Lrnr_density_gaussian: sl3 extension: Density Estimation With Mean Model and...

In alexpkeil1/vibr: Variable Importance in Black-Box Regression

sl3 extension: Density Estimation With Mean Model and Homoscedastic normal errors

Description

This learner assumes a mean model with homoscedastic errors: Y ~ E(Y|W) + epsilon. E(Y|W) is fit using a glm, and then the errors are assumed normally distributed epsilon_i ~ Normal(0, sigma_i) where sigma_i is the estimated standard error of the residual.

Format

R6Class object.

Value

Learner object with methods for training and prediction. See Lrnr_base for documentation on learners.

Parameters

intercept, default=TRUE

include intercept in mean model

transfun, default=identity

function to transform outcome

Super class

sl3::Lrnr_base -> Lrnr_density_gaussian

Methods

Public methods

• Lrnr_density_gaussian$new()

• Lrnr_density_gaussian$clone()

Inherited methods

• sl3::Lrnr_base$assert_trained()
• sl3::Lrnr_base$base_chain()
• sl3::Lrnr_base$base_predict()
• sl3::Lrnr_base$base_train()
• sl3::Lrnr_base$chain()
• sl3::Lrnr_base$custom_chain()
• sl3::Lrnr_base$get_outcome_range()
• sl3::Lrnr_base$get_outcome_type()
• sl3::Lrnr_base$predict()
• sl3::Lrnr_base$predict_fold()
• sl3::Lrnr_base$print()
• sl3::Lrnr_base$process_formula()
• sl3::Lrnr_base$reparameterize()
• sl3::Lrnr_base$retrain()
• sl3::Lrnr_base$sample()
• sl3::Lrnr_base$set_train()
• sl3::Lrnr_base$subset_covariates()
• sl3::Lrnr_base$train()
• sl3::Lrnr_base$train_sublearners()

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Method new()

Usage

Lrnr_density_gaussian$new(intercept = TRUE, transfun = function(x) x, ...)

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Method clone()

The objects of this class are cloneable with this method.

Usage

Lrnr_density_gaussian$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

Other Learners: Lrnr_multinom, Lrnr_polspline_quiet, Lrnr_solnp_density_quiet, Lrnr_solnp_quiet, Lrnr_stepwise

alexpkeil1/vibr documentation built on Sept. 13, 2023, 3:20 a.m.