Regression Response Surface Model Learner

Fit a linear model with a response-surface component. Calls rsm::rsm() from FIXME: rsm.

Initial parameter values

• modelfun: This parameter controls how the formula for rsm::rsm() is created. Possible values are:

  • "FO" - first order

  • "TWI" - wo-way interactions, this is with 1st oder terms

  • "SO" - full second order

Dictionary

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

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

Meta Information

• Task type: “regr”

• Predict Types: “response”

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

• Required Packages: mlr3, rsm

Parameters

Id	Type	Default	Levels
modelfun	character	FO	FO, TWI, SO

References

Lenth, V R (2010). “Response-surface methods in R, using rsm.” Journal of Statistical Software, 32, 1--17.

See also

• Dictionary of Learners: mlr3::mlr_learners.

• as.data.table(mlr_learners) for a table of available Learners in the running session (depending on the loaded packages).

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners

• mlr3learners for a selection of recommended learners.

• mlr3cluster for unsupervised clustering learners.

• mlr3pipelines to combine learners with pre- and postprocessing steps.

• mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.

Author

sebffischer

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrRSM

Methods

Public methods

• LearnerRegrRSM$new()

• LearnerRegrRSM$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$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

LearnerRegrRSM$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerRegrRSM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

learner = mlr3::lrn("regr.rsm")
print(learner)
#> <LearnerRegrRSM:regr.rsm>: Response Surface Model
#> * Model: -
#> * Parameters: modelfun=FO
#> * Packages: mlr3, rsm
#> * Predict Types:  [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: -

# available parameters:
learner$param_set$ids()
#> [1] "modelfun"