bayes.probit: Simulates from a probit binary response regression model...

In LearnBayes: Functions for Learning Bayesian Inference

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

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)

Example output

LearnBayes documentation built on May 1, 2019, 7:03 p.m.