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Calls partykit::mob from package partykit.

Dictionary

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

mlr_learners$get("regr.mob")
lrn("regr.mob")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”, “se”

  • Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”

  • Required Packages: mlr3extralearners, partykit, sandwich, coin

Parameters

IdTypeDefaultLevelsRange
rhslist-\((-\infty, \infty)\)
fitlist-\((-\infty, \infty)\)
offsetlist-\((-\infty, \infty)\)
clusterlist-\((-\infty, \infty)\)
alphanumeric0.05\([0, 1]\)
bonferronilogicalTRUETRUE, FALSE\((-\infty, \infty)\)
minsizeinteger-\([1, \infty)\)
minsplitinteger-\([1, \infty)\)
minbucketinteger-\([1, \infty)\)
maxdepthintegerInf\([0, \infty)\)
mtryintegerInf\([0, \infty)\)
trimnumeric0.1\([0, \infty)\)
breaktieslogicalFALSETRUE, FALSE\((-\infty, \infty)\)
parmlist-\((-\infty, \infty)\)
dfsplitinteger-\([0, \infty)\)
prunelist-\((-\infty, \infty)\)
restartlogicalTRUETRUE, FALSE\((-\infty, \infty)\)
verboselogicalFALSETRUE, FALSE\((-\infty, \infty)\)
maxvarinteger-\([1, \infty)\)
caseweightslogicalTRUETRUE, FALSE\((-\infty, \infty)\)
ytypecharactervectorvector, matrix, data.frame\((-\infty, \infty)\)
xtypecharactermatrixvector, matrix, data.frame\((-\infty, \infty)\)
terminallistobject\((-\infty, \infty)\)
innerlistobject\((-\infty, \infty)\)
modellogicalTRUETRUE, FALSE\((-\infty, \infty)\)
numsplitcharacterleftleft, center\((-\infty, \infty)\)
catsplitcharacterbinarybinary, multiway\((-\infty, \infty)\)
vcovcharacteropgopg, info, sandwich\((-\infty, \infty)\)
ordinalcharacterchisqchisq, max, L2\((-\infty, \infty)\)
nrepinteger10000\([0, \infty)\)
applyfunlist-\((-\infty, \infty)\)
coresintegerNULL\((-\infty, \infty)\)
additionallist-\((-\infty, \infty)\)
predict_funlist-\((-\infty, \infty)\)

References

Hothorn T, Zeileis A (2015). “partykit: A Modular Toolkit for Recursive Partytioning in R.” Journal of Machine Learning Research, 16(118), 3905-3909. http://jmlr.org/papers/v16/hothorn15a.html

Hothorn T, Hornik K, Zeileis A (2006). “Unbiased Recursive Partitioning: A Conditional Inference Framework.” Journal of Computational and Graphical Statistics, 15(3), 651–674. doi: 10.1198/106186006x133933

Zeileis A, Hothorn T, Hornik K (2008). “Model-Based Recursive Partitioning.” Journal of Computational and Graphical Statistics, 17(2), 492–514. doi: 10.1198/106186008X319331

See also

Author

sumny

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrMob

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

LearnerRegrMob$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (requireNamespace("partykit", quietly = TRUE) && requireNamespace("sandwich", quietly = TRUE) && requireNamespace("coin", quietly = TRUE)) {
  learner = mlr3::lrn("regr.mob")
  print(learner)

  # available parameters:
  learner$param_set$ids()
}
#> <LearnerRegrMob:regr.mob>
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3extralearners, partykit, sandwich, coin
#> * Predict Type: response
#> * Feature types: logical, integer, numeric, character, factor, ordered
#> * Properties: weights
#>  [1] "rhs"         "fit"         "offset"      "cluster"     "alpha"      
#>  [6] "bonferroni"  "minsize"     "minsplit"    "minbucket"   "maxdepth"   
#> [11] "mtry"        "trim"        "breakties"   "parm"        "dfsplit"    
#> [16] "prune"       "restart"     "verbose"     "maxvar"      "caseweights"
#> [21] "ytype"       "xtype"       "terminal"    "inner"       "model"      
#> [26] "numsplit"    "catsplit"    "vcov"        "ordinal"     "nrep"       
#> [31] "applyfun"    "cores"       "additional"  "predict_fun"