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

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. 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)

[Package LearnBayes version 2.15 Index]