R: Observation sensitivity analysis in beta-binomial model

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 Index]