bayes.probit: Simulates from a probit binary response regression model using data augmentation and Gibbs sampling
Description
Gives a simulated sample from the joint posterior distribution of the regression
vector for a binary response regression model with a probit link and a
informative normal(beta, P) prior. Also computes the log marginal likelihood when
a subjective prior is used.
Usage
bayes.probit(y,X,m,prior=list(beta=0,P=0))
Arguments
y
vector of binary responses
X
covariate matrix
m
number of simulations desired
prior
list with components beta, the prior mean, and P, the prior precision matrix
Value
beta
matrix of simulated draws of regression vector beta where each row corresponds to one draw
log.marg
simulation estimate at log marginal likelihood of the model
# NOT RUN {response=c(0,1,0,0,0,1,1,1,1,1)
covariate=c(1,2,3,4,5,6,7,8,9,10)
X=cbind(1,covariate)
prior=list(beta=c(0,0),P=diag(c(.5,10)))
m=1000s=bayes.probit(response,X,m,prior)
# }