bayes.influence {LearnBayes} | R Documentation |
Observation sensitivity analysis in beta-binomial model
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)
[Package LearnBayes version 2.15.1 Index]