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Table 7 Comparison among DPART, FUM, and FCM in data sets with unbalanced sample sizes

From: Characterization of a Bayesian genetic clustering algorithm based on a Dirichlet process prior and comparison among Bayesian clustering methods

 

Nl = 20

    

Nl = 50

 

M = 0.003

   

M = 0.001

M = 0.003

 

N (10, 10)

N (10, 100)

N (10, 200)

N (10, 300)

N (10, 300)

N (10, 300)

DPART

0.056 (83)

0.018 (95)

0.025 (94)

0.023 (96)

0.001 (100)

0.002 (100)

 

2.42

2.07

2.10

2.05

2.19

2.23

FUM

0.024 (96)

0.010 (100)

0.009 (99)

0.095 (54)

0.001 (100)

0.001 (100)

FCM

0.053 (89)

0.041 (83)

0.146 (10)

0.190 (0)

0.024 (84)

0.021 (90)

  1. Average , the number of data sets in which was 0.1 or less (in parentheses), and the average K values (in Italic) are shown. Nl and M indicate the number of loci and the migration rate, respectively. N ( ) denotes sample sizes.