R: Expectation-Maximization Algorithm for the Negative Binomial...
negbinom.em
R Documentation
Expectation-Maximization Algorithm for the Negative Binomial Distribution
Description
This function provides the empirical Bayes estimates for the parameters theta of a negative binomial distribution (see dnegbinom) using an Expectation-Maximization algorithm.
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
dnegbinom,
EBGM,
mixnegbinom.em.
Examples
data("simdata", package = "mederrRank")
summary(simdata)
## Not run:
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)
plot(fit, fit2, simdata)
## End(Not run)
theta0 <- runif(2, 0, 5)
ans <- negbinom.em(simdata, theta0, 50000, 0.01,
se = TRUE, stratified = TRUE)
ans$theta
ans$se
## Not run:
summary(fit2, ans, simdata)
## End(Not run)