Description Usage Arguments Value Author(s) Examples

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.

1 | ```
bayes.probit(y,X,m,prior=list(beta=0,P=0))
``` |

`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 |

`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 |

Jim Albert

1 2 3 4 5 6 |

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