Observation sensitivity analysis in beta-binomial model
Computes probability intervals for the log precision parameter K in a beta-binomial model for all "leave one out" models using sampling importance resampling
bayes.influence(theta,data)
theta |
matrix of simulated draws from the posterior of (logit eta, log K) |
data |
matrix with columns of counts and sample sizes |
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 |
Jim Albert
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)
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