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]