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

1 |

`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 |

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).

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 |

Roger Bivand Roger.Bivand@nhh.no and Olaf Berke, Population Medicine, OVC, University of Guelph, CANADA

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.

1 2 3 4 5 6 7 8 9 10 11 | ```
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
``` |

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