Estimation of a Shrinkage Factor for Linear Regression
Estimate a shrinkage factor for shrinkage-after-estimation techniques, with application to linear regression models.
ols.shrink(b, dat, sdm)
b |
1 x |
dat |
a |
sdm |
the shrinkage design matrix. This determines the regression coefficients that will be involved in the shrinkage process. |
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.
the function returns a shrinkage factor.
Currently, this function can only derive a single shrinkage factor for a given model, and is unable to estimate (weighted) predictor-specific shrinkage factors.
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)
## 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)
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