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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 computationThu, 10 Dec 2009 11:44:55 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/10/t126047075998ks3ycybyhalh5.htm/, Retrieved Fri, 29 Mar 2024 06:36:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65713, Retrieved Fri, 29 Mar 2024 06:36:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [ARIMA Backward Selection] [] [2009-12-07 09:20:41] [b98453cac15ba1066b407e146608df68]
-   PD    [ARIMA Backward Selection] [WS10] [2009-12-10 18:44:55] [557d56ec4b06cd0135c259898de8ce95] [Current]
-   PD      [ARIMA Backward Selection] [ws 10] [2009-12-11 12:30:53] [af8eb90b4bf1bcfcc4325c143dbee260]
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Dataseries X:
10284,5
12792
12823,61538
13845,66667
15335,63636
11188,5
13633,25
12298,46667
15353,63636
12696,15385
12213,93333
13683,72727
11214,14286
13950,23077
11179,13333
11801,875
11188,82353
16456,27273
11110,0625
16530,69231
10038,41176
11681,25
11148,88235
8631
9386,444444
9764,736842
12043,75
12948,06667
10987,125
11648,3125
10633,35294
10219,3
9037,6
10296,31579
11705,41176
10681,94444
9362,947368
11306,35294
10984,45
10062,61905
8118,583333
8867,48
8346,72
8529,307692
10697,18182
8591,84
8695,607143
8125,571429
7009,758621
7883,466667
7527,645161
6763,758621
6682,333333
7855,681818
6738,88
7895,434783
6361,884615
6935,956522
8344,454545
9107,944444




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65713&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65713&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65713&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )-0.8513-0.3326-0.3549-0.3623-0.1603-0.1841-0.246-0.06350.22420.23620.1549
(p-val)(0 )(0.0669 )(0.054 )(0.0574 )(0.3898 )(0.3192 )(0.1834 )(0.7261 )(0.2123 )(0.1859 )(0.2559 )
Estimates ( 2 )-0.8394-0.3121-0.3321-0.3333-0.1291-0.1563-0.202400.26380.25390.165
(p-val)(0 )(0.0688 )(0.0529 )(0.05 )(0.4279 )(0.3475 )(0.1369 )(NA )(0.0598 )(0.1395 )(0.2173 )
Estimates ( 3 )-0.819-0.2686-0.2837-0.25140-0.0788-0.183400.28310.27570.1735
(p-val)(0 )(0.0973 )(0.0724 )(0.0611 )(NA )(0.5582 )(0.1699 )(NA )(0.0397 )(0.1018 )(0.1916 )
Estimates ( 4 )-0.8234-0.2639-0.2671-0.260100-0.135900.29620.29690.1803
(p-val)(0 )(0.1027 )(0.0845 )(0.0515 )(NA )(NA )(0.2007 )(NA )(0.0301 )(0.0716 )(0.1735 )
Estimates ( 5 )-0.8096-0.2751-0.2615-0.21200000.24490.31690.2096
(p-val)(0 )(0.0941 )(0.0967 )(0.0978 )(NA )(NA )(NA )(NA )(0.0644 )(0.0582 )(0.1129 )
Estimates ( 6 )-0.7687-0.1974-0.2369-0.220900000.22580.15580
(p-val)(0 )(0.2206 )(0.1466 )(0.0914 )(NA )(NA )(NA )(NA )(0.0932 )(0.2491 )(NA )
Estimates ( 7 )-0.7303-0.1785-0.2542-0.238200000.118400
(p-val)(0 )(0.2737 )(0.1234 )(0.072 )(NA )(NA )(NA )(NA )(0.2228 )(NA )(NA )
Estimates ( 8 )-0.64370-0.1508-0.213700000.123500
(p-val)(0 )(NA )(0.2671 )(0.1039 )(NA )(NA )(NA )(NA )(0.2065 )(NA )(NA )
Estimates ( 9 )-0.675700-0.119800000.131200
(p-val)(0 )(NA )(NA )(0.235 )(NA )(NA )(NA )(NA )(0.1843 )(NA )(NA )
Estimates ( 10 )-0.644900000000.135500
(p-val)(0 )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(0.1784 )(NA )(NA )
Estimates ( 11 )-0.64420000000000
(p-val)(0 )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )

\begin{tabular}{lllllllll}
\hline
ARIMA Parameter Estimation and Backward Selection \tabularnewline
Iteration & ar1 & ar2 & ar3 & ar4 & ar5 & ar6 & ar7 & ar8 & ar9 & ar10 & ar11 \tabularnewline
Estimates ( 1 ) & -0.8513 & -0.3326 & -0.3549 & -0.3623 & -0.1603 & -0.1841 & -0.246 & -0.0635 & 0.2242 & 0.2362 & 0.1549 \tabularnewline
(p-val) & (0 ) & (0.0669 ) & (0.054 ) & (0.0574 ) & (0.3898 ) & (0.3192 ) & (0.1834 ) & (0.7261 ) & (0.2123 ) & (0.1859 ) & (0.2559 ) \tabularnewline
Estimates ( 2 ) & -0.8394 & -0.3121 & -0.3321 & -0.3333 & -0.1291 & -0.1563 & -0.2024 & 0 & 0.2638 & 0.2539 & 0.165 \tabularnewline
(p-val) & (0 ) & (0.0688 ) & (0.0529 ) & (0.05 ) & (0.4279 ) & (0.3475 ) & (0.1369 ) & (NA ) & (0.0598 ) & (0.1395 ) & (0.2173 ) \tabularnewline
Estimates ( 3 ) & -0.819 & -0.2686 & -0.2837 & -0.2514 & 0 & -0.0788 & -0.1834 & 0 & 0.2831 & 0.2757 & 0.1735 \tabularnewline
(p-val) & (0 ) & (0.0973 ) & (0.0724 ) & (0.0611 ) & (NA ) & (0.5582 ) & (0.1699 ) & (NA ) & (0.0397 ) & (0.1018 ) & (0.1916 ) \tabularnewline
Estimates ( 4 ) & -0.8234 & -0.2639 & -0.2671 & -0.2601 & 0 & 0 & -0.1359 & 0 & 0.2962 & 0.2969 & 0.1803 \tabularnewline
(p-val) & (0 ) & (0.1027 ) & (0.0845 ) & (0.0515 ) & (NA ) & (NA ) & (0.2007 ) & (NA ) & (0.0301 ) & (0.0716 ) & (0.1735 ) \tabularnewline
Estimates ( 5 ) & -0.8096 & -0.2751 & -0.2615 & -0.212 & 0 & 0 & 0 & 0 & 0.2449 & 0.3169 & 0.2096 \tabularnewline
(p-val) & (0 ) & (0.0941 ) & (0.0967 ) & (0.0978 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0644 ) & (0.0582 ) & (0.1129 ) \tabularnewline
Estimates ( 6 ) & -0.7687 & -0.1974 & -0.2369 & -0.2209 & 0 & 0 & 0 & 0 & 0.2258 & 0.1558 & 0 \tabularnewline
(p-val) & (0 ) & (0.2206 ) & (0.1466 ) & (0.0914 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.0932 ) & (0.2491 ) & (NA ) \tabularnewline
Estimates ( 7 ) & -0.7303 & -0.1785 & -0.2542 & -0.2382 & 0 & 0 & 0 & 0 & 0.1184 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (0.2737 ) & (0.1234 ) & (0.072 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.2228 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & -0.6437 & 0 & -0.1508 & -0.2137 & 0 & 0 & 0 & 0 & 0.1235 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (0.2671 ) & (0.1039 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.2065 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & -0.6757 & 0 & 0 & -0.1198 & 0 & 0 & 0 & 0 & 0.1312 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (0.235 ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.1843 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & -0.6449 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0.1355 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (0.1784 ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & -0.6442 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \tabularnewline
(p-val) & (0 ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 14 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 15 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 16 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 17 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 18 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 19 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 20 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 21 ) & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65713&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]ar4[/C][C]ar5[/C][C]ar6[/C][C]ar7[/C][C]ar8[/C][C]ar9[/C][C]ar10[/C][C]ar11[/C][/ROW]
[ROW][C]Estimates ( 1 )[/C][C]-0.8513[/C][C]-0.3326[/C][C]-0.3549[/C][C]-0.3623[/C][C]-0.1603[/C][C]-0.1841[/C][C]-0.246[/C][C]-0.0635[/C][C]0.2242[/C][C]0.2362[/C][C]0.1549[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0669 )[/C][C](0.054 )[/C][C](0.0574 )[/C][C](0.3898 )[/C][C](0.3192 )[/C][C](0.1834 )[/C][C](0.7261 )[/C][C](0.2123 )[/C][C](0.1859 )[/C][C](0.2559 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]-0.8394[/C][C]-0.3121[/C][C]-0.3321[/C][C]-0.3333[/C][C]-0.1291[/C][C]-0.1563[/C][C]-0.2024[/C][C]0[/C][C]0.2638[/C][C]0.2539[/C][C]0.165[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0688 )[/C][C](0.0529 )[/C][C](0.05 )[/C][C](0.4279 )[/C][C](0.3475 )[/C][C](0.1369 )[/C][C](NA )[/C][C](0.0598 )[/C][C](0.1395 )[/C][C](0.2173 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]-0.819[/C][C]-0.2686[/C][C]-0.2837[/C][C]-0.2514[/C][C]0[/C][C]-0.0788[/C][C]-0.1834[/C][C]0[/C][C]0.2831[/C][C]0.2757[/C][C]0.1735[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0973 )[/C][C](0.0724 )[/C][C](0.0611 )[/C][C](NA )[/C][C](0.5582 )[/C][C](0.1699 )[/C][C](NA )[/C][C](0.0397 )[/C][C](0.1018 )[/C][C](0.1916 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]-0.8234[/C][C]-0.2639[/C][C]-0.2671[/C][C]-0.2601[/C][C]0[/C][C]0[/C][C]-0.1359[/C][C]0[/C][C]0.2962[/C][C]0.2969[/C][C]0.1803[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.1027 )[/C][C](0.0845 )[/C][C](0.0515 )[/C][C](NA )[/C][C](NA )[/C][C](0.2007 )[/C][C](NA )[/C][C](0.0301 )[/C][C](0.0716 )[/C][C](0.1735 )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]-0.8096[/C][C]-0.2751[/C][C]-0.2615[/C][C]-0.212[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.2449[/C][C]0.3169[/C][C]0.2096[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.0941 )[/C][C](0.0967 )[/C][C](0.0978 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0644 )[/C][C](0.0582 )[/C][C](0.1129 )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]-0.7687[/C][C]-0.1974[/C][C]-0.2369[/C][C]-0.2209[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.2258[/C][C]0.1558[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.2206 )[/C][C](0.1466 )[/C][C](0.0914 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.0932 )[/C][C](0.2491 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]-0.7303[/C][C]-0.1785[/C][C]-0.2542[/C][C]-0.2382[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1184[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](0.2737 )[/C][C](0.1234 )[/C][C](0.072 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.2228 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]-0.6437[/C][C]0[/C][C]-0.1508[/C][C]-0.2137[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1235[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](0.2671 )[/C][C](0.1039 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.2065 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]-0.6757[/C][C]0[/C][C]0[/C][C]-0.1198[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1312[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](0.235 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.1843 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]-0.6449[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0.1355[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](0.1784 )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]-0.6442[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](0 )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/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][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][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][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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 14 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 15 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 16 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 17 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 18 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 19 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 20 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 21 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/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][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65713&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65713&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
Iterationar1ar2ar3ar4ar5ar6ar7ar8ar9ar10ar11
Estimates ( 1 )-0.8513-0.3326-0.3549-0.3623-0.1603-0.1841-0.246-0.06350.22420.23620.1549
(p-val)(0 )(0.0669 )(0.054 )(0.0574 )(0.3898 )(0.3192 )(0.1834 )(0.7261 )(0.2123 )(0.1859 )(0.2559 )
Estimates ( 2 )-0.8394-0.3121-0.3321-0.3333-0.1291-0.1563-0.202400.26380.25390.165
(p-val)(0 )(0.0688 )(0.0529 )(0.05 )(0.4279 )(0.3475 )(0.1369 )(NA )(0.0598 )(0.1395 )(0.2173 )
Estimates ( 3 )-0.819-0.2686-0.2837-0.25140-0.0788-0.183400.28310.27570.1735
(p-val)(0 )(0.0973 )(0.0724 )(0.0611 )(NA )(0.5582 )(0.1699 )(NA )(0.0397 )(0.1018 )(0.1916 )
Estimates ( 4 )-0.8234-0.2639-0.2671-0.260100-0.135900.29620.29690.1803
(p-val)(0 )(0.1027 )(0.0845 )(0.0515 )(NA )(NA )(0.2007 )(NA )(0.0301 )(0.0716 )(0.1735 )
Estimates ( 5 )-0.8096-0.2751-0.2615-0.21200000.24490.31690.2096
(p-val)(0 )(0.0941 )(0.0967 )(0.0978 )(NA )(NA )(NA )(NA )(0.0644 )(0.0582 )(0.1129 )
Estimates ( 6 )-0.7687-0.1974-0.2369-0.220900000.22580.15580
(p-val)(0 )(0.2206 )(0.1466 )(0.0914 )(NA )(NA )(NA )(NA )(0.0932 )(0.2491 )(NA )
Estimates ( 7 )-0.7303-0.1785-0.2542-0.238200000.118400
(p-val)(0 )(0.2737 )(0.1234 )(0.072 )(NA )(NA )(NA )(NA )(0.2228 )(NA )(NA )
Estimates ( 8 )-0.64370-0.1508-0.213700000.123500
(p-val)(0 )(NA )(0.2671 )(0.1039 )(NA )(NA )(NA )(NA )(0.2065 )(NA )(NA )
Estimates ( 9 )-0.675700-0.119800000.131200
(p-val)(0 )(NA )(NA )(0.235 )(NA )(NA )(NA )(NA )(0.1843 )(NA )(NA )
Estimates ( 10 )-0.644900000000.135500
(p-val)(0 )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(0.1784 )(NA )(NA )
Estimates ( 11 )-0.64420000000000
(p-val)(0 )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 14 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 15 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 16 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 17 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 18 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 19 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 20 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 21 )NANANANANANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
10.2844905616232
1850.84869368271
1662.04487752885
1045.24777269575
2107.33245589759
-3061.78630323041
-374.473573343553
382.058354313643
1996.99681526058
-423.451648401412
-2535.56929115658
1154.54777964560
-1660.21665755246
941.734914174654
-444.964629258311
-1495.37287716536
-30.6702782551838
4458.28420751795
-1589.48797986238
2038.40210235074
-3195.83320313618
-2209.24635587723
156.416589193459
-2485.82861891137
-952.58466659375
948.486818985855
1809.46282864082
3098.12011394089
-2112.02993118701
276.061715757714
-811.115963911507
-996.446554950175
-1107.64899303698
394.359432405878
2169.54778521545
-423.501203515332
-2101.48111166327
1358.45650484533
841.757360091564
-991.93265195378
-2482.40076500923
-344.664671691007
-208.325663207601
-344.095725800966
2424.24963408702
-528.707966134645
-1517.12066147995
-459.517719037472
-1358.53951465198
417.493996337433
106.155300851481
-922.802031093877
-598.755774015899
827.194287046844
-74.9824813225869
422.320600625075
-710.52319210476
-263.709217338956
1660.34592360269
1719.96838395424

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
10.2844905616232 \tabularnewline
1850.84869368271 \tabularnewline
1662.04487752885 \tabularnewline
1045.24777269575 \tabularnewline
2107.33245589759 \tabularnewline
-3061.78630323041 \tabularnewline
-374.473573343553 \tabularnewline
382.058354313643 \tabularnewline
1996.99681526058 \tabularnewline
-423.451648401412 \tabularnewline
-2535.56929115658 \tabularnewline
1154.54777964560 \tabularnewline
-1660.21665755246 \tabularnewline
941.734914174654 \tabularnewline
-444.964629258311 \tabularnewline
-1495.37287716536 \tabularnewline
-30.6702782551838 \tabularnewline
4458.28420751795 \tabularnewline
-1589.48797986238 \tabularnewline
2038.40210235074 \tabularnewline
-3195.83320313618 \tabularnewline
-2209.24635587723 \tabularnewline
156.416589193459 \tabularnewline
-2485.82861891137 \tabularnewline
-952.58466659375 \tabularnewline
948.486818985855 \tabularnewline
1809.46282864082 \tabularnewline
3098.12011394089 \tabularnewline
-2112.02993118701 \tabularnewline
276.061715757714 \tabularnewline
-811.115963911507 \tabularnewline
-996.446554950175 \tabularnewline
-1107.64899303698 \tabularnewline
394.359432405878 \tabularnewline
2169.54778521545 \tabularnewline
-423.501203515332 \tabularnewline
-2101.48111166327 \tabularnewline
1358.45650484533 \tabularnewline
841.757360091564 \tabularnewline
-991.93265195378 \tabularnewline
-2482.40076500923 \tabularnewline
-344.664671691007 \tabularnewline
-208.325663207601 \tabularnewline
-344.095725800966 \tabularnewline
2424.24963408702 \tabularnewline
-528.707966134645 \tabularnewline
-1517.12066147995 \tabularnewline
-459.517719037472 \tabularnewline
-1358.53951465198 \tabularnewline
417.493996337433 \tabularnewline
106.155300851481 \tabularnewline
-922.802031093877 \tabularnewline
-598.755774015899 \tabularnewline
827.194287046844 \tabularnewline
-74.9824813225869 \tabularnewline
422.320600625075 \tabularnewline
-710.52319210476 \tabularnewline
-263.709217338956 \tabularnewline
1660.34592360269 \tabularnewline
1719.96838395424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65713&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]10.2844905616232[/C][/ROW]
[ROW][C]1850.84869368271[/C][/ROW]
[ROW][C]1662.04487752885[/C][/ROW]
[ROW][C]1045.24777269575[/C][/ROW]
[ROW][C]2107.33245589759[/C][/ROW]
[ROW][C]-3061.78630323041[/C][/ROW]
[ROW][C]-374.473573343553[/C][/ROW]
[ROW][C]382.058354313643[/C][/ROW]
[ROW][C]1996.99681526058[/C][/ROW]
[ROW][C]-423.451648401412[/C][/ROW]
[ROW][C]-2535.56929115658[/C][/ROW]
[ROW][C]1154.54777964560[/C][/ROW]
[ROW][C]-1660.21665755246[/C][/ROW]
[ROW][C]941.734914174654[/C][/ROW]
[ROW][C]-444.964629258311[/C][/ROW]
[ROW][C]-1495.37287716536[/C][/ROW]
[ROW][C]-30.6702782551838[/C][/ROW]
[ROW][C]4458.28420751795[/C][/ROW]
[ROW][C]-1589.48797986238[/C][/ROW]
[ROW][C]2038.40210235074[/C][/ROW]
[ROW][C]-3195.83320313618[/C][/ROW]
[ROW][C]-2209.24635587723[/C][/ROW]
[ROW][C]156.416589193459[/C][/ROW]
[ROW][C]-2485.82861891137[/C][/ROW]
[ROW][C]-952.58466659375[/C][/ROW]
[ROW][C]948.486818985855[/C][/ROW]
[ROW][C]1809.46282864082[/C][/ROW]
[ROW][C]3098.12011394089[/C][/ROW]
[ROW][C]-2112.02993118701[/C][/ROW]
[ROW][C]276.061715757714[/C][/ROW]
[ROW][C]-811.115963911507[/C][/ROW]
[ROW][C]-996.446554950175[/C][/ROW]
[ROW][C]-1107.64899303698[/C][/ROW]
[ROW][C]394.359432405878[/C][/ROW]
[ROW][C]2169.54778521545[/C][/ROW]
[ROW][C]-423.501203515332[/C][/ROW]
[ROW][C]-2101.48111166327[/C][/ROW]
[ROW][C]1358.45650484533[/C][/ROW]
[ROW][C]841.757360091564[/C][/ROW]
[ROW][C]-991.93265195378[/C][/ROW]
[ROW][C]-2482.40076500923[/C][/ROW]
[ROW][C]-344.664671691007[/C][/ROW]
[ROW][C]-208.325663207601[/C][/ROW]
[ROW][C]-344.095725800966[/C][/ROW]
[ROW][C]2424.24963408702[/C][/ROW]
[ROW][C]-528.707966134645[/C][/ROW]
[ROW][C]-1517.12066147995[/C][/ROW]
[ROW][C]-459.517719037472[/C][/ROW]
[ROW][C]-1358.53951465198[/C][/ROW]
[ROW][C]417.493996337433[/C][/ROW]
[ROW][C]106.155300851481[/C][/ROW]
[ROW][C]-922.802031093877[/C][/ROW]
[ROW][C]-598.755774015899[/C][/ROW]
[ROW][C]827.194287046844[/C][/ROW]
[ROW][C]-74.9824813225869[/C][/ROW]
[ROW][C]422.320600625075[/C][/ROW]
[ROW][C]-710.52319210476[/C][/ROW]
[ROW][C]-263.709217338956[/C][/ROW]
[ROW][C]1660.34592360269[/C][/ROW]
[ROW][C]1719.96838395424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65713&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65713&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
10.2844905616232
1850.84869368271
1662.04487752885
1045.24777269575
2107.33245589759
-3061.78630323041
-374.473573343553
382.058354313643
1996.99681526058
-423.451648401412
-2535.56929115658
1154.54777964560
-1660.21665755246
941.734914174654
-444.964629258311
-1495.37287716536
-30.6702782551838
4458.28420751795
-1589.48797986238
2038.40210235074
-3195.83320313618
-2209.24635587723
156.416589193459
-2485.82861891137
-952.58466659375
948.486818985855
1809.46282864082
3098.12011394089
-2112.02993118701
276.061715757714
-811.115963911507
-996.446554950175
-1107.64899303698
394.359432405878
2169.54778521545
-423.501203515332
-2101.48111166327
1358.45650484533
841.757360091564
-991.93265195378
-2482.40076500923
-344.664671691007
-208.325663207601
-344.095725800966
2424.24963408702
-528.707966134645
-1517.12066147995
-459.517719037472
-1358.53951465198
417.493996337433
106.155300851481
-922.802031093877
-598.755774015899
827.194287046844
-74.9824813225869
422.320600625075
-710.52319210476
-263.709217338956
1660.34592360269
1719.96838395424



Parameters (Session):
par1 = 1 ; par2 = 0 ; par3 = 0 ; par4 = 12 ; par5 = 1 ; par6 = 0 ; par7 = 0 ; par8 = 3 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 0 ; par9 = 0 ;
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
par6 <- 11
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')