logisticpost

Computes the log posterior density of (beta0, beta1) when
yi are independent binomial(ni, pi) and logit(pi)=beta0+beta1*xi and
a uniform prior is placed on (beta0, beta1)

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

logisticpost function

<dl> <dt>beta</dt>
<dd>vector of parameter values beta0 and beta1</dd> <dt>data</dt>
<dd>matrix of columns of covariate values x, sample sizes n, and number of successes y</dd></dl>

Arguments

Author

Log posterior for a binary response model with a logistic link and a uniform prior — logisticpost

<dl>

 <dt>beta</dt>
<dd>vector of parameter values beta0 and beta1</dd>

 <dt>data</dt>
<dd>matrix of columns of covariate values x, sample sizes n, and number of successes y</dd>

</dl>

logisticpost: Log posterior for a binary response model with a logistic link and a uniform prior

Description

Usage

Value

Arguments

Author

Examples