Table 4.

Average Relative Efficiency Across Parameters, Simulation A

	Listwise deletion		Log weights		CART weights		RF weights		MI
	30%	50%	30%	50%	30%	50%	30%	50%	30%	50%
N = 100
Linear	.955	.886	.954	.958	1.006	1.053	.994	1.075	.942	.885
One split	.788	.896	.893	.959	1.010	1.095	1.001	1.197	.790	.887
Two splits	.880	.821	.931	.982	1.016	1.049	1.032	1.168	.887	.822
Three splits	.893	.884	.935	.943	1.018	1.073	1.006	1.081	.900	.881
N = 250
Linear	1.006	.970	1.009	1.074	1.085	1.179	1.057	1.233	1.009	.971
One split	.895	.971	1.069	1.072	1.152	1.167	1.331	1.348	.896	.976
Two splits	.902	.902	.978	1.103	1.121	1.131	1.227	1.395	.910	.911
Three splits	.945	.957	1.009	.998	1.135	1.171	1.102	1.191	.948	.946
N = 500
Linear	.940	.917	.948	1.036	1.010	1.051	1.012	1.281	.937	.933
One split	.816	.932	1.005	1.037	1.007	1.019	1.360	1.327	.828	.937
Two splits	.883	.858	.955	1.042	1.052	1.019	1.208	1.507	.89	.872
Three splits	.867	.873	.909	.899	1.040	1.009	1.076	1.276	.868	.883

Note. Relative efficiency is computed as the efficiency of measure X over the efficiency of pruned CART. Log = logistic regression; CART = classification and regression trees; RF = random forests; MI = multiple imputation.