Stepwise Forward Variable Selection in Penalized Regression

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Documentation for package ‘stepPenal’ version 0.2

• DESCRIPTION file.

Help Pages

findROC	Compute the area under the ROC curve
lassomodel	Fits a lasso model and a lasso followed by a stepAIC algorithm.
objFun	Objective function
optimPenaLik	Variable selection based on the combined penalty CL= (1-w)L0 + wL1
optimPenaLikL2	Variable selection based on the combined penalty CL2= (1-w)L0 + wL2
penalBrier	Evaluation of the performance of risk prediction models with binary status response variable.
SimData	Simulate data with normally distributed predictors and binary response
stepaic	Stepwise forward variable selection based on the AIC criterion
StepPenal	Stepwise forward variable selection using penalized regression.
StepPenalL2	Stepwise forward variable selection using penalized regression.
tuneParam	Tune parameters w and lamda using the CL penalty
tuneParamCL2	Tune parameters w and lamda using the CL2 penalty