define_penalty: Define regularization object for predictor and external data.

In USCbiostats/hierr: Hierarchical Regularized Regression

Define regularization object for predictor and external data.

Description

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)
)

USCbiostats/hierr documentation built on Dec. 12, 2023, 12:55 p.m.