optimPenaLikL2: Variable selection based on the combined penalty CL2= (1-w)L0...

In stepPenal: Stepwise Forward Variable Selection in Penalized Regression

Description

Methods to use for optimization include the Hooke-Jeeves derivative-free minimization algorithm (hjk), and the BFGS method (modified Quasi-Newton). This algorithm does variable selection by shrinking the coefficients towards zero using the combined penalty (CL2= (1-w)L0 + wL2).

Usage

optimPenaLikL2(Data, lamda, w, standardize = TRUE, algorithms = c("QN",
  "hjk"))

Arguments

Data	should have the following structure: the first column must be the response variable y
lamda	tuning penalty parameter
w	the weight parameter for the sum (1-w)L0+ wL2
standardize	standardize Logical flag for x variable standardization, prior to fitting the model sequence. The coefficients are always returned on the original scale. Default is standardize=TRUE
algorithms	select between Simulated annealing or Differential evolution

Details

it is recommended to use the tuneParam function to tune parameters lamda and w prior using the optimPenaLik function.

Value

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

Examples

## Not run: 
# use the optimPenaLik 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)

# example with Quasi-Newton:
before <- Sys.time()
PenalQN <- optimPenaLikL2(Data=simData, lamda=2, w=0.6,
                     algorithms=c("QN"))
after <- Sys.time()
after-before
PenalQN

## End(Not run)

stepPenal documentation built on May 1, 2019, 10:11 p.m.