glm.loglik: Log likelihood function for glm regression with a Jeffreys parameter prior and BIC approximations of the posterior
This function is created as an example of how to create an estimator that is used
to calculate the marginal likelihood of a model.
Description
Log likelihood function for glm regression with a Jeffreys parameter prior and BIC approximations of the posterior
This function is created as an example of how to create an estimator that is used
to calculate the marginal likelihood of a model.
Usage
glm.loglik(
y,
x,
model,
complex,
mlpost_params = list(r = exp(-0.5), family = "Gamma")
)
Value
A list with the log marginal likelihood combined with the log prior (crit) and the posterior mode of the coefficients (coefs).
Arguments
y
A vector containing the dependent variable
x
The matrix containing the precalculated features
model
The model to estimate as a logical vector
complex
A list of complexity measures for the features
mlpost_params
A list of parameters for the log likelihood, supplied by the user, family must be specified