Table 2.

Number of classes obtained for various clustering methods applied to simulated data

	Case 1 (5 true classes)				Case 2 (4 true classes)
Method	J	Median	Mean	SD	J	Median	Mean	SD
DynTree	25	3	2.5	0.50	25	2	2.0	0.00
	50	3	2.5	0.50	50	2	2.0	0.00
	500	3	2.7	0.58	500	2	2.0	0.00
	1000	3	2.8	0.59	1000	2	2.0	0.00
HOPACH (best)	25	40	38.0	12.10	5	17	18.9	9.10
	50	35	35.4	11.38	10	14	15.0	8.27
	500	23	23.0	9.52	25	25	24.7	9.80
	1000	23	23.1	9.47	50	25	25.3	7.34
HOPACH (greedy)	25	8	13.4	14.41	5	5	7.1	6.35
	50	6	11.9	12.66	10	5	7.1	7.11
	500	5	6.6	5.19	25	7.5	10.8	8.52
	1000	4	6.2	4.41	50	8	10.1	7.85
RPMM	25	8	7.7	2.00	5	2	2.0	0.10
	50	5	5.6	1.32	10	2	2.4	2.28
	500	5	5.0	0.22	25	4	4.0	0.20
	1000	5	5.0	0.00	50	4	4.1	0.58

DynTree = Hierarchical clustering with classes determined by dynamic tree cutting

HOPACH(best) = HOPACH with 'best' number of classes

HOPACH(greedy) = HOPACH with 'greedy' number of classes

RPMM = Recursively partitioned mixture model employing BIC

J = Number of loci considered in analysis