BAS (version 1.2.2)

bayesglm.fit: Fitting Generalized Linear Models and 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

x

design matrix

y

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() and poisson() with canonical links are implemented.

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

coefficients

MLEs

se

Standard errors of coefficients based on the sqrt of the diagonal of the inverse information matrix

mu

fitted mean

rank

numeric rank of the fitted linear model

deviance

minus twice the log likelihood evaluated at the MLEs

g

value of g in g-priors

shrinkage

shrinkage factor for coefficients in linear predictor

RegSS

quadratic form beta'I(beta)beta used in shrinkage

logmarglik

the 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

glm

See Also

bic.prior

Examples

require(MASS)
library(MASS)
Pima.tr
Y = as.numeric(Pima.tr$type) - 1
X = cbind(1, as.matrix(Pima.tr[,1:7]))
out = bayesglm.fit(X, Y, family=binomial(),coefprior=bic.prior(n=length(Y)))
out$coef
out$se
# using built in function
glm(type ~ ., family=binomial(), data=Pima.tr)