R: mxPenaltyElasticNet

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 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.

[Package OpenMx version 2.20.6 Index]