logisticpost: Log posterior for a binary response model with a logistic...

In bayesball/LearnBayes: Learning Bayesian Inference

Description

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)

Usage

logisticpost(beta,data)

Arguments

beta	vector of parameter values beta0 and beta1
data	matrix of columns of covariate values x, sample sizes n, and number of successes y

Value

value of the log posterior

Author(s)

Jim Albert

Examples

x = c(-0.86,-0.3,-0.05,0.73)
n = c(5,5,5,5)
y = c(0,1,3,5)
data = cbind(x, n, y)
beta=c(2,10)
logisticpost(beta,data)

bayesball/LearnBayes documentation built on May 11, 2019, 9:21 p.m.