update_scale_p: Update conditional expectation of prior probabilities of...

In jmleach-bst/ssnet: Fit Spike-and-Slab Elastic Net GLM With Spatial Priors on Parameters

Update conditional expectation of prior probabilities of inclusion at each iteration.

Description

Update conditional expectation of prior probabilities of inclusion at each iteration.

Usage

update_scale_p(b0, ss, theta, alpha)

Arguments

b0	A vector of current parameter estimates.
ss	A vector of spike and slab prior scales, respectively.
theta	The current estimate of prior probabilities of inclusion.
alpha	A scalar value between 0 and 1 determining the compromise between the Ridge and Lasso models. Whenvalpha = 1 reduces to the Lasso, and when alpha = 0 reduces to Ridge.

Value

A list whose first element is a vector of updated scale parameters for eachvparameter and whose second element is a vector of updated conditional expectationsvof prior probabilities of model inclusion.

Note

This function is a modified version of update_scale_p() from the R package BhGLM.

jmleach-bst/ssnet documentation built on March 4, 2024, 5:04 p.m.