mlr_learners_regr.lmer: Regression Linear Mixed Effects Learner

mlr_learners_regr.lmerR Documentation

Regression Linear Mixed Effects Learner

Description

Linear model with random effects. Calls lme4::lmer() from lme4.

Formula

Although most mlr3 learners don't allow to specify the formula manually, and automatically set it by valling task$formula(), this learner allows to set the formula because it's core functionality depends it. This means that it might not always use all features that are available in the task. Be aware, that this can sometimes lead to unexpected error messages, because mlr3 checks the compatibility between the learner and the task on all available features.

Dictionary

This Learner can be instantiated via lrn():

lrn("regr.lmer")

Meta Information

  • Task type: “regr”

  • Predict Types: “response”

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

  • Required Packages: mlr3, lme4

Parameters

Id Type Default Levels Range
formula untyped - -
REML logical TRUE TRUE, FALSE -
start untyped NULL -
verbose integer 0 [0, \infty)
contrasts untyped NULL -
optimizer character nloptwrap Nelder_Mead, bobyqa, nlminbwrap, nloptwrap -
restart_edge logical FALSE TRUE, FALSE -
boundary.tol numeric 1e-05 [0, \infty)
calc.derivs logical TRUE TRUE, FALSE -
check.nobs.vs.rankZ character ignore ignore, warning, message, stop -
check.nobs.vs.nlev character stop ignore, warning, message, stop -
check.nlev.gtreq.5 character ignore ignore, warning, message, stop -
check.nlev.gtr.1 character stop ignore, warning, message, stop -
check.nobs.vs.nRE character stop ignore, warning, message, stop -
check.rankX character message+drop.cols message+drop.cols, silent.drop.cols, warn+drop.cols, stop.deficient, ignore -
check.scaleX character warning warning, stop, silent.rescale, message+rescale, warn+rescale, ignore -
check.formula.LHS character stop ignore, warning, message, stop -
check.conv.grad untyped "lme4::.makeCC(\"warning\", tol = 2e-3, relTol = NULL)" -
check.conv.singular untyped "lme4::.makeCC(action = \"message\", tol = formals(lme4::isSingular)$tol)" -
check.conv.hess untyped "lme4::.makeCC(action = \"warning\", tol = 1e-6)" -
optCtrl untyped list() -
newparams untyped NULL -
re.form untyped NULL -
random.only logical FALSE TRUE, FALSE -
allow.new.levels logical FALSE TRUE, FALSE -
na.action untyped "stats::na.pass" -

Offset

If a Task contains a column with the offset role, it is automatically incorporated during training via the offset argument in lme4::lmer(). No offset is applied during prediction for this learner.

Super classes

mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrLmer

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage
LearnerRegrLmer$new()

Method clone()

The objects of this class are cloneable with this method.

Usage
LearnerRegrLmer$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Author(s)

s-kganz

References

Bates, M D (2010). “lme4: Mixed-effects modeling with R.”

See Also

Examples


# Define the Learner and set parameter values
learner = lrn("regr.lmer", formula = cmedv ~ (1 | town))

# Define a Task
task = tsk("boston_housing")

learner$train(task)
print(learner$model)


mlr-org/mlr3extralearners documentation built on June 11, 2025, 7:06 p.m.