Bayesian regression model selection using G priors
Using Zellner's G priors, computes the log marginal density for all possible regression models
bayes.model.selection(y, X, c, constant=TRUE)
y |
vector of response values |
X |
matrix of covariates |
c |
parameter of the G prior |
constant |
logical variable indicating if a constant term is in the matrix X |
mod.prob |
data frame specifying the model, the value of the log marginal density and the value of the posterior model probability |
converge |
logical vector indicating if the laplace algorithm converged for each model |
Jim Albert
data(birdextinct) logtime=log(birdextinct$time) X=cbind(1,birdextinct$nesting,birdextinct$size,birdextinct$status) bayes.model.selection(logtime,X,100)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.