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Define regularization object for predictor and external data.

Source: R/define_penalty.R
define_penalty.Rd

Defines regularization for predictors and external data variables in xrnet fitting. Use helper functions define_lasso, define_ridge, or define_enet to specify a common penalty on x or external.

Usage

define_penalty(
  penalty_type = 1,
  quantile = 0.5,
  num_penalty = 20,
  penalty_ratio = NULL,
  user_penalty = NULL,
  custom_multiplier = NULL
)

Arguments

penalty_type

type of regularization. Default is 1 (Lasso). Can supply either a scalar value or vector with length equal to the number of variables the matrix.

  • 0 = Ridge

  • (0,1) = Elastic-Net

  • 1 = Lasso / Quantile

quantile

specifies quantile for quantile penalty. Default of 0.5 reduces to lasso (currently not implemented).

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:

penalty_type

The penalty type, scalar with value in range [0, 1].

quantile

Quantile for quantile penalty, 0.5 defaults to lasso (not currently implemented).

num_penalty

The number of penalty values in the penalty path.

penalty_ratio

The ratio of the minimum penalty value compared to the maximum penalty value.

user_penalty

User-defined numeric vector of penalty values, NULL if not provided by user.

custom_multiplier

User-defined feature-specific penalty multipliers, NULL if not provided by user.

Examples


# define ridge penalty with penalty grid split into 30 values
my_penalty <- define_penalty(penalty_type = 0, num_penalty = 30)

# define elastic net (0.5) penalty with user-defined penalty
my_custom_penalty <- define_penalty(
  penalty_type = 0.5, user_penalty = c(100, 50, 10, 1, 0.1)
)