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

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/mederrRank.R

Description

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

Usage

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EBGM(eb.result)

Arguments

eb.result

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

Details

For further details see DuMouchel (1999).

Value

EBGM returns the vector of geometric means.

Author(s)

Sergio Venturini [email protected],

Jessica A. Myers [email protected]

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

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## Not run: 
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)
## End(Not run)

mederrRank documentation built on May 30, 2017, 2:55 a.m.