bayesglm.fit: Fitting Generalized Linear Models Bayesian marginal likelihood evaluation

Description

A version of glm.fit rewritten in C; also returns marginal likelihoods for Baysesian model comparison

Usage

bayesglm.fit(x, y, weights = rep(1, nobs),
            start = NULL, etastart = NULL, 
            mustart = NULL, offset = rep(0, nobs), family = binomial(),
            coefprior = bic.prior(nobs),
            control = glm.control(), intercept = TRUE)

Arguments

design matrix

response

weights

optional vector of weights to be used in the fitting process. SHould be NULL or a numeric vector.

start

starting value for coefficients in the linear predictor

etastart

starting values for the linear predictor

mustart

starting values for the vectors of means

offset

a priori known component to be included in the linear predictor

family

a description of the error distribution and link function for exponential family; currently only binomial() is coded.

coefprior

function specifying prior distribution on coefficients with optionlal hyperparamters leading to marginal likelihood calculations; options include bic.prior(),aic.prior(), and ic.prior()

control

a list of parameters that control convergence in the fitting process. See the documentation for glm.control()

intercept

should an intercept be included in the null model?

Value

coefficientsMLEs
seStandard errors of coefficients based on the sqrt of the diagonal of the inverse information matrix
mufitted mean
ranknumeric rank of the fitted linear model
devianceminus twice the log likelihood evaluated at the MLEs
gvalue of g in g-priors
shrinkagesrhinkage factor for coefficients in linear predictor
RegSSquadractic form beta'I(beta)beta used in shrinkage
logmarglikthe log marginal or integrated log likelihood (up to a constant)

Details

C version of glm-fit. For different prior choices returns, marginal likelihood of model using a Laplace approximation.

References