mederrRank (version 0.1.0)

EBGM: Geometric Mean of the Relative Risk Empirical Bayes Posterior Distribution

Description

This function computes the geometric mean of the empirical Bayes posterior distribution for the observed vs. expected count relative risk.

Usage

EBGM(eb.result)

Value

EBGM returns the vector of geometric means.

Arguments

eb.result

output of the mixnegbinom.em or negbinom.em commands.

Author

Sergio Venturini ,

Jessica A. Myers

Details

For further details see DuMouchel (1999).

References

DuMouchel W. (1999), "Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System". The American Statistician, 53, 177-190.

Myers, J. A., Venturini, S., Dominici, F. and Morlock, L. (2011), "Random Effects Models for Identifying the Most Harmful Medication Errors in a Large, Voluntary Reporting Database". Technical Report.

See Also

mixnegbinom.em, negbinom.em.

Examples

if (FALSE) {
data("simdata", package = "mederrRank")
summary(simdata)

fit <- bhm.mcmc(simdata, nsim = 1000, burnin = 500, scale.factor = 1.1)
resamp <- bhm.resample(fit, simdata, p.resample = .1,
	k = c(3, 6, 10, 30, 60, Inf), eta = c(.5, .8, 1, 1.25, 2))
fit2 <- bhm.constr.resamp(fit, resamp, k = 3, eta = .8)

theta0 <- c(10, 6, 100, 100, .1)
ans <- mixnegbinom.em(simdata, theta0, 50000, 0.01,
	se = FALSE, stratified = TRUE)

ni <- simdata@numi
rank(EBGM(ans)[1:ni])
summary(fit2, ans, simdata)}