FBMS (version 1.2)

gaussian_tcch_log_likelihood: Log likelihood function for Gaussian regression with parameter priors from BAS package

Description

This function computes the marginal likelihood of a Gaussian regression model under different priors.

Usage

gaussian_tcch_log_likelihood(
  y,
  x,
  model,
  complex,
  mlpost_params = list(r = exp(-0.5), beta_prior = list(type = "intrinsic"))
)

Value

A list with elements:

crit

Log marginal likelihood combined with the log prior.

coefs

Posterior mode of the coefficients.

Arguments

y

A numeric vector containing the dependent variable.

x

A matrix containing the independent variables, including an intercept column.

model

A logical vector indicating which variables to include in the model.

complex

A list containing complexity measures for the features.

mlpost_params

A list of parameters for the log likelihood, specifying the tuning parameters of beta priors.

Examples

gaussian_tcch_log_likelihood(rnorm(100), matrix(rnorm(100)), c(TRUE), list(oc=1))