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Table 4 Simulation example 4: mandatory covariates are irrelevant

From: Ridle for sparse regression with mandatory covariates with application to the genetic assessment of histologic grades of breast cancer

 

Method

rpe

g-measure

Specificity (\(\mathcal {M}\))

Sensitivity (\(\mathcal {O}\))

Specificity (\(\mathcal {O}\))

ρ 0=0.25

Ridge

1.671 (0.012)

    
 

Lasso

1.911 (0.022)

0.383 (0.034)

1.000 (0.000)

0.200 (0.028)

0.975 (0.008)

 

Elastic net

1.744 (0.019)

0.585 (0.015)

0.600 (0.053)

0.600 (0.050)

0.835 (0.036)

 

\(\mathcal {M}\)-unpenalized lasso

2.357 (0.032)

0.215 (0.103)

0.000 (0.000)

0.050 (0.024)

0.995 (0.003)

 

\(\mathcal {M}\)-unpenalized elastic net

2.210 (0.034)

0.308 (0.054)

0.000 (0.000)

0.525 (0.065)

0.732 (0.057)

 

Ridle

1.854 (0.012)

0.309 (0.029)

0.000 (0.000)

0.100 (0.024)

0.982 (0.005)

ρ 0=0.5

Ridge

1.807 (0.014)

    
 

Lasso

2.045 (0.035)

0.571 (0.013)

0.800 (0.006)

0.400 (0.039)

0.925 (0.007)

 

Elastic net

1.773 (0.034)

0.667 (0.008)

0.500 (0.048)

0.800 (0.048)

0.756 (0.020)

 

\(\mathcal {M}\)-unpenalized lasso

2.242 (0.023)

0.299 (0.035)

0.000 (0.000)

0.100 (0.021)

0.982 (0.004)

 

\(\mathcal {M}\)-unpenalized elastic net

2.080 (0.028)

0.305 (0.094)

0.000 (0.000)

0.550 (0.072)

0.700 (0.079)

 

Ridle

1.801 (0.039)

0.528 (0.032)

0.000 (0.000)

0.300 (0.038)

0.943 (0.005)

ρ 0=0.75

Ridge

1.564 (0.022)

    
 

Lasso

1.365 (0.029)

0.684 (0.008)

0.700 (0.041)

0.600 (0.012)

0.900 (0.003)

 

Elastic net

1.237 (0.030)

0.745 (0.005)

0.300 (0.046)

0.900 (0.011)

0.775 (0.014)

 

\(\mathcal {M}\)-unpenalized lasso

1.747 (0.043)

0.428 (0.003)

0.000 (0.000)

0.200 (0.000)

0.964 (0.003)

 

\(\mathcal {M}\)-unpenalized elastic net

1.662 (0.043)

0.514 (0.016)

0.000 (0.000)

0.350 (0.023)

0.900 (0.015)

 

Ridle

1.253 (0.042)

0.596 (0.017)

0.000 (0.000)

0.400 (0.026)

0.945 (0.003)

  1. The \(\mathcal {M}\)-unpenalized lasso and \(\mathcal {M}\)-unpenalized elastic net were performed without penalization on the mandatory covariates. g-measure is estimated from all predictors. specificity (\(\mathcal {M}\)) is computed in terms of the mandatory variables only, whereas sensitivity (\(\mathcal {O}\)) and specificity (\(\mathcal {O}\)) are computed in terms of the optional variables only
  2. n=50, p=250, \(|\mathcal {M}|=10\). The smallest rpe and largest two g-measures are boldfaced