Density Logspline Learner

Calls logspline::logspline from package logspline.

Dictionary

This Learner can be instantiated via the dictionary mlr_learners or with the associated sugar function lrn():

mlr_learners$get("dens.logspline")
lrn("dens.logspline")

Traits

• Packages: logspline

• Predict Types: pdf, cdf

• Feature Types: integer, numeric

• Properties:

References

Charles Kooperberg and Charles J. Stone. Logspline density estimation for censored data (1992). Journal of Computational and Graphical Statistics, 1, 301–328.

See also

Dictionary of Learners: mlr3::mlr_learners

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensLogspline

Methods

Public methods

• LearnerDensLogspline$new()

• LearnerDensLogspline$clone()

Inherited methods

• mlr3::Learner$format()

• mlr3::Learner$help()

• mlr3::Learner$predict()

• mlr3::Learner$predict_newdata()

• mlr3::Learner$print()

• mlr3::Learner$reset()

• mlr3::Learner$train()

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Method new()

Creates a new instance of this R6 class.

Usage

LearnerDensLogspline$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerDensLogspline$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# stop example failing with warning if package not installed
learner = suppressWarnings(mlr3::lrn("dens.logspline"))
print(learner)
#> <LearnerDensLogspline:dens.logspline>
#> * Model: -
#> * Parameters: list()
#> * Packages: logspline
#> * Predict Type: pdf
#> * Feature types: integer, numeric
#> * Properties: -

# available parameters:
learner$param_set$ids()
#> [1] "lbound"       "ubound"       "maxknots"     "knots"        "nknots"      
#> [6] "penalty"      "silent"       "mind"         "error.action"