Free Statistics

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 computationThu, 10 Dec 2009 14:56:05 -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/t1260482396rno4lay8cdck5f9.htm/, Retrieved Thu, 28 Mar 2024 18:57:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65825, Retrieved Thu, 28 Mar 2024 18:57:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsverbetering
Estimated Impact114
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   [ARIMA Backward Selection] [] [2009-11-27 14:53:14] [b98453cac15ba1066b407e146608df68]
-   PD    [ARIMA Backward Selection] [] [2009-12-03 18:49:27] [90f6d58d515a4caed6fb4b8be4e11eaa]
-   P         [ARIMA Backward Selection] [Workshop 9] [2009-12-10 21:56:05] [682632737e024f9e62885141c5f654cd] [Current]
Feedback Forum

Post a new message
Dataseries X:
8.00
8.10
7.70
7.50
7.60
7.80
7.80
7.80
7.50
7.50
7.10
7.50
7.50
7.60
7.70
7.70
7.90
8.10
8.20
8.20
8.20
7.90
7.30
6.90
6.60
6.70
6.90
7.00
7.10
7.20
7.10
6.90
7.00
6.80
6.40
6.70
6.60
6.40
6.30
6.20
6.50
6.80
6.80
6.40
6.10
5.80
6.10
7.20
7.30
6.90
6.10
5.80
6.20
7.10
7.70
7.90
7.70
7.40
7.50
8.00




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65825&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]8 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=65825&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65825&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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







ARIMA Parameter Estimation and Backward Selection
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.4219-0.0255-0.4915-0.89540.0085-0.4658-0.3561
(p-val)(0.0061 )(0.8758 )(7e-04 )(0 )(0.9831 )(0.0301 )(0.536 )
Estimates ( 2 )0.4135-0.0276-0.4979-1.13830-0.4642-0.3592
(p-val)(0.0068 )(0.8645 )(5e-04 )(0 )(NA )(0.0172 )(0.2201 )
Estimates ( 3 )0.40240-0.5096-1.13530-0.4766-0.345
(p-val)(0.0033 )(NA )(0 )(0 )(NA )(0.0074 )(0.2173 )
Estimates ( 4 )0.45560-0.4669-0.94380-0.51060
(p-val)(4e-04 )(NA )(1e-04 )(0 )(NA )(0.0021 )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(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 ) & 0.4219 & -0.0255 & -0.4915 & -0.8954 & 0.0085 & -0.4658 & -0.3561 \tabularnewline
(p-val) & (0.0061 ) & (0.8758 ) & (7e-04 ) & (0 ) & (0.9831 ) & (0.0301 ) & (0.536 ) \tabularnewline
Estimates ( 2 ) & 0.4135 & -0.0276 & -0.4979 & -1.1383 & 0 & -0.4642 & -0.3592 \tabularnewline
(p-val) & (0.0068 ) & (0.8645 ) & (5e-04 ) & (0 ) & (NA ) & (0.0172 ) & (0.2201 ) \tabularnewline
Estimates ( 3 ) & 0.4024 & 0 & -0.5096 & -1.1353 & 0 & -0.4766 & -0.345 \tabularnewline
(p-val) & (0.0033 ) & (NA ) & (0 ) & (0 ) & (NA ) & (0.0074 ) & (0.2173 ) \tabularnewline
Estimates ( 4 ) & 0.4556 & 0 & -0.4669 & -0.9438 & 0 & -0.5106 & 0 \tabularnewline
(p-val) & (4e-04 ) & (NA ) & (1e-04 ) & (0 ) & (NA ) & (0.0021 ) & (NA ) \tabularnewline
Estimates ( 5 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 6 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 7 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 8 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 9 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 10 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 11 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 12 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
Estimates ( 13 ) & NA & NA & NA & NA & NA & NA & NA \tabularnewline
(p-val) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) & (NA ) \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65825&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]0.4219[/C][C]-0.0255[/C][C]-0.4915[/C][C]-0.8954[/C][C]0.0085[/C][C]-0.4658[/C][C]-0.3561[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0061 )[/C][C](0.8758 )[/C][C](7e-04 )[/C][C](0 )[/C][C](0.9831 )[/C][C](0.0301 )[/C][C](0.536 )[/C][/ROW]
[ROW][C]Estimates ( 2 )[/C][C]0.4135[/C][C]-0.0276[/C][C]-0.4979[/C][C]-1.1383[/C][C]0[/C][C]-0.4642[/C][C]-0.3592[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0068 )[/C][C](0.8645 )[/C][C](5e-04 )[/C][C](0 )[/C][C](NA )[/C][C](0.0172 )[/C][C](0.2201 )[/C][/ROW]
[ROW][C]Estimates ( 3 )[/C][C]0.4024[/C][C]0[/C][C]-0.5096[/C][C]-1.1353[/C][C]0[/C][C]-0.4766[/C][C]-0.345[/C][/ROW]
[ROW][C](p-val)[/C][C](0.0033 )[/C][C](NA )[/C][C](0 )[/C][C](0 )[/C][C](NA )[/C][C](0.0074 )[/C][C](0.2173 )[/C][/ROW]
[ROW][C]Estimates ( 4 )[/C][C]0.4556[/C][C]0[/C][C]-0.4669[/C][C]-0.9438[/C][C]0[/C][C]-0.5106[/C][C]0[/C][/ROW]
[ROW][C](p-val)[/C][C](4e-04 )[/C][C](NA )[/C][C](1e-04 )[/C][C](0 )[/C][C](NA )[/C][C](0.0021 )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 5 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 6 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 7 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 8 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 9 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 10 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 11 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 12 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[ROW][C]Estimates ( 13 )[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C](p-val)[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][C](NA )[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65825&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65825&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
Iterationar1ar2ar3ma1sar1sar2sma1
Estimates ( 1 )0.4219-0.0255-0.4915-0.89540.0085-0.4658-0.3561
(p-val)(0.0061 )(0.8758 )(7e-04 )(0 )(0.9831 )(0.0301 )(0.536 )
Estimates ( 2 )0.4135-0.0276-0.4979-1.13830-0.4642-0.3592
(p-val)(0.0068 )(0.8645 )(5e-04 )(0 )(NA )(0.0172 )(0.2201 )
Estimates ( 3 )0.40240-0.5096-1.13530-0.4766-0.345
(p-val)(0.0033 )(NA )(0 )(0 )(NA )(0.0074 )(0.2173 )
Estimates ( 4 )0.45560-0.4669-0.94380-0.51060
(p-val)(4e-04 )(NA )(1e-04 )(0 )(NA )(0.0021 )(NA )
Estimates ( 5 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 6 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 7 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 8 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 9 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 10 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 11 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 12 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )
Estimates ( 13 )NANANANANANANA
(p-val)(NA )(NA )(NA )(NA )(NA )(NA )(NA )







Estimated ARIMA Residuals
Value
0.0268364801934054
0.268974804631799
-0.142698040626084
-0.0649895177038167
0.0877955643403605
0.0567131851744123
-0.0893170785872131
0.134075000403698
-0.378633585964905
-0.108898850310898
-0.453629669499431
-0.0442437022248864
0.062510077595423
-0.143468461277700
-0.0305889206088328
-0.0552847126011986
0.0794528506838844
0.0184331732234474
-0.0446538010417529
0.223981726197178
-0.0523367016098643
0.0513722371200867
0.40231333199086
-0.0423978557696024
-0.236840065892825
0.252370158220452
-0.0482718775183034
0.0825611235099097
0.0442032386877798
-0.0237518571796082
-0.162166669783396
-0.0101834507930462
-0.071262984201312
0.54342252788313
0.08597878503618
-0.277484540867257
-0.034642853199093
-0.267410035974983
0.116966817660426
0.0330382584768762
0.183471417812309
0.143484341422960
0.184688541573045
0.11662330405596
0.099488776130697
0.194725033551557
-0.20923056188151

\begin{tabular}{lllllllll}
\hline
Estimated ARIMA Residuals \tabularnewline
Value \tabularnewline
0.0268364801934054 \tabularnewline
0.268974804631799 \tabularnewline
-0.142698040626084 \tabularnewline
-0.0649895177038167 \tabularnewline
0.0877955643403605 \tabularnewline
0.0567131851744123 \tabularnewline
-0.0893170785872131 \tabularnewline
0.134075000403698 \tabularnewline
-0.378633585964905 \tabularnewline
-0.108898850310898 \tabularnewline
-0.453629669499431 \tabularnewline
-0.0442437022248864 \tabularnewline
0.062510077595423 \tabularnewline
-0.143468461277700 \tabularnewline
-0.0305889206088328 \tabularnewline
-0.0552847126011986 \tabularnewline
0.0794528506838844 \tabularnewline
0.0184331732234474 \tabularnewline
-0.0446538010417529 \tabularnewline
0.223981726197178 \tabularnewline
-0.0523367016098643 \tabularnewline
0.0513722371200867 \tabularnewline
0.40231333199086 \tabularnewline
-0.0423978557696024 \tabularnewline
-0.236840065892825 \tabularnewline
0.252370158220452 \tabularnewline
-0.0482718775183034 \tabularnewline
0.0825611235099097 \tabularnewline
0.0442032386877798 \tabularnewline
-0.0237518571796082 \tabularnewline
-0.162166669783396 \tabularnewline
-0.0101834507930462 \tabularnewline
-0.071262984201312 \tabularnewline
0.54342252788313 \tabularnewline
0.08597878503618 \tabularnewline
-0.277484540867257 \tabularnewline
-0.034642853199093 \tabularnewline
-0.267410035974983 \tabularnewline
0.116966817660426 \tabularnewline
0.0330382584768762 \tabularnewline
0.183471417812309 \tabularnewline
0.143484341422960 \tabularnewline
0.184688541573045 \tabularnewline
0.11662330405596 \tabularnewline
0.099488776130697 \tabularnewline
0.194725033551557 \tabularnewline
-0.20923056188151 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65825&T=2

[TABLE]
[ROW][C]Estimated ARIMA Residuals[/C][/ROW]
[ROW][C]Value[/C][/ROW]
[ROW][C]0.0268364801934054[/C][/ROW]
[ROW][C]0.268974804631799[/C][/ROW]
[ROW][C]-0.142698040626084[/C][/ROW]
[ROW][C]-0.0649895177038167[/C][/ROW]
[ROW][C]0.0877955643403605[/C][/ROW]
[ROW][C]0.0567131851744123[/C][/ROW]
[ROW][C]-0.0893170785872131[/C][/ROW]
[ROW][C]0.134075000403698[/C][/ROW]
[ROW][C]-0.378633585964905[/C][/ROW]
[ROW][C]-0.108898850310898[/C][/ROW]
[ROW][C]-0.453629669499431[/C][/ROW]
[ROW][C]-0.0442437022248864[/C][/ROW]
[ROW][C]0.062510077595423[/C][/ROW]
[ROW][C]-0.143468461277700[/C][/ROW]
[ROW][C]-0.0305889206088328[/C][/ROW]
[ROW][C]-0.0552847126011986[/C][/ROW]
[ROW][C]0.0794528506838844[/C][/ROW]
[ROW][C]0.0184331732234474[/C][/ROW]
[ROW][C]-0.0446538010417529[/C][/ROW]
[ROW][C]0.223981726197178[/C][/ROW]
[ROW][C]-0.0523367016098643[/C][/ROW]
[ROW][C]0.0513722371200867[/C][/ROW]
[ROW][C]0.40231333199086[/C][/ROW]
[ROW][C]-0.0423978557696024[/C][/ROW]
[ROW][C]-0.236840065892825[/C][/ROW]
[ROW][C]0.252370158220452[/C][/ROW]
[ROW][C]-0.0482718775183034[/C][/ROW]
[ROW][C]0.0825611235099097[/C][/ROW]
[ROW][C]0.0442032386877798[/C][/ROW]
[ROW][C]-0.0237518571796082[/C][/ROW]
[ROW][C]-0.162166669783396[/C][/ROW]
[ROW][C]-0.0101834507930462[/C][/ROW]
[ROW][C]-0.071262984201312[/C][/ROW]
[ROW][C]0.54342252788313[/C][/ROW]
[ROW][C]0.08597878503618[/C][/ROW]
[ROW][C]-0.277484540867257[/C][/ROW]
[ROW][C]-0.034642853199093[/C][/ROW]
[ROW][C]-0.267410035974983[/C][/ROW]
[ROW][C]0.116966817660426[/C][/ROW]
[ROW][C]0.0330382584768762[/C][/ROW]
[ROW][C]0.183471417812309[/C][/ROW]
[ROW][C]0.143484341422960[/C][/ROW]
[ROW][C]0.184688541573045[/C][/ROW]
[ROW][C]0.11662330405596[/C][/ROW]
[ROW][C]0.099488776130697[/C][/ROW]
[ROW][C]0.194725033551557[/C][/ROW]
[ROW][C]-0.20923056188151[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65825&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65825&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
0.0268364801934054
0.268974804631799
-0.142698040626084
-0.0649895177038167
0.0877955643403605
0.0567131851744123
-0.0893170785872131
0.134075000403698
-0.378633585964905
-0.108898850310898
-0.453629669499431
-0.0442437022248864
0.062510077595423
-0.143468461277700
-0.0305889206088328
-0.0552847126011986
0.0794528506838844
0.0184331732234474
-0.0446538010417529
0.223981726197178
-0.0523367016098643
0.0513722371200867
0.40231333199086
-0.0423978557696024
-0.236840065892825
0.252370158220452
-0.0482718775183034
0.0825611235099097
0.0442032386877798
-0.0237518571796082
-0.162166669783396
-0.0101834507930462
-0.071262984201312
0.54342252788313
0.08597878503618
-0.277484540867257
-0.034642853199093
-0.267410035974983
0.116966817660426
0.0330382584768762
0.183471417812309
0.143484341422960
0.184688541573045
0.11662330405596
0.099488776130697
0.194725033551557
-0.20923056188151



Parameters (Session):
par1 = FALSE ; par2 = 1 ; par3 = 2 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;
Parameters (R input):
par1 = FALSE ; par2 = 1 ; par3 = 2 ; 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')