Table 5

Maximum posterior classification (MAP) for women, J = 7
Region Name Class e.1 e.2 e.3 e.4 e.5 e.6 e.7
1 Düsseldorf 3 0 0 0.98 0.02 0 0 0
2 Duisburg 2 0 1 0 0 0 0 0
3 Essen 3 0 0.05 0.95 0 0 0 0
4 Krefeld 4 0 0 0.01 0.99 0 0 0
5 Mönchengladbach 2 0 1 0 0 0 0 0
6 Mülheim a.d. Ruhr 4 0 0 0 1 0 0 0
7 Oberhausen 2 0 1 0 0 0 0 0
8 Remscheid 3 0 0 1 0 0 0 0
9 Solingen 4 0 0 0 0.96 0.04 0 0
10 Wuppertal 4 0 0 0.12 0.88 0 0 0
11 Kleve 3 0 0 1 0 0 0 0
12 Mettmann 5 0 0 0 0 1 0 0
13 Neuss 5 0 0 0 0 1 0 0
14 Viersen 3 0 0 0.98 0.02 0 0 0
15 Wesel 4 0 0 0.13 0.87 0 0 0
16 Aachen (city) 5 0 0 0 0 1 0 0
17 Bonn 7 0 0 0 0 0 0 1
18 Köln 3 0 0 0.88 0.12 0 0 0
19 Leverkusen 5 0 0 0 0 1 0 0
20 Aachen (rural) 3 0 0 1 0 0 0 0
21 Düren 3 0 0 0.97 0.03 0 0 0
22 Erftkreis 3 0 0 0.83 0.17 0 0 0
23 Euskirchen 3 0 0 1 0 0 0 0
24 Heinsberg 3 0 0 0.79 0.21 0 0 0
25 Oberbergischer Kreis 4 0 0 0 1 0 0 0
26 Rheinisch-Bergischer Kreis 6 0 0 0 0 0 1 0
27 Rhein-Sieg-Kreis 5 0 0 0 0 0.74 0.26 0
28 Bottrop 3 0 0.01 0.99 0 0 0 0
29 Gelsenkirchen 1 1 0 0 0 0 0 0
30 Münster 7 0 0 0 0 0 0 1
31 Borken 5 0 0 0 0 0.98 0.02 0
32 Coesfeld 6 0 0 0 0 0 1 0
33 Recklinghausen 2 0 0.94 0.06 0 0 0 0
34 Steinfurt 6 0 0 0 0 0 1 0
35 Warendorf 6 0 0 0 0 0.44 0.56 0
36 Bielefeld 6 0 0 0 0 0 1 0
37 Gütersloh 6 0 0 0 0 0 1 0
38 Herford 6 0 0 0 0 0 1 0
39 Höxter 6 0 0 0 0 0.09 0.91 0
40 Lippe 6 0 0 0 0 0 1 0
41 Minden-Lübbecke 6 0 0 0 0 0.01 0.99 0
42 Paderborn 6 0 0 0 0 0.02 0.98 0
43 Bochum 3 0 0 1 0 0 0 0
44 Dortmund 2 0 1 0 0 0 0 0
45 Hagen 4 0 0 0 1 0 0 0
46 Hamm 4 0 0 0.27 0.73 0 0 0
47 Herne 2 0 1 0 0 0 0 0
48 Ennepe-Ruhr-Kreis 4 0 0 0 1 0 0 0
49 Hochsauerlandkreis 5 0 0 0 0 1 0 0
50 Märkischer Kreis 3 0 0 1 0 0 0 0
51 Olpe 5 0 0 0 0 0.84 0.16 0
52 Siegen-Wittgenstein 5 0 0 0 0.06 0.94 0 0
53 Soest 4 0 0 0.32 0.68 0 0 0
54 Unna 4 0 0 0.07 0.93 0 0 0

Böhning et al.

Böhning et al. BMC Medical Research Methodology 2013 13:36   doi:10.1186/1471-2288-13-36

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