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. 2016 Apr 27;203(3):1425–1438. doi: 10.1534/genetics.115.185181

Table 1. Parameter estimates, model goodness of fit, model complexity, and predictive accuracy (case study I).

Whole data analysis 200 CVs
Model Predictors Log likelihoodb Effective number of parameters (pD) Deviance information criteria (DIC) Average CV-AUCc Proportion of times (of 200 CVs) model in column had AUC > model in row
Age at diagnosis Racea Lobular (Y/N) Tumor subtype Pathological stage Gene expression M2 M3 M4 M5 M6 M7 (COV) M8 (COV + WGGE)
M1 X −146.1 2.1 294.3 0.557d (0.007) 0.14 <0.01 >0.99 >0.99 >0.99 >0.99 >0.99
M2 X −147.5 2.0 296.9 0.525d,e (0.023) 0.59 >0.99 >0.99 >0.99 >0.99 >0.99
M3 X −144.3 2.0 290.6 0.526e (0.020) >0.99 >0.99 >0.99 >0.99 >0.99
M4 X −138.6 4.1 281.3 0.618f (0.013) 0.14 >0.99 >0.99 >0.99
M5 X −142.4 2.0 286.9 0.596f (0.012) >0.99 >0.99 >0.99
M6 X −132.4 15.5 280.3 0.659g (0.011) >0.99 >0.99
M7: COV X X X X X −146.3 3.2 295.8 0.704h (0.007) >0.99
M8: COV + WGGE X X X X X X −131.3 17.6 280.3 0.721i (0.010)
a

African American, Y/N.

b

Estimated posterior mean of the log likelihood.

c

Average over 200 tenfold CVs.

d,e,f,g,h,i

The same letter indicates that the models are no different (empirical P < 0.05).