estRidge: Estimate Coefficients for Ridge Regression
In eshrink: Shrinkage for Effect Estimation

Description Usage Arguments Details Author(s) See Also

View source: R/functions_ridge.R

Computes a vector of regression coefficients for a provided ridge penalty.

1	estRidge(lambda, X, y, penalize, XtX = crossprod(X))

`lambda`	ridge penalty factor
`X`	design matrix for the regression.
`y`	outcome vector. Unless `X` contains an intercept column, this should typically be centered.
`penalize`	vector giving penalty structure. Values must be in [0, 1]. See Details for more information.
`XtX`	(optional) cross product of the design matrix. If running simulations or other procedure for identical `X`, providing a pre-computed value can reduce computational cost.

The input penalize is a vector of ridge penalty factors, such that the penalty for covariate j is lambda*penalize[j]. Although its primary purpose is for indicating which variables to penalize (1) and which to not penalize (0), fractional values between 0 and 1 are accepted. Defaults to c(0, rep(1, p-1)), where p is number of columns in X (this penalizes all coefficients but the first).

The design matrix X is assumed to contain only numeric values, so any factors should be coded according to desired contrast (e.g., via model.matrix)

Joshua Keller

festRidge, mseRidge

eshrink documentation built on Jan. 13, 2021, 6:59 a.m.