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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")

Meta Information

  • Task type: “dens”

  • Predict Types: “pdf”, “cdf”

  • Feature Types: “integer”, “numeric”

  • Required Packages: mlr3extralearners, logspline

Parameters

IdTypeDefaultLevelsRange
lboundnumeric-\((-\infty, \infty)\)
uboundnumeric-\((-\infty, \infty)\)
maxknotsnumeric0\([0, \infty)\)
knotslist-\((-\infty, \infty)\)
nknotsnumeric0\([0, \infty)\)
penaltylist-\((-\infty, \infty)\)
silentlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
mindnumeric-1\((-\infty, \infty)\)
error.actioninteger2\([0, 2]\)

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

Author

RaphaelS1

Super classes

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

Methods

Inherited methods


Method new()

Creates a new instance of this R6 class.

Usage


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

if (requireNamespace("logspline", quietly = TRUE)) {
  learner = mlr3::lrn("dens.logspline")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerDensLogspline:dens.logspline>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, logspline
#> * Predict Type: pdf
#> * Feature types: integer, numeric
#> * Properties: -
#> [1] "lbound"       "ubound"       "maxknots"     "knots"        "nknots"      
#> [6] "penalty"      "silent"       "mind"         "error.action"