estimate_risk: Calculate information criteria.

In hierNest: Penalized Regression with Hierarchical Nested Parameterization Structure

Calculate information criteria.

Description

This function uses the degrees of freedom to calculate various information criteria. This function uses the "unknown variance" version of the likelihood. Only implemented for Gaussian regression. The constant is ignored (as in [stats::extractAIC()]).

Usage

estimate_risk(object, x, type = c("AIC", "BIC", "GCV"), approx_df = FALSE)

Arguments

object	fitted object from a call to [sparsegl()].
x	Matrix. The matrix of predictors used to estimate the 'sparsegl' object. May be missing if 'approx_df = TRUE'.
type	one or more of AIC, BIC, or GCV.
approx_df	the 'df' component of a 'sparsegl' object is an approximation (albeit a fairly accurate one) to the actual degrees-of-freedom. However, the exact value requires inverting a portion of ‘X’X'. So this computation may take some time (the default computes the exact df).

Value

a 'data.frame' with as many rows as 'object$lambda'. It contains columns 'lambda', 'df', and the requested risk types.

References

Vaiter S, Deledalle C, Peyré G, Fadili J, Dossal C. (2012). The Degrees of Freedom of the Group Lasso for a General Design. https://arxiv.org/pdf/1212.6478.pdf.

See Also

[sparsegl()] method.

hierNest documentation built on March 24, 2026, 5:07 p.m.