bayes.influence: Observation sensitivity analysis in beta-binomial model

In bayesball/LearnBayes: Learning Bayesian Inference

Description

Computes probability intervals for the log precision parameter K in a beta-binomial model for all "leave one out" models using sampling importance resampling

Usage

bayes.influence(theta,data)

Arguments

theta	matrix of simulated draws from the posterior of (logit eta, log K)
data	matrix with columns of counts and sample sizes

Value

summary	vector of 5th, 50th, 95th percentiles of log K for complete sample posterior
summary.obs	matrix where the ith row contains the 5th, 50th, 95th percentiles of log K for posterior when the ith observation is removed

Author(s)

Jim Albert

Examples

data(cancermortality)
start=array(c(-7,6),c(1,2))
fit=laplace(betabinexch,start,cancermortality)
tpar=list(m=fit$mode,var=2*fit$var,df=4)
theta=sir(betabinexch,tpar,1000,cancermortality)
intervals=bayes.influence(theta,cancermortality)

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