shrink: Global, Parameterwise, and Joint Post-Estimation Shrinkage
Post-estimation shrinkage of regression coefficients in statistical modeling can be
used to correct for the overestimation of regression coefficients caused by variable selection.
While global shrinkage modifies all regression coefficients by the same factor, parameterwise
shrinkage factors differ between regression coefficients. With highly correlated or semantically
related variables, such as several columns of a design matrix describing a nonlinear
effect, parameterwise shrinkage factors are not interpretable and a compromise between global
and parameterwise shrinkage, termed 'joint shrinkage', is a useful extension.
A computational shortcut to resampling-based shrinkage factor estimation based on DFBETA
residuals is applied.
Global, parameterwise, and joint shrinkage for models fitted by lm, glm, coxph, or mfp is