weibullregpost

Computes the log posterior density of (log sigma, mu, beta) for a Weibull
proportional odds regression model

models

A collection of functions helpful in learning the basic tenets of Bayesian statistical inference.  It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions.  It contains MCMC algorithms for summarizing posterior distributions defined by the user.  It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

Jim Albert

LearnBayes

Functions for Learning Bayesian Inference

weibullregpost function

<dl> <dt>theta</dt>
<dd>vector of parameter values log sigma, mu, and beta</dd> <dt>data</dt>
<dd>data matrix with columns survival time, censoring variable, and covariate matrix</dd></dl>

Arguments

Author

Log posterior of a Weibull proportional odds model for survival data — weibullregpost

<dl>

 <dt>theta</dt>
<dd>vector of parameter values log sigma, mu, and beta</dd>

 <dt>data</dt>
<dd>data matrix with columns survival time, censoring variable, and covariate matrix</dd>

</dl>

weibullregpost: Log posterior of a Weibull proportional odds model for survival data

Description

Usage

Value

Arguments

Author

Examples