Stepwise Forward Variable Selection in Penalized Regression
Compute the area under the ROC curve
Fits a lasso model and a lasso followed by a stepAIC algorithm.
Objective function
Variable selection based on the combined penalty CL= (1-w)L0 + wL1
Variable selection based on the combined penalty CL2= (1-w)L0 + wL2
Evaluation of the performance of risk prediction models with binary st...
Simulate data with normally distributed predictors and binary response
Stepwise forward variable selection based on the AIC criterion
Stepwise forward variable selection using penalized regression.
Stepwise forward variable selection using penalized regression.
Tune parameters w and lamda using the CL penalty
Tune parameters w and lamda using the CL2 penalty
Model Selection Based on Combined Penalties. This package implements a stepwise forward variable selection algorithm based on a penalized likelihood criterion that combines the L0 with L2 or L1 norms.