spdep (version 0.6-15)

EBest: Global Empirical Bayes estimator

Description

The function computes global empirical Bayes estimates for rates "shrunk" to the overall mean.

Usage

EBest(n, x, family="poisson")

Arguments

n

a numeric vector of counts of cases

x

a numeric vector of populations at risk

family

either "poisson" for rare conditions or "binomial" for non-rare conditions

Value

A data frame with two columns:

raw

a numerical vector of raw (crude) rates

estmm

a numerical vector of empirical Bayes estimates

and a parameters attribute list with components:

a

global method of moments phi value

m

global method of moments gamma value

Details

Details of the implementation for the "poisson" family are to be found in Marshall, p. 284--5, and Bailey and Gatrell p. 303--306 and exercise 8.2, pp. 328--330. For the "binomial" family, see Martuzzi and Elliott (implementation by Olaf Berke).

References

Marshall R M (1991) Mapping disease and mortality rates using Empirical Bayes Estimators, Applied Statistics, 40, 283--294; Bailey T, Gatrell A (1995) Interactive Spatial Data Analysis, Harlow: Longman, pp. 303--306, Martuzzi M, Elliott P (1996) Empirical Bayes estimation of small area prevalence of non-rare conditions, Statistics in Medicine 15, 1867--1873.

See Also

EBlocal, probmap, EBImoran.mc

Examples

# NOT RUN {
example(auckland)
res <- EBest(auckland$M77_85, 9*auckland$Und5_81)
attr(res, "parameters")
cols <- grey(6:2/7)
brks <- c(-Inf,2,2.5,3,3.5,Inf)
plot(auckland, col=cols[findInterval(res$estmm*1000, brks, all.inside=TRUE)])
legend("bottomleft", fill=cols, legend=leglabs(brks), bty="n")
title(main="Global moment estimator of infant mortality per 1000 per year")
data(huddersfield)
res <- EBest(huddersfield$cases, huddersfield$total, family="binomial")
round(res[,1:2],4)*100
# }