betabinexch {LearnBayes}R Documentation

Log posterior of logit mean and log precision for Binomial/beta exchangeable model

Description

Computes the log posterior density of logit mean and log precision for a Binomial/beta exchangeable model

Usage

betabinexch(theta,data)

Arguments

theta matrix of parameter values where each row represents a value of (logit eta, log K)
data a matrix with columns y (counts) and n (sample sizes)

Value

vector of values of the log posterior where each value corresponds to each row of the parameters in theta

Author(s)

Jim Albert

Examples

n=c(20,20,20,20,20)
y=c(1,4,3,6,10)
data=cbind(y,n)
par1=c(-1,0)
par2=c(-1,1)
theta=rbind(par1,par2)
betabinexch(theta,data)

[Package LearnBayes version 1.0 Index]