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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 h ^ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGafmiAaGMbaKaaaaa@2D3E@
setting true # z used # z n β ρ Intercept Slope R 2
1 5 5 100 1.10 71.50a(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
  1. This table shows the simulation results of estimated regression coefficients β and the nonparametric function h(·) in model logit(π) = + h(z) for binary outcomes based on 300 runs. True β = 1. In the table, ais the average of the estimated ρ ^ MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacPC6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGafqyWdiNbaKaaaaa@2DA5@ from 300 simulations.