| DBModelSelect-package {DBModelSelect} | R Documentation |
Distribution-Based Model Selection
Description
Perform model selection using distribution and probability-based methods, including standardized AIC, BIC, and AICc. These standardized information criteria allow one to perform model selection in a way similar to the prevalent "Rule of 2" method, but formalize the method to rely on probability theory. A novel goodness-of-fit procedure for assessing linear regression models is also available. This test relies on theoretical properties of the estimated error variance for a normal linear regression model, and employs a bootstrap procedure to assess the null hypothesis that the fitted model shows no lack of fit. For more information, see Koeneman and Cavanaugh (2023) <arXiv:2309.10614>. Functionality to perform all subsets linear or generalized linear regression is also available.
Details
The DESCRIPTION file:
| Package: | DBModelSelect |
| Type: | Package |
| Title: | Distribution-Based Model Selection |
| Version: | 0.2.0 |
| Date: | 2023-08-22 |
| Authors@R: | person("Scott H.", "Koeneman", email = "Scott.Koeneman@jefferson.edu", role = c("aut", "cre")) |
| Description: | Perform model selection using distribution and probability-based methods, including standardized AIC, BIC, and AICc. These standardized information criteria allow one to perform model selection in a way similar to the prevalent "Rule of 2" method, but formalize the method to rely on probability theory. A novel goodness-of-fit procedure for assessing linear regression models is also available. This test relies on theoretical properties of the estimated error variance for a normal linear regression model, and employs a bootstrap procedure to assess the null hypothesis that the fitted model shows no lack of fit. For more information, see Koeneman and Cavanaugh (2023) <arXiv:2309.10614>. Functionality to perform all subsets linear or generalized linear regression is also available. |
| URL: | https://github.com/shkoeneman/DBModelSelect |
| License: | GPL-3 |
| Depends: | R (>= 4.1.0) |
| RoxygenNote: | 7.2.3 |
| Author: | Scott H. Koeneman [aut, cre] |
| Maintainer: | Scott H. Koeneman <Scott.Koeneman@jefferson.edu> |
Index of help topics:
AICc Corrected AIC for linear models
BootGOFTestLM Bootstrap goodness-of-fit procedure for linear
models
DBModelSelect-package Distribution-Based Model Selection
FitGLMSubsets Perform all subsets regression for generalized
linear models
FitLMSubsets Perform all subsets linear regression
StandICModelSelect Model selection using standardized information
criteria
The DBModelSelect package provides several methods of
model selection based in distributional theory. This includes
an implementation of selection using standardized information
criteria in the StandICModelSelect function, and
the implementation of an omnibus goodness-of-fit test for
linear models in the BootGOFTestLM function.
Author(s)
Maintainer: Scott H. Koeneman Scott.Koeneman@jefferson.edu
See Also
Useful links: