Skip to main content

Table 2 Cross-validations of the EBLASSO-NE, EBLASSO-NEG and LASSO for the simulation with only main effects

From: Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping

Algorithm

Parametersa

logL± STEb

 

0.0011

−0.39 ± 0.03

 

0.0022

−0.42 ± 0.03

 

0.0447

−0.42 ± 0.04

EBLASSO-NE

0.0500

−0.36 ± 0.02c

 

0.0631

−0.39 ± 0.02

 

0.1259

−0.41 ± 0.03

 

0.2512

−0.40 ± 0.01

 

(−0.5,0.05)

−0.38 ± 0.03

 

(0.01,0.05)

−0.37 ± 0.02

 

(1,0.05)

−0.47 ± 0.02

EBLASSO-NEG

(0.01,5)

−0.39 ± 0.03

 

(0.01,6)

–0.36 ± 0.02c

 

(0.01,7)

−0.37 ± 0.02

 

0.1037

−0.56 ± 0.02

 

0.0516

−0.44 ± 0.03

LASSO

0.0257

−0.37 ± 0.04

 

0.0128

−0.35 ± 0.05c

 

0.0064

−0.36 ± 0.06

  1. aParameters are λ for EBLASSO-NE and LASSO, (a, b) for EBLASSO-NEG.
  2. bThe average log likelihood and standard error were obtained from ten-fold cross validation.
  3. cThe optimal log likelihood and corresponding parameter(s) chosen for comparison with other methods.