Regression Response Surface Model Learner
mlr_learners_regr.rsm.Rd
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 forrsm::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()
:
$get("regr.rsm")
mlr_learnerslrn("regr.rsm")
References
Lenth, V R (2010). “Response-surface methods in R, using rsm.” Journal of Statistical Software, 32, 1--17.
See also
as.data.table(mlr_learners)
for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner
-> mlr3::LearnerRegr
-> LearnerRegrRSM
Examples
learner = mlr3::lrn("regr.rsm")
print(learner)
#> <LearnerRegrRSM:regr.rsm>: Response Surface Model
#> * Model: -
#> * Parameters: modelfun=FO
#> * Packages: mlr3, rsm
#> * Predict Types: [response]
#> * Feature Types: integer, numeric, factor, ordered
#> * Properties: -
# available parameters:
learner$param_set$ids()
#> [1] "modelfun"