Regression Response Surface Model Learner

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

Custom mlr3 parameters

• 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 lrn():

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, 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$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$format()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$print()
• mlr3::Learner$reset()
• mlr3::Learner$train()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerRegrRSM$new()

---

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

# Define the Learner
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: -

# Define a Task
task = mlr3::tsk("mtcars")

# Create train and test set
ids = mlr3::partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

print(learner$model)
#> 
#> Call:
#> rsm(formula = mpg ~ FO(am, carb, cyl, disp, drat, gear, hp, qsec, 
#>     vs, wt), data = data)
#> 
#> Coefficients:
#>                                               (Intercept)  
#>                                                  -5.36532  
#>   FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)am  
#>                                                  -0.19950  
#> FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)carb  
#>                                                  -1.04050  
#>  FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)cyl  
#>                                                   0.93560  
#> FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)disp  
#>                                                   0.01830  
#> FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)drat  
#>                                                   2.11353  
#> FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)gear  
#>                                                   3.45674  
#>   FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)hp  
#>                                                  -0.01372  
#> FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)qsec  
#>                                                   0.82490  
#>   FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)vs  
#>                                                   0.52874  
#>   FO(am, carb, cyl, disp, drat, gear, hp, qsec, vs, wt)wt  
#>                                                  -4.60735  
#> 


# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> regr.mse 
#> 16.23455