stepPenal (version 0.2)

optimPenaLikL2: Variable selection based on the combined penalty CL2= (1-w)L0 + wL2

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

Value

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

Details

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

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
# }
# NOT RUN {
# }