transplantpost: Log posterior of a Pareto model for survival data
In LearnBayes: Functions for Learning Bayesian Inference

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

1	transplantpost(theta,data)

`theta`	vector of parameter values of log tau, log lambda, and log p
`data`	data matrix with columns survival time, transplant indicator, time to transplant, and censoring indicator

value of the log posterior

Jim Albert

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data(stanfordheart)
theta=c(0,3,-1)
transplantpost(theta,stanfordheart)

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

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