mxPenaltyElasticNet {OpenMx} | R Documentation |
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
, 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 |
... |
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.