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Calls gss::ssden from package gss.

Dictionary

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

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

Meta Information

  • Task type: “dens”

  • Predict Types: “pdf”, “cdf”

  • Feature Types: “integer”, “numeric”

  • Required Packages: mlr3extralearners, gss

Parameters

IdTypeDefaultLevelsRange
typelist-\((-\infty, \infty)\)
alphanumeric1.4\((-\infty, \infty)\)
weightslist-\((-\infty, \infty)\)
na.actionlistfunction (object, ...) , UseMethod("na.omit")\((-\infty, \infty)\)
id.basislist-\((-\infty, \infty)\)
nbasisinteger-\((-\infty, \infty)\)
seednumeric-\((-\infty, \infty)\)
domainlist-\((-\infty, \infty)\)
quadlist-\((-\infty, \infty)\)
qdsz.depthnumeric-\((-\infty, \infty)\)
biaslist-\((-\infty, \infty)\)
precnumeric1e-07\((-\infty, \infty)\)
maxiterinteger30\([1, \infty)\)
skip.iterlogical-TRUE, FALSE\((-\infty, \infty)\)

References

Gu, C. and Wang, J. (2003), Penalized likelihood density estimation: Direct cross-validation and scalable approximation. Statistica Sinica, 13, 811–826.

See also

Author

RaphaelS1

Super classes

mlr3::Learner -> mlr3proba::LearnerDens -> LearnerDensSpline

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

LearnerDensSpline$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

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

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerDensSpline:dens.spline>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, gss
#> * Predict Type: pdf
#> * Feature types: integer, numeric
#> * Properties: missings
#>  [1] "type"       "alpha"      "weights"    "na.action"  "id.basis"  
#>  [6] "nbasis"     "seed"       "domain"     "quad"       "qdsz.depth"
#> [11] "bias"       "prec"       "maxiter"    "skip.iter"