logistic.loglik.ala {FBMS}R Documentation

Log likelihood function for logistic regression with an approximate Laplace approximations used 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 logistic regression with an approximate Laplace approximations used 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

logistic.loglik.ala(y, x, model, complex, params = list(r = exp(-0.5)))

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

params

A list of parameters for the log likelihood, supplied by the user

Value

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

Examples

logistic.loglik.ala(as.integer(rnorm(100) > 0), matrix(rnorm(100)), TRUE, list(oc = 1))



[Package FBMS version 1.1 Index]