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Define lasso regularization object for predictor and external data

Source: R/define_penalty.R
define_lasso.Rd

Helper function to define a lasso penalty regularization object. See define_penalty for more details.

Usage

define_lasso(
  num_penalty = 20,
  penalty_ratio = NULL,
  user_penalty = NULL,
  custom_multiplier = NULL
)

Arguments

num_penalty

number of penalty values to fit in grid. Default is 20.

penalty_ratio

ratio between minimum and maximum penalty for x. Default is 1e-04 if \(n > p\) and 0.01 if \(n <= p\).

user_penalty

user-defined vector of penalty values to use in penalty path.

custom_multiplier

variable-specific penalty multipliers to apply to overall penalty. Default is 1 for all variables. 0 is no penalization.

Value

A list object with regularization settings that are used to define the regularization for predictors or external data in xrnet and tune_xrnet. The list elements will match those returned by define_penalty, but with the penalty_type automatically set to 1.