apricom (version 1.0.0)

ols.shrink: Estimation of a Shrinkage Factor for Linear Regression

Description

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

Usage

ols.shrink(b, dat, sdm)

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 continuous outcome variable. This dataset acts as a "test set" or "validation set".

sdm

the shrinkage design matrix. This determines the regression coefficients that will be involved in the shrinkage process.

Value

the function returns a shrinkage factor.

Details

This is an accessory function that 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.

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)

Examples

## Shrinkage design matrix examples for a model with an
## intercept and 4 predictors:
## 1. Uniform shrinkage (default design within apricomp).
   sdm1 <- matrix(c(0, rep(1, 4)), nrow = 1)
   print(sdm1)
## 2. Non-uniform shrinkage; 1 shrinkage factor applied only to the
##    first two predictors
   sdm2 <- matrix(c(0, 1, 1, 0, 0), nrow = 1)
   print(sdm2)