mxPenaltyElasticNet: mxPenaltyElasticNet

In OpenMx: Extended Structural Equation Modelling

mxPenaltyElasticNet

Description

Elastic net regularization

Usage

mxPenaltyElasticNet(
  what,
  name,
  alpha = 0,
  alpha.step = 0.1,
  alpha.max = 1,
  lambda = 0,
  lambda.step = 0.1,
  lambda.max = 0.4,
  alpha.min = NA,
  lambda.min = NA,
  epsilon = 1e-05,
  scale = 1,
  ...,
  hyperparams = c("alpha", "lambda")
)

Arguments

what	A character vector of parameters to regularize
name	Name of the regularizer object
alpha	strength of the mixing parameter to be applied at start (default 0.5). Note that 0 indicates a ridge regression with penalty \frac{lambda}{2} , and 1 indicates a LASSO regression with penalty lambda.
alpha.step	alpha step during penalty search (default 0.1)
alpha.max	when to end the alpha search (default 1)
lambda	strength of the penalty to be applied at starting values (default 0)
lambda.step	step function for lambda step (default .01)
lambda.max	end of lambda range (default .4)
alpha.min	beginning of the alpha range (default 0)
lambda.min	beginning of the lambda range (default lambda)
epsilon	how close to zero is zero?
scale	a given parameter is divided by scale before comparison with epsilon
...	Not used. Forces remaining arguments to be specified by name
hyperparams	a character vector of hyperparameter names

Details

Applies elastic net regularization. Elastic net is a weighted combination of ridge and LASSO penalties.

OpenMx documentation built on Nov. 8, 2023, 1:08 a.m.