EBest: Global Empirical Bayes estimator
In spdep: Spatial Dependence: Weighting Schemes, Statistics and Models

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

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

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

`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.

EBlocal, probmap, EBImoran.mc

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

Loading required package: sp
Loading required package: Matrix

acklnd> require(maptools)
Loading required package: maptools
Checking rgeos availability: TRUE

acklnd> auckland <- readShapePoly(system.file("etc/shapes/auckland.shp",
acklnd+  package="spdep")[1])

acklnd> auckland.nb <- poly2nb(auckland)
Warning message:
use rgdal::readOGR or sf::st_read 
$a
[1] 7.284173e-07

$b
[1] 0.002633436

     raw estmm
1  42.86 34.44
2  28.95 29.56
3  28.31 28.94
4  21.43 28.93
5  33.33 30.72
6  30.86 30.43
7  33.70 31.85
8  45.61 35.93
9  26.67 28.95
10 29.41 29.95
11 27.76 28.36
12 33.75 31.75
13 32.67 31.84
14 22.91 25.36
15 34.21 32.33
16 33.77 31.72
17 29.09 29.69
18 15.87 24.36
19 40.91 32.11
20 40.00 31.01
21 28.79 29.53
22 38.89 31.47
23 28.32 29.10
24 33.33 31.44
25 13.95 22.36
26 33.77 31.72
27 31.58 30.31
28 33.33 30.42
29 26.67 28.26
30 63.64 33.57
31 34.38 32.24
32 20.00 27.62
33 19.15 24.60
34 25.53 28.54
35 24.14 27.79
36 44.00 32.98
37 25.33 27.95
38 18.18 26.96
39 31.78 30.97
40 32.20 30.88
41 26.19 28.23
42 19.05 26.65
43 16.67 26.81
44 20.00 28.66
45 32.99 31.54
46 50.00 31.97
47 37.21 32.30
48 32.79 31.13
49 38.89 31.47
50 25.00 27.26
51 26.03 28.29
52 30.43 30.18
53 19.75 25.28
54 50.00 31.61
55 21.62 27.67
56 22.22 27.88
57 36.36 30.71
58 31.25 30.22
59  0.00 29.73
60 29.41 29.95
61 31.16 30.71
62 33.33 30.58
63 33.87 32.22
64 30.00 30.04
65 30.34 30.19
66 40.74 35.00
67 46.43 38.96
68 34.21 31.91
69 24.26 26.10
70 29.41 29.75
71 47.17 39.13