gausscov (0.0.3)
The Gaussian Covariate Method for Variable Selection.
http://cran.rproject.org/web/packages/gausscov
Given the standard linear model the traditional way of deciding whether to include the jth covariate is to apply the Ftest to decide whether the corresponding beta coefficient is zero. The Gaussian covariate method is completely different. The question as to whether the beta coefficient is or is not zero is replaced by the question as to whether the covariate is better or worse than i.i.d. Gaussian noise. The Pvalue for the covariate is the probability that Gaussian noise is better. Surprisingly this can be given exactly and it is the same a the Pvalue for the classical model based on the Fdistribution. The Gaussian covariate Pvalue is model free, it is the same for any data set. Using the idea it is possible to do covariate selection for a small number of covariates 25 by considering all subsets. Post selection inference causes no problems as the Pvalues hold whatever the data. The idea extends to stepwise regression again with exact probabilities. In the simplest version the only parameter is a specified cutoff Pvalue which can be interpreted as the probability of a false positive being included in the final selection. For more information see the website below and the accompanying papers: L. Davies and L. Duembgen, "A Modelfree Approach to Linear Least Squares Regression with Exact Probabilities and Applications to Covariate Selection", 2019, . L. Davies, "Lasso, Knockoff and Gaussian covariates: A comparison", 2018, .
Maintainer:
Laurie Davies
Author(s): Laurie Davies [aut, cre]
License: GPL3
Uses: Does not use any package
Released 9 days ago.
2 previous versions
 gausscov_0.0.2. Released 5 months ago.
 gausscov_0.0.1. Released 8 months ago.
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