Classification Linear Mixed Effect Learner

Generalized linear model with random effects. Calls lme4::glmer() from lme4.

Initial parameter values

• family - Is set to stats::binomial(link = "logit").

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("classif.glmer")

Meta Information

• Task type: “classif”

• Predict Types: “response”, “prob”

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

• Required Packages: mlr3, lme4

Parameters

Id	Type	Default	Levels	Range
formula	untyped	-		-
start	untyped	NULL		-
verbose	integer	0		\([0, \infty)\)
offset	untyped	NULL		-
contrasts	untyped	NULL		-
optimizer	character	-	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	-
family	untyped	"stats::binomial(link = \"logit\")"		-
nAGQ	integer	1		\([0, \infty)\)
mustart	untyped	-		-
etastart	untyped	-		-
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()		-
tolPwrss	untyped	-		-
compDev	logical	TRUE	TRUE, FALSE	-
nAGQ0initStep	logical	TRUE	TRUE, FALSE	-
check.response.not.const	untyped	"stop"		-
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"		-

References

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

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::LearnerClassif -> LearnerClassifGlmer

Methods

Public methods

• LearnerClassifGlmer$new()

• LearnerClassifGlmer$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

LearnerClassifGlmer$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClassifGlmer$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("classif.glmer",
  formula = credit_risk ~ (1 | credit_history) + job + property + telephone + savings)

# Define a Task
task = tsk("german_credit")
task$select(c("credit_history", "job", "property", "telephone", "savings"))

# Train the learner
learner$train(task)

print(learner$model)
#> $model
#> Generalized linear mixed model fit by maximum likelihood (Laplace
#>   Approximation) [glmerMod]
#>  Family: binomial  ( logit )
#> Formula: credit_risk ~ (1 | credit_history) + job + property + telephone +  
#>     savings
#>    Data: data
#>       AIC       BIC    logLik  deviance  df.resid 
#> 1143.8155 1207.6163 -558.9077 1117.8155       987 
#> Random effects:
#>  Groups         Name        Std.Dev.
#>  credit_history (Intercept) 0.687   
#> Number of obs: 1000, groups:  credit_history, 5
#> Fixed Effects:
#>                                         (Intercept)  
#>                                            -0.49718  
#>                             jobunskilled - resident  
#>                                            -0.19139  
#>                        jobskilled employee/official  
#>                                            -0.13544  
#>        jobmanager/self-empl/highly qualif. employee  
#>                                            -0.02365  
#>                                propertycar or other  
#>                                             0.56577  
#> propertybuilding soc. savings agr. / life insurance  
#>                                             0.50720  
#>                                 propertyreal estate  
#>                                             1.11104  
#>                  telephoneyes (under customer name)  
#>                                            -0.26805  
#>                                 savings... < 100 DM  
#>                                            -0.33521  
#>                          savings100 <= ... < 500 DM  
#>                                            -1.08991  
#>                         savings500 <= ... < 1000 DM  
#>                                            -1.24679  
#>                               savings... >= 1000 DM  
#>                                            -1.04639  
#> 
#> $target_levels
#> [1] "good" "bad" 
#>