StepPenalL2: Stepwise forward variable selection using penalized...

Description Usage Arguments Details Value See Also Examples

View source: R/add.r

Description

Stepwise forward variable selection based on the combination of L2 and L0 penalties. The optimization is done using the "BFGS" method in stats::optim

Usage

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StepPenalL2(Data, lamda, w, standardize = TRUE)

Arguments

Data

should have the following structure: the first column must be the binary response variable y.

lamda

the tuning penalty parameter

w

the weight parameter for the sum (1-w)L0+ wL2

standardize

Logical flag for the predictors' standardization, prior to fitting the model. Default is standardize=TRUE

Details

lamda and w parameters need to be tuned by cross-Validation using stepPenal::tuneParam

Value

a list with the shrinked coefficients and the names of the selected variables, i.e those variables with an estimated coefficient different from zero.

See Also

optim

Examples

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# use the StepPenal function on a simulated dataset, with given lamda and w.

set.seed(14)
beta    <- c(3, 2, -1.6, -1)
noise   <- 5
simData <- SimData(N=100, beta=beta, noise=noise, corr=TRUE)
## Not run: 
before <- Sys.time()
stepPenalL2 <- StepPenalL2(Data=simData, lamda=1.5, w=0.6)
after <- Sys.time()
after-before

(varstepPenal<- stepPenalL2$coeffP)

## End(Not run)

stepPenal documentation built on Aug. 25, 2018, 1:03 a.m.