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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 computationTue, 04 Dec 2012 12:14:16 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/04/t1354641277mqxr03azrk0s1vr.htm/, Retrieved Thu, 28 Mar 2024 08:44:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=196407, Retrieved Thu, 28 Mar 2024 08:44:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
- RMPD        [ARIMA Backward Selection] [WS 9, ARIMA backw...] [2012-12-04 17:14:16] [e4c351aee2a0bb2c047702ea90f356fa] [Current]
Feedback Forum

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Dataseries X:
655362
873127
1107897
1555964
1671159
1493308
2957796
2638691
1305669
1280496
921900
867888
652586
913831
1108544
1555827
1699283
1509458
3268975
2425016
1312703
1365498
934453
775019
651142
843192
1146766
1652601
1465906
1652734
2922334
2702805
1458956
1410363
1019279
936574
708917
885295
1099663
1576220
1487870
1488635
2882530
2677026
1404398
1344370
936865
872705
628151
953712
1160384
1400618
1661511
1495347
2918786
2775677
1407026
1370199
964526
850851
683118
847224
1073256
1514326
1503734
1507712
2865698
2788128
1391596
1366378
946295
859626




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196407&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196407&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196407&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sma1
Estimates ( 1 )-0.46010.13070.24910.3212-0.014-0.6595
(p-val)(0.3023 )(0.3754 )(0.0503 )(0.4832 )(0.9636 )(0.0954 )
Estimates ( 2 )-0.45920.13130.24830.32110-0.6755
(p-val)(0.3044 )(0.373 )(0.0502 )(0.4832 )(NA )(4e-04 )
Estimates ( 3 )-0.1560.16830.20300-0.6568
(p-val)(0.2231 )(0.1802 )(0.1111 )(NA )(NA )(3e-04 )
Estimates ( 4 )00.18430.175100-0.7005
(p-val)(NA )(0.1457 )(0.1708 )(NA )(NA )(4e-04 )
Estimates ( 5 )00.1585000-0.757
(p-val)(NA )(0.2104 )(NA )(NA )(NA )(0.0014 )
Estimates ( 6 )00000-0.7631
(p-val)(NA )(NA )(NA )(NA )(NA )(0.0016 )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ma1 & sar1 & sma1 \tabularnewline
Estimates ( 1 ) & -0.4601 & 0.1307 & 0.2491 & 0.3212 & -0.014 & -0.6595 \tabularnewline
(p-val) & (0.3023 ) & (0.3754 ) & (0.0503 ) & (0.4832 ) & (0.9636 ) & (0.0954 ) \tabularnewline
Estimates ( 2 ) & -0.4592 & 0.1313 & 0.2483 & 0.3211 & 0 & -0.6755 \tabularnewline
(p-val) & (0.3044 ) & (0.373 ) & (0.0502 ) & (0.4832 ) & (NA ) & (4e-04 ) \tabularnewline
Estimates ( 3 ) & -0.156 & 0.1683 & 0.203 & 0 & 0 & -0.6568 \tabularnewline
(p-val) & (0.2231 ) & (0.1802 ) & (0.1111 ) & (NA ) & (NA ) & (3e-04 ) \tabularnewline
Estimates ( 4 ) & 0 & 0.1843 & 0.1751 & 0 & 0 & -0.7005 \tabularnewline
(p-val) & (NA ) & (0.1457 ) & (0.1708 ) & (NA ) & (NA ) & (4e-04 ) \tabularnewline
Estimates ( 5 ) & 0 & 0.1585 & 0 & 0 & 0 & -0.757 \tabularnewline
(p-val) & (NA ) & (0.2104 ) & (NA ) & (NA ) & (NA ) & (0.0014 ) \tabularnewline
Estimates ( 6 ) & 0 & 0 & 0 & 0 & 0 & -0.7631 \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0016 ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196407&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]sma1[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.4601[/C][C]0.1307[/C][C]0.2491[/C][C]0.3212[/C][C]-0.014[/C][C]-0.6595[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3023 )[/C][C](0.3754 )[/C][C](0.0503 )[/C][C](0.4832 )[/C][C](0.9636 )[/C][C](0.0954 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.4592[/C][C]0.1313[/C][C]0.2483[/C][C]0.3211[/C][C]0[/C][C]-0.6755[/C][/ROW]
[ROW][C](p-val)[/C][C](0.3044 )[/C][C](0.373 )[/C][C](0.0502 )[/C][C](0.4832 )[/C][C](NA )[/C][C](4e-04 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.156[/C][C]0.1683[/C][C]0.203[/C][C]0[/C][C]0[/C][C]-0.6568[/C][/ROW]
[ROW][C](p-val)[/C][C](0.2231 )[/C][C](0.1802 )[/C][C](0.1111 )[/C][C](NA )[/C][C](NA )[/C][C](3e-04 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0[/C][C]0.1843[/C][C]0.1751[/C][C]0[/C][C]0[/C][C]-0.7005[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.1457 )[/C][C](0.1708 )[/C][C](NA )[/C][C](NA )[/C][C](4e-04 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]0[/C][C]0.1585[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.757[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](0.2104 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0014 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]-0.7631[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0016 )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196407&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196407&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
Iterationar1ar2ar3ma1sar1sma1
Estimates ( 1 )-0.46010.13070.24910.3212-0.014-0.6595
(p-val)(0.3023 )(0.3754 )(0.0503 )(0.4832 )(0.9636 )(0.0954 )
Estimates ( 2 )-0.45920.13130.24830.32110-0.6755
(p-val)(0.3044 )(0.373 )(0.0502 )(0.4832 )(NA )(4e-04 )
Estimates ( 3 )-0.1560.16830.20300-0.6568
(p-val)(0.2231 )(0.1802 )(0.1111 )(NA )(NA )(3e-04 )
Estimates ( 4 )00.18430.175100-0.7005
(p-val)(NA )(0.1457 )(0.1708 )(NA )(NA )(4e-04 )
Estimates ( 5 )00.1585000-0.757
(p-val)(NA )(0.2104 )(NA )(NA )(NA )(0.0014 )
Estimates ( 6 )00000-0.7631
(p-val)(NA )(NA )(NA )(NA )(NA )(0.0016 )
Estimates ( 7 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.931604094340952
-1.35079999378479
16.9515622856283
0.462687294067202
-2.76380239410492
8.59847388662093
5.2673841709568
68.9574411607092
-54.3462028466642
-8.71298681196569
38.1584581442671
4.72027528910337
-44.3580329075925
-2.26926684539219
-19.2707054086556
16.7526364424981
38.6689153084354
-82.2994954701198
49.2130678619394
-38.3948813150166
40.8604103836102
65.9420019680111
25.6672692867789
32.6845522956179
51.9794607888822
26.0333505591676
-3.38902152767911
-15.4945199118163
-6.70069799728095
-42.0722794202535
-25.602959767193
-37.7201640124299
29.2456691202212
23.3777129594913
-8.76725447722645
-14.5685757880171
6.36718657550943
-23.4997143268269
37.6196169680179
24.5813903284862
-80.4444032857175
33.4514120281896
-4.21820049440099
-26.8850185153783
49.3059217170244
15.8778274236481
0.0471764120420078
3.28626679901741
-8.5274874544803
14.179620575668
-26.1656140512272
-28.0748030028607
-3.3354826450112
-31.0637828212144
-5.45980617194966
-25.1578492523924
38.7866504893429
7.57368788413811
-2.17106379028225
-5.74927230466776
-1.34213129555684

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.931604094340952 \tabularnewline
-1.35079999378479 \tabularnewline
16.9515622856283 \tabularnewline
0.462687294067202 \tabularnewline
-2.76380239410492 \tabularnewline
8.59847388662093 \tabularnewline
5.2673841709568 \tabularnewline
68.9574411607092 \tabularnewline
-54.3462028466642 \tabularnewline
-8.71298681196569 \tabularnewline
38.1584581442671 \tabularnewline
4.72027528910337 \tabularnewline
-44.3580329075925 \tabularnewline
-2.26926684539219 \tabularnewline
-19.2707054086556 \tabularnewline
16.7526364424981 \tabularnewline
38.6689153084354 \tabularnewline
-82.2994954701198 \tabularnewline
49.2130678619394 \tabularnewline
-38.3948813150166 \tabularnewline
40.8604103836102 \tabularnewline
65.9420019680111 \tabularnewline
25.6672692867789 \tabularnewline
32.6845522956179 \tabularnewline
51.9794607888822 \tabularnewline
26.0333505591676 \tabularnewline
-3.38902152767911 \tabularnewline
-15.4945199118163 \tabularnewline
-6.70069799728095 \tabularnewline
-42.0722794202535 \tabularnewline
-25.602959767193 \tabularnewline
-37.7201640124299 \tabularnewline
29.2456691202212 \tabularnewline
23.3777129594913 \tabularnewline
-8.76725447722645 \tabularnewline
-14.5685757880171 \tabularnewline
6.36718657550943 \tabularnewline
-23.4997143268269 \tabularnewline
37.6196169680179 \tabularnewline
24.5813903284862 \tabularnewline
-80.4444032857175 \tabularnewline
33.4514120281896 \tabularnewline
-4.21820049440099 \tabularnewline
-26.8850185153783 \tabularnewline
49.3059217170244 \tabularnewline
15.8778274236481 \tabularnewline
0.0471764120420078 \tabularnewline
3.28626679901741 \tabularnewline
-8.5274874544803 \tabularnewline
14.179620575668 \tabularnewline
-26.1656140512272 \tabularnewline
-28.0748030028607 \tabularnewline
-3.3354826450112 \tabularnewline
-31.0637828212144 \tabularnewline
-5.45980617194966 \tabularnewline
-25.1578492523924 \tabularnewline
38.7866504893429 \tabularnewline
7.57368788413811 \tabularnewline
-2.17106379028225 \tabularnewline
-5.74927230466776 \tabularnewline
-1.34213129555684 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=196407&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.931604094340952[/C][/ROW]
[ROW][C]-1.35079999378479[/C][/ROW]
[ROW][C]16.9515622856283[/C][/ROW]
[ROW][C]0.462687294067202[/C][/ROW]
[ROW][C]-2.76380239410492[/C][/ROW]
[ROW][C]8.59847388662093[/C][/ROW]
[ROW][C]5.2673841709568[/C][/ROW]
[ROW][C]68.9574411607092[/C][/ROW]
[ROW][C]-54.3462028466642[/C][/ROW]
[ROW][C]-8.71298681196569[/C][/ROW]
[ROW][C]38.1584581442671[/C][/ROW]
[ROW][C]4.72027528910337[/C][/ROW]
[ROW][C]-44.3580329075925[/C][/ROW]
[ROW][C]-2.26926684539219[/C][/ROW]
[ROW][C]-19.2707054086556[/C][/ROW]
[ROW][C]16.7526364424981[/C][/ROW]
[ROW][C]38.6689153084354[/C][/ROW]
[ROW][C]-82.2994954701198[/C][/ROW]
[ROW][C]49.2130678619394[/C][/ROW]
[ROW][C]-38.3948813150166[/C][/ROW]
[ROW][C]40.8604103836102[/C][/ROW]
[ROW][C]65.9420019680111[/C][/ROW]
[ROW][C]25.6672692867789[/C][/ROW]
[ROW][C]32.6845522956179[/C][/ROW]
[ROW][C]51.9794607888822[/C][/ROW]
[ROW][C]26.0333505591676[/C][/ROW]
[ROW][C]-3.38902152767911[/C][/ROW]
[ROW][C]-15.4945199118163[/C][/ROW]
[ROW][C]-6.70069799728095[/C][/ROW]
[ROW][C]-42.0722794202535[/C][/ROW]
[ROW][C]-25.602959767193[/C][/ROW]
[ROW][C]-37.7201640124299[/C][/ROW]
[ROW][C]29.2456691202212[/C][/ROW]
[ROW][C]23.3777129594913[/C][/ROW]
[ROW][C]-8.76725447722645[/C][/ROW]
[ROW][C]-14.5685757880171[/C][/ROW]
[ROW][C]6.36718657550943[/C][/ROW]
[ROW][C]-23.4997143268269[/C][/ROW]
[ROW][C]37.6196169680179[/C][/ROW]
[ROW][C]24.5813903284862[/C][/ROW]
[ROW][C]-80.4444032857175[/C][/ROW]
[ROW][C]33.4514120281896[/C][/ROW]
[ROW][C]-4.21820049440099[/C][/ROW]
[ROW][C]-26.8850185153783[/C][/ROW]
[ROW][C]49.3059217170244[/C][/ROW]
[ROW][C]15.8778274236481[/C][/ROW]
[ROW][C]0.0471764120420078[/C][/ROW]
[ROW][C]3.28626679901741[/C][/ROW]
[ROW][C]-8.5274874544803[/C][/ROW]
[ROW][C]14.179620575668[/C][/ROW]
[ROW][C]-26.1656140512272[/C][/ROW]
[ROW][C]-28.0748030028607[/C][/ROW]
[ROW][C]-3.3354826450112[/C][/ROW]
[ROW][C]-31.0637828212144[/C][/ROW]
[ROW][C]-5.45980617194966[/C][/ROW]
[ROW][C]-25.1578492523924[/C][/ROW]
[ROW][C]38.7866504893429[/C][/ROW]
[ROW][C]7.57368788413811[/C][/ROW]
[ROW][C]-2.17106379028225[/C][/ROW]
[ROW][C]-5.74927230466776[/C][/ROW]
[ROW][C]-1.34213129555684[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=196407&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=196407&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
0.931604094340952
-1.35079999378479
16.9515622856283
0.462687294067202
-2.76380239410492
8.59847388662093
5.2673841709568
68.9574411607092
-54.3462028466642
-8.71298681196569
38.1584581442671
4.72027528910337
-44.3580329075925
-2.26926684539219
-19.2707054086556
16.7526364424981
38.6689153084354
-82.2994954701198
49.2130678619394
-38.3948813150166
40.8604103836102
65.9420019680111
25.6672692867789
32.6845522956179
51.9794607888822
26.0333505591676
-3.38902152767911
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-80.4444032857175
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-4.21820049440099
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49.3059217170244
15.8778274236481
0.0471764120420078
3.28626679901741
-8.5274874544803
14.179620575668
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-28.0748030028607
-3.3354826450112
-31.0637828212144
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Parameters (Session):
par1 = FALSE ; par2 = 0.5 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 0.5 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 1 ; 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')