inference: Running the GMD inference on a KPR model

In pknight24/KPR: Kernel-penalized regression

Running the GMD inference on a KPR model

Description

Running the GMD inference on a KPR model

Usage

inference(KPR.output, mu = 1, r = 0.05, weight = TRUE, scale = FALSE, ...)

Arguments

KPR.output	Output from running the KPR function.
mu	GMD inference parameter
r	GMD inference parameter
weight	Logical, indicates whether to include a penalty factor when computing the beta.glasso vector.
scale	Logical, indicates whether to scale the design matrix with respect to the eigenvalues of the composite Q matrix.
...	Additional parameters passed to the GMD function.

Value

An object of classes KPR with the following fields added:

p.values	P-values for each penalized coefficient, resulting from the GMD inference.
bound	The stochastic bound used to compute each p-value.
sigmaepsi.hat	The estimated standard deviation of epsilon.

pknight24/KPR documentation built on Aug. 5, 2023, 7:01 a.m.