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

In stepPenal: Stepwise Forward Variable Selection in Penalized Regression

Description

Methods to use for optimization include Hooke-Jeeves derivative-free minimization algorithm (hjk), or the BFGS method (modified Quasi-Newton). This method does variable selection by shrinking the coefficients towards zero using the combined penalty (CL= (1-w)L0 + wL1).

Usage

optimPenaLik(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+ wL1
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 BFGS ('QN') or Hooke-Jeeves (hjk) algorithm.

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.

See Also

optim

Examples

# use the optimPenaLik function on a simulated dataset, with given lamda and w.
## Not run: 
set.seed(14)
beta    <- c(3, 2, -1.6, -1)
noise   <- 5
simData <- SimData(N=100, beta=beta, noise=noise, corr=TRUE)

# use BFGS

before  <- Sys.time()
PenalQN <- optimPenaLik(Data=simData, lamda=1.5, w=0.7,
                     algorithms=c("QN"))
(tot <- Sys.time()-before)
PenalQN


# use Hooke-Jeeves algorithm

before  <- Sys.time()
Penalhjk <- optimPenaLik(Data=simData, lamda=1.5, w=0.7,
                       algorithms=c("hjk"))
(totRun  <- Sys.time() - before)
# total run of approx 0.25sec

Penalhjk

## End(Not run)

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