Exact Variable-Subset Selection in Linear Regression
Extract AIC values from a subset regression
Extract BIC values from a subset regression
Extract the ceofficients from a subset regression
Extract the deviance from a subset regression
Extract the fitted values from a subset regression
Extract a formula from a subset regression
Heatmap of a subset regression
Best-subset regression
Best-subset regression
Best-subset regression
Best-subset regression
Package lmSubsets
All-subsets regression
All-subsets regression
All-subsets regression
Extract the log-likelihood from a subset regression
Extract the model frame from a subset regression
Extract a model matrix from a subset regression
Extract the model response from a subset regression
Model response
Plot a subset regression
Refit a subset regression
Refitting models
Extract the residuals from all-subsets regression
Extract the residual standard deviation from a subset regression
Summarize a subset regression
Extract variable names from a subset regression
Extract the variance-covariance matrix from a subset regression
Exact and approximation algorithms for variable-subset selection in ordinary linear regression models. Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann et al. (2020) <doi:10.18637/jss.v093.i03>.