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Table 3 Summary of results for the simulated data with only main effects

From: Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping

Algorithm Parameters PE ± STE* Number of effects†‡ CPU time (sec)
  (0.001, 0.001) 11.52 ± 0.5677 14/0 11.1 1.2
  (0.01, 0.01) 11.52 ± 0.578 16/0 10.53 1.3
  (0.05, 0.05) 11.36 ± 0.6088 17/0 10.32 1.1
  (0.1, 0.1) 11.23 ± 0.5571 17/0 10.32 1.1
  (0.5, 0.5) 11.32 ± 0.5937 17/0 10.34 1.1
EBLASSO (1, 1) 11.4 ± 0.5929 16/0 10.64 1.1
  (0.5, 0.1) 11.57 ± 0.5593 15/0 10.83 1.3
  (-0.5, 0.1) 10.87 ± 0.5599 17/0 10.31 1.6
  (-0.75, 0.1) 10.78 ± 0.5646 20/5 9.52 1.5
  (-0.95, 0.1) 11.09 ± 0.5045 22/20 8.71 1.4
  (-1, 0.0001) 17.73 ± 2.0244 9/0 16.07 1491.9
  (-1, 0.0005) 15.81 ± 2.5732 16/0 11.66 1676.0
EB (-1, 0.001) 12.21 ± 1.7635 17/2 10.65 1657.9
  (-1, 0.01) 10.69 ± 0.9903 19/4 9.05 1954.9
  (-1, 0.1) 11.63 ± 0.5743 20/20 7.29 2222.7
RVM - - 20/42 7.81 268.7
  0.1347 10.77 ± 0.4583 16/27 9.47 0.7
  0.0850 10.52 ± 0.4442 20/49 8.89 0.7
LASSO 0.0675 10.50 ± 0.5248 20/48 8.63 0.7
  0.0536 10.52 ± 0.4382 19/35 8.28 0.7
  0.0338 10.59 ± 0.4434 17/2 7.35 0.7
  1. Parameters are (a, b) for the EBLASSO, (τ, ω) for the EB and λ for the LASSO.
  2. *The average PE and the standard error were obtained from ten-fold cross validation.
  3. The number of detected effects and residual variance were obtained using all 1000 samples not from cross validation.
  4. The first number is the number of true positive effects; the second number is number of false positive effects. All the effects counted have a p-value ≤ 0.05.