ml.shrink: Estimation of a Shrinkage Factor for Logistic Regression

In apricom: Tools for the a Priori Comparison of Regression Modelling Strategies

Description

Estimate a shrinkage factor for shrinkage-after-estimation techniques, with application to logistic regression models.

Usage

ml.shrink(b, dat)

Arguments

b	1 x m matrix of regression coefficients, derived by resampling or sample-splitting
dat	a p x m data matrix, where the final column is a binary outcome variable. This dataset acts as a "test set" or "validation set".

Details

This function works together with bootval, splitval, kcrossval and loocval to estimate a shrinkage factor. For further details, see References. This function should not be used directly, and instead should be called via one of the aforementioned shrinkage-after-estimation functions.

Value

the function returns a single shrinkage factor

Note

Currently, this function can only derive a single shrinkage factor for a given model, and is unable to estimate (weighted) predictor-specific shrinkage factors.

References

Harrell, F. E. "Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis." Springer, (2001).

Steyerberg, E. W. "Clinical Prediction Models", Springer (2009)

apricom documentation built on May 2, 2019, 6:21 a.m.