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. 2016 Oct 31;205(1):77–88. doi: 10.1534/genetics.116.192195

Table 2. Estimates of four measures over 50 replicates under different simulated scenarios.

Deviance MSE AUC Misclassification
Scenario 1 n = 500, m = 1000
Lasso 638.983(17.431) 0.224(0.008) 0.693(0.031) 0.359(0.029)
sslasso: s0 = 0.01 685.229(18.922) 0.246(0.009) 0.595(0.067) 0.442(0.051)
sslasso: s0 = 0.02 643.119(23.694) 0.226(0.011) 0.681(0.038) 0.371(0.031)
sslasso: s0 = 0.03 637.226(22.197) 0.223(0.010) 0.692(0.033) 0.362(0.029)
sslasso: s0 = 0.04 636.930(18.928)a 0.223(0.009) 0.692(0.029) 0.364(0.026)
sslasso: s0 = 0.05 639.385(16.732) 0.224(0.008) 0.694(0.029) 0.364(0.022)
sslasso: s0 = 0.06 639.784(17.359) 0.224(0.008) 0.684(0.029) 0.369(0.024)
sslasso: s0 = 0.07 645.151(19.752) 0.227(0.009) 0.674(0.028) 0.372(0.025)
Scenario 2 n = 500, m = 1000
Lasso 601.872(15.666) 0.207(0.007) 0.753(0.025) 0.320(0.022)
sslasso: s0 = 0.01 640.816(26.885) 0.224(0.012) 0.686(0.042) 0.367(0.031)
sslasso: s0 = 0.02 581.940(24.945) 0.199(0.012) 0.761(0.033) 0.308(0.028)
sslasso: s0 = 0.03 581.661(28.271)a 0.198(0.010) 0.765(0.028) 0.306(0.023)
sslasso: s0 = 0.04 583.037(21.964) 0.199(0.009) 0.764(0.026) 0.307(0.021)
sslasso: s0 = 0.05 590.185(19.343) 0.202(0.008) 0.755(0.023) 0.314(0.018)
sslasso: s0 = 0.06 595.879(19.388) 0.204(0.008) 0.751(0.024) 0.328(0.018)
sslasso: s0 = 0.07 603.756(20.020) 0.208(0.008) 0.738(0.024) 0.333(0.020)
Scenario 3 n = 500, m = 1000
Lasso 561.917(14.623) 0.190(0.006) 0.790(0.021) 0.289(0.020)
sslasso: s0 = 0.01 585.600(34.703) 0.201(0.014) 0.759(0.035) 0.318(0.030)
sslasso: s0 = 0.02 531.956(26.214) 0.180(0.010) 0.808(0.021) 0.269(0.022)
sslasso: s0 = 0.03 532.747(26.343) 0.179(0.010) 0.808(0.021) 0.271(0.022)
sslasso: s0 = 0.04 530.781(24.638)a 0.179(0.010) 0.809(0.020) 0.274(0.020)
sslasso: s0 = 0.05 541.192(24.496) 0.182(0.010) 0.802(0.020) 0.279(0.019)
sslasso: s0 = 0.06 550.971(25.065) 0.186(0.010) 0.794(0.020) 0.284(0.019)
sslasso: s0 = 0.07 559.430(24.311) 0.190(0.009) 0.785(0.020) 0.293(0.019)
Scenario 4 n = 500, m = 3000
Lasso 655.349(11.253) 0.232(0.005) 0.665(0.028) 0.382(0.024)
sslasso: s0 = 0.01 680.988(16.432) 0.244(0.008) 0.601(0.058) 0.430(0.119)
sslasso: s0 = 0.02 655.714(23.241) 0.231(0.010) 0.663(0.034) 0.385(0.027)
sslasso: s0 = 0.03 646.877(20.963) 0.228(0.009) 0.673(0.030) 0.372(0.026)
sslasso: s0 = 0.04 645.278(16.039)a 0.227(0.007) 0.674(0.024) 0.377(0.022)
sslasso: s0 = 0.05 654.349(16.241) 0.231(0.007) 0.659(0.027) 0.390(0.023)
sslasso: s0 = 0.06 665.488(18.227) 0.236(0.008) 0.646(0.028) 0.400(0.026)
sslasso: s0 = 0.07 675.374(20.660) 0.241(0.009) 0.634(0.028) 0.404(0.026)
Scenario 5 n = 500, m = 3000
Lasso 620.034(16.209) 0.215(0.007) 0.726(0.030) 0.334(0.027)
sslasso: s0 = 0.01 642.083(30.947) 0.225(0.014) 0.683(0.056) 0.363(0.045)
sslasso: s0 = 0.02 597.547(34.288) 0.205(0.015) 0.745(0.039) 0.322(0.033)
sslasso: s0 = 0.03 593.701(32.304)a 0.205(0.013) 0.746(0.034) 0.318(0.030)
sslasso: s0 = 0.04 596.421(30.006) 0.205(0.012) 0.746(0.032) 0.324(0.029)
sslasso: s0 = 0.05 610.549(24.024) 0.211(0.010) 0.731(0.030) 0.333(0.025)
sslasso: s0 = 0.06 623.014(24.530) 0.217(0.011) 0.715(0.032) 0.347(0.028)
sslasso: s0 = 0.07 634.536(26.023) 0.222(0.011) 0.701(0.033) 0.355(0.026)
Scenario 6 n = 500, m = 3000
Lasso 570.138(17.989) 0.193(0.008) 0.791(0.026) 0.289(0.026)
sslasso: s0 = 0.01 568.332(35.346) 0.194(0.015) 0.777(0.036) 0.302(0.029)
sslasso: s0 = 0.02 537.665(28.103) 0.180(0.011) 0.806(0.025) 0.275(0.028)
sslasso: s0 = 0.03 530.081(29.097)a 0.178(0.012) 0.812(0.025) 0.271(0.027)
sslasso: s0 = 0.04 530.535(26.149) 0.178(0.011) 0.811(0.023) 0.266(0.025)
sslasso: s0 = 0.05 542.091(26.825) 0.184(0.011) 0.801(0.024) 0.275(0.024)
sslasso: s0 = 0.06 557.014(27.697) 0.189(0.011) 0.788(0.025) 0.288(0.022)
sslasso: s0 = 0.07 572.405(28.018) 0.195(0.011) 0.776(0.025) 0.299(0.024)

Values in parentheses are SE. The slab scales, s1, are 1 in all scenarios.

aThe smallest deviance values indicate the optimal model.