EBGM | R Documentation |
This function computes the geometric mean of the empirical Bayes posterior distribution for the observed vs. expected count relative risk.
EBGM(eb.result)
eb.result |
output of the |
For further details see DuMouchel (1999).
EBGM
returns the vector of geometric means.
Sergio Venturini sergio.venturini@unicatt.it,
Jessica A. Myers jmyers6@partners.org
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.
mixnegbinom.em
,
negbinom.em
.
## 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)
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