DBModelSelect-package: Distribution-Based Model Selection

In DBModelSelect: Distribution-Based Model Selection

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

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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 [e-mail]

See Also

Useful links:

• https://github.com/shkoeneman/DBModelSelect

DBModelSelect documentation built on Sept. 21, 2023, 1:06 a.m.