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Table 4 Summary of the results of the EBLASSO algorithm for the real data

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

a= b PE ± STE* Number of effects†‡
0.001 0.70 ± 0.21 1/1/1 0.6706
0.01 0.79 ± 0.31 2/2/2 0.5996
0.05 0.70 ± 0.21 11/11/11 0.2699
0.07 0.96 ± 0.30 10/15/15 0.2104
0.1 1.20 ± 0.18 13/128/132 2.59E-06
0.5 1.21 ± 0.09 9/112/122 2.59E-06
1 1.25 ± 0.17 8/115/132 2.59E-06
  1. *The average PE and the standard error were obtained from five-fold cross validation.
  2. The number of effects and residual variance were obtained using all 150 samples not from cross validation.
  3. The first number is the number of effects with a p-value ≤ 0.05 and proportion of variance ≥ 0.5%; the second number is the number of effects with a p-value ≤ 0.05; the third number is the total number of non-zero effects reported by the program.