \tabularnewline
-4.69873555403637 \tabularnewline
2.56831716357019 \tabularnewline
4.27668193236512 \tabularnewline
-1.20714344816596 \tabularnewline
0.211048540694087 \tabularnewline
-0.0829231146347885 \tabularnewline
-0.150366982715922 \tabularnewline
-3.50121813426095 \tabularnewline
2.65921161541432 \tabularnewline
2.1691538672451 \tabularnewline
-0.984981090401227 \tabularnewline
-5.16460042097535 \tabularnewline
1.59357131797001 \tabularnewline
3.40691279597887 \tabularnewline
1.77613777872188 \tabularnewline
-2.53399934816038 \tabularnewline
-6.0625246893897 \tabularnewline
6.33974865135055 \tabularnewline
-1.91641802897427 \tabularnewline
2.1427610878425 \tabularnewline
-1.39076088445121 \tabularnewline
1.28661063768256 \tabularnewline
-3.41162157893602 \tabularnewline
5.94785766532979 \tabularnewline
-2.63489845748288 \tabularnewline
-3.28380251192823 \tabularnewline
1.66162205099291 \tabularnewline
3.1995774044176 \tabularnewline
2.20163175968498 \tabularnewline
-1.24882663664644 \tabularnewline
-1.34508296991178 \tabularnewline
-0.592798024822048 \tabularnewline
-3.30548286480658 \tabularnewline
4.69916339011195 \tabularnewline
0.717352736444453 \tabularnewline
-0.336727365214928 \tabularnewline
-0.491429981862198 \tabularnewline
0.692120915697467 \tabularnewline
-3.28310081438428 \tabularnewline
-3.58321752781721 \tabularnewline
1.34969011429344 \tabularnewline
4.63003832972732 \tabularnewline
-1.2956454234843 \tabularnewline
-7.56216482225701 \tabularnewline
2.2934184020231 \tabularnewline
4.84336359479808 \tabularnewline
-0.0714959106085093 \tabularnewline
0.140776420935931 \tabularnewline
-2.94098692272781 \tabularnewline
3.40636301558854 \tabularnewline
1.4912819575459 \tabularnewline
-7.00117716338345 \tabularnewline
0.416595445749149 \tabularnewline
1.35370292134219 \tabularnewline
-1.88524352362777 \tabularnewline
0.952104932306015 \tabularnewline
-1.25374459224973 \tabularnewline
3.45027153650374 \tabularnewline
4.05505663866231 \tabularnewline
-4.30099112871876 \tabularnewline
-0.368925292936352 \tabularnewline
1.24649715431094 \tabularnewline
-2.3958617261413 \tabularnewline
0.466848289588546 \tabularnewline
5.05166709080528 \tabularnewline
-3.12404785138481 \tabularnewline
1.8908554023434 \tabularnewline
-5.87434875124186 \tabularnewline
-3.02157107254867 \tabularnewline
4.02961089847573 \tabularnewline
3.57240487931667 \tabularnewline
0.602746695669408 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=270241&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C][/C][/ROW]
[ROW][C]0.101047520244773[/C][/ROW]
[ROW][C]-0.598290538008299[/C][/ROW]
[ROW][C]0.240717559766546[/C][/ROW]
[ROW][C]-5.07502396672215[/C][/ROW]
[ROW][C]-3.46596474175262[/C][/ROW]
[ROW][C]5.48728224506136[/C][/ROW]
[ROW][C]5.25147772935666[/C][/ROW]
[ROW][C]-0.365129088048725[/C][/ROW]
[ROW][C]-2.9509948537149[/C][/ROW]
[ROW][C]-4.03678474699818[/C][/ROW]
[ROW][C]1.70554353557883[/C][/ROW]
[ROW][C]-3.35637913814204[/C][/ROW]
[ROW][C]1.64831708428995[/C][/ROW]
[ROW][C]3.49883421297039[/C][/ROW]
[ROW][C]-4.88757297772448[/C][/ROW]
[ROW][C]1.98809967426909[/C][/ROW]
[ROW][C]3.20680789763763[/C][/ROW]
[ROW][C]-2.3003606870837[/C][/ROW]
[ROW][C]-1.01562436203127[/C][/ROW]
[ROW][C]0.520872654357301[/C][/ROW]
[ROW][C]-3.348523959078[/C][/ROW]
[ROW][C]5.4942987413787[/C][/ROW]
[ROW][C]0.825368607448018[/C][/ROW]
[ROW][C]1.46792072290071[/C][/ROW]
[ROW][C]-0.584099559877515[/C][/ROW]
[ROW][C]-1.82987354838692[/C][/ROW]
[ROW][C]4.71734872304972[/C][/ROW]
[ROW][C]0.0291184758948047[/C][/ROW]
[ROW][C]-4.90001535632311[/C][/ROW]
[ROW][C]-0.187922549041463[/C][/ROW]
[ROW][C]-2.39774436934053[/C][/ROW]
[ROW][C]1.78379951897679[/C][/ROW]
[ROW][C]-0.997986024162058[/C][/ROW]
[ROW][C]-0.620082903601443[/C][/ROW]
[ROW][C]1.37606433236041[/C][/ROW]
[ROW][C]0.148720902124692[/C][/ROW]
[ROW][C]-0.233389711273385[/C][/ROW]
[ROW][C]2.41811375123081[/C][/ROW]
[ROW][C]-2.28218479391573[/C][/ROW]
[ROW][C]0.832012027947789[/C][/ROW]
[ROW][C]-4.69873555403637[/C][/ROW]
[ROW][C]2.56831716357019[/C][/ROW]
[ROW][C]4.27668193236512[/C][/ROW]
[ROW][C]-1.20714344816596[/C][/ROW]
[ROW][C]0.211048540694087[/C][/ROW]
[ROW][C]-0.0829231146347885[/C][/ROW]
[ROW][C]-0.150366982715922[/C][/ROW]
[ROW][C]-3.50121813426095[/C][/ROW]
[ROW][C]2.65921161541432[/C][/ROW]
[ROW][C]2.1691538672451[/C][/ROW]
[ROW][C]-0.984981090401227[/C][/ROW]
[ROW][C]-5.16460042097535[/C][/ROW]
[ROW][C]1.59357131797001[/C][/ROW]
[ROW][C]3.40691279597887[/C][/ROW]
[ROW][C]1.77613777872188[/C][/ROW]
[ROW][C]-2.53399934816038[/C][/ROW]
[ROW][C]-6.0625246893897[/C][/ROW]
[ROW][C]6.33974865135055[/C][/ROW]
[ROW][C]-1.91641802897427[/C][/ROW]
[ROW][C]2.1427610878425[/C][/ROW]
[ROW][C]-1.39076088445121[/C][/ROW]
[ROW][C]1.28661063768256[/C][/ROW]
[ROW][C]-3.41162157893602[/C][/ROW]
[ROW][C]5.94785766532979[/C][/ROW]
[ROW][C]-2.63489845748288[/C][/ROW]
[ROW][C]-3.28380251192823[/C][/ROW]
[ROW][C]1.66162205099291[/C][/ROW]
[ROW][C]3.1995774044176[/C][/ROW]
[ROW][C]2.20163175968498[/C][/ROW]
[ROW][C]-1.24882663664644[/C][/ROW]
[ROW][C]-1.34508296991178[/C][/ROW]
[ROW][C]-0.592798024822048[/C][/ROW]
[ROW][C]-3.30548286480658[/C][/ROW]
[ROW][C]4.69916339011195[/C][/ROW]
[ROW][C]0.717352736444453[/C][/ROW]
[ROW][C]-0.336727365214928[/C][/ROW]
[ROW][C]-0.491429981862198[/C][/ROW]
[ROW][C]0.692120915697467[/C][/ROW]
[ROW][C]-3.28310081438428[/C][/ROW]
[ROW][C]-3.58321752781721[/C][/ROW]
[ROW][C]1.34969011429344[/C][/ROW]
[ROW][C]4.63003832972732[/C][/ROW]
[ROW][C]-1.2956454234843[/C][/ROW]
[ROW][C]-7.56216482225701[/C][/ROW]
[ROW][C]2.2934184020231[/C][/ROW]
[ROW][C]4.84336359479808[/C][/ROW]
[ROW][C]-0.0714959106085093[/C][/ROW]
[ROW][C]0.140776420935931[/C][/ROW]
[ROW][C]-2.94098692272781[/C][/ROW]
[ROW][C]3.40636301558854[/C][/ROW]
[ROW][C]1.4912819575459[/C][/ROW]
[ROW][C]-7.00117716338345[/C][/ROW]
[ROW][C]0.416595445749149[/C][/ROW]
[ROW][C]1.35370292134219[/C][/ROW]
[ROW][C]-1.88524352362777[/C][/ROW]
[ROW][C]0.952104932306015[/C][/ROW]
[ROW][C]-1.25374459224973[/C][/ROW]
[ROW][C]3.45027153650374[/C][/ROW]
[ROW][C]4.05505663866231[/C][/ROW]
[ROW][C]-4.30099112871876[/C][/ROW]
[ROW][C]-0.368925292936352[/C][/ROW]
[ROW][C]1.24649715431094[/C][/ROW]
[ROW][C]-2.3958617261413[/C][/ROW]
[ROW][C]0.466848289588546[/C][/ROW]
[ROW][C]5.05166709080528[/C][/ROW]
[ROW][C]-3.12404785138481[/C][/ROW]
[ROW][C]1.8908554023434[/C][/ROW]
[ROW][C]-5.87434875124186[/C][/ROW]
[ROW][C]-3.02157107254867[/C][/ROW]
[ROW][C]4.02961089847573[/C][/ROW]
[ROW][C]3.57240487931667[/C][/ROW]
[ROW][C]0.602746695669408[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=270241&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=270241&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
0.101047520244773
-0.598290538008299
0.240717559766546
-5.07502396672215
-3.46596474175262
5.48728224506136
5.25147772935666
-0.365129088048725
-2.9509948537149
-4.03678474699818
1.70554353557883
-3.35637913814204
1.64831708428995
3.49883421297039
-4.88757297772448
1.98809967426909
3.20680789763763
-2.3003606870837
-1.01562436203127
0.520872654357301
-3.348523959078
5.4942987413787
0.825368607448018
1.46792072290071
-0.584099559877515
-1.82987354838692
4.71734872304972
0.0291184758948047
-4.90001535632311
-0.187922549041463
-2.39774436934053
1.78379951897679
-0.997986024162058
-0.620082903601443
1.37606433236041
0.148720902124692
-0.233389711273385
2.41811375123081
-2.28218479391573
0.832012027947789
-4.69873555403637
2.56831716357019
4.27668193236512
-1.20714344816596
0.211048540694087
-0.0829231146347885
-0.150366982715922
-3.50121813426095
2.65921161541432
2.1691538672451
-0.984981090401227
-5.16460042097535
1.59357131797001
3.40691279597887
1.77613777872188
-2.53399934816038
-6.0625246893897
6.33974865135055
-1.91641802897427
2.1427610878425
-1.39076088445121
1.28661063768256
-3.41162157893602
5.94785766532979
-2.63489845748288
-3.28380251192823
1.66162205099291
3.1995774044176
2.20163175968498
-1.24882663664644
-1.34508296991178
-0.592798024822048
-3.30548286480658
4.69916339011195
0.717352736444453
-0.336727365214928
-0.491429981862198
0.692120915697467
-3.28310081438428
-3.58321752781721
1.34969011429344
4.63003832972732
-1.2956454234843
-7.56216482225701
2.2934184020231
4.84336359479808
-0.0714959106085093
0.140776420935931
-2.94098692272781
3.40636301558854
1.4912819575459
-7.00117716338345
0.416595445749149
1.35370292134219
-1.88524352362777
0.952104932306015
-1.25374459224973
3.45027153650374
4.05505663866231
-4.30099112871876
-0.368925292936352
1.24649715431094
-2.3958617261413
0.466848289588546
5.05166709080528
-3.12404785138481
1.8908554023434
-5.87434875124186
-3.02157107254867
4.02961089847573
3.57240487931667
0.602746695669408



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 0 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 0 ; par9 = 0 ;
R code (references can be found in the software module):
par9 <- '1'
par8 <- '2'
par7 <- '1'
par6 <- '3'
par5 <- '12'
par4 <- '0'
par3 <- '0'
par2 <- '1'
par1 <- 'TRUE'
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')