bayes.probit {LearnBayes}R Documentation

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.

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

matrix of simulated draws of regression vector beta where each row corresponds to one draw

Author(s)

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)

[Package LearnBayes version 1.2 Index]