Skip to main content

Table 2 Performance of different GLM methods for Poisson regression over 100 simulations, where values in the parenthesis are the standard deviations, and ANSF: Average number of selected features; rMSE: Average square root of mean squared error; \(|\hat { {\beta } }- {\beta } | =\sum _{i} |\hat { {\beta } }_{i} - {\beta }_{i}|\): average absolute bias when comparing true and estimated parameters

From: Sparse generalized linear model with L 0 approximation for feature selection and prediction with big omics data

 

PMS

glmnet

SparseReg

L 0ADRIDGE

  

L 1

SCAD

MC+

 
 

rMSE

1.10(±.091)

1.090(±.092)

1 . 0 8 7 ( ± . 0 9 1 )

1.937(±.222)

N =100

\(|\hat { {\beta }} - {\beta } |\)

1.755(±.274)

1.754(±.275)

1.737±.273)

0 . 2 2 2 ( ± . 1 1 6 )

P =100

ANSF

43.03(±3.52)

43.07(±3.57)

42.06(±3.51)

3 . 9 9 ( ± . 1 0 0 )

 

PTM

0%

0%

0%

9 9 %

 

FDR

90.6%

90.6%

90.6%

0 %

 

rMSE

0.503(±.017)

0.502(±.017)

0 . 5 0 1 ( ± . 0 1 8 )

2.108(±.359)

N =100

\(|\hat { {\beta }} - {\beta } |\)

2.671(±.421)

2.673(±.425)

2.821±2.012)

0 . 4 2 4 ( ± . 3 5 0 )

P=103

ANSF

75.47(±5.61)

75.82(±5.71)

75.14(±8.69)

3 . 6 1 0 ( ± . 6 0 1 )

 

PTM

0%

0%

0%

6 4 %

 

FDR

94.7%

94.7%

94.6%

2 . 4 %

 

rMSE

0 . 2 7 1 ( ± . 0 0 4 )

0.272(±.012)

0.275(±.025)

1.916(±.081)

N =500

\(|\hat { {\beta }} - {\beta } |\)

5.845(±.280)

6.185(±2.359)

5.807±.273)

0 . 0 8 6 ( ± . 0 3 3 )

P=104

ANSF

465.6(±14.1)

475.1(±15.5)

463.6(±13.9)

4 . 0 0 0 ( ± . 0 0 0 )

 

PTM

0%

0%

0%

1 0 0 %

 

FDR

99.1%

99.2%

99.1%

0 %

  1. PMS: Performance Measures. PTM: Percentage of true models. FDR: False discovery rate. The values in boldface indicate the best performance