transplantpost

Computes the log posterior density of (log tau, log lambda, log p) for a Pareto
model for survival data

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

transplantpost function

<dl> <dt>theta</dt>
<dd>vector of parameter values of log tau, log lambda, and log p</dd> <dt>data</dt>
<dd>data matrix with columns survival time, transplant indicator, time to transplant, and
censoring indicator</dd></dl>

Arguments

Author

Log posterior of a Pareto model for survival data — transplantpost

<dl>

 <dt>theta</dt>
<dd>vector of parameter values of log tau, log lambda, and log p</dd>

 <dt>data</dt>
<dd>data matrix with columns survival time, transplant indicator, time to transplant, and
censoring indicator</dd>

</dl>

transplantpost: Log posterior of a Pareto model for survival data

Description

Usage

Value

Arguments

Author

Examples