LearnBayes (version 2.15.2)

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))

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

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

Author

Jim Albert

Examples

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=1000
s=bayes.probit(response,X,m,prior)