BMC Bioinformatics

Table 2 Simulation results on estimation.

From: Estimation and testing for the effect of a genetic pathway on a disease outcome using logistic kernel machine regression via logistic mixed models

				Model Parameter Estimates		Reg of h on $\hat{h}$
setting	true # z	used # z	n	β	ρ	Intercept	Slope	R ²
1	5	5	100	1.10	71.50^a(estimated)	-0.06	1.06	0.82
				1.14	1.00 (fixed)	-0.28	1.48	0.79
				1.08	20.00 (fixed)	-0.08	1.15	0.84
2	5	5	200	0.99	90.03 (estimated)	0.01	1.04	0.87
				1.05	1.00 (fixed)	-0.01	1.13	0.84
				0.96	20.00 (fixed)	-0.00	1.07	0.87
3	5	5	300	0.98	111.76 (estimated)	-0.01	1.04	0.90
				1.03	1.00 (fixed)	-0.02	1.10	0.87
				0.97	20.00 (fixed)	-0.01	1.06	0.90

This table shows the simulation results of estimated regression coefficients β and the nonparametric function h(·) in model logit(π) = xβ+ h(z) for binary outcomes based on 300 runs. True β = 1. In the table, ^ais the average of the estimated $\hat{ρ}$ from 300 simulations.

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ISSN: 1471-2105

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