logistic.loglik {FBMS}

Log likelihood function for logistic regression with a prior p(m)=sum(total_width) 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 a prior p(m)=sum(total_width) 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(y, x, model, complex, params = list(r = 1))

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