glm.loglik.g: Log likelihood function for glm regression with Zellner's...

In FBMS: Flexible Bayesian Model Selection and Model Averaging

Log likelihood function for glm regression with Zellner's g-prior and BIC-like approximations

Description

This function estimates marginal likelihood for generalized linear models using a BIC-style penalty adjusted to approximate Zellner's g-prior effect.

Usage

glm.loglik.g(
  y,
  x,
  model,
  complex,
  mlpost_params = list(r = NULL, family = "binomial", g = NULL)
)

Arguments

y	A vector containing the dependent variable
x	The matrix containing the precalculated features
model	A logical vector indicating which features are included in the model
complex	A list of complexity measures for the features
mlpost_params	A list of parameters for the log likelihood, including: r - scalar tuning parameter for the prior (default is 1 / number of rows of x) family - GLM family as string ("binomial", "poisson", "Gamma"), default is "binomial" g - scalar specifying the g prior hyperparameter (default max of model size squared and sample size)

Value

A list with the approximate log marginal likelihood (crit) and the posterior mode of coefficients (coefs)

Examples

glm.loglik.g(as.integer(rnorm(100) > 0), 
cbind(1, matrix(rnorm(100))), c(TRUE, TRUE), list(oc = 1),
 list(r = 1/100, family = "binomial", g = 10))

FBMS documentation built on Sept. 13, 2025, 1:09 a.m.