lasso_classifier: Lasso classifier

In autoMrP: Improving MrP with Ensemble Learning

Lasso classifier

Description

lasso_classifier applies lasso classification to a data set.

Usage

lasso_classifier(
  L2.fix,
  L1.re,
  data.train,
  lambda,
  model.family,
  y,
  verbose = c(TRUE, FALSE)
)

Arguments

L2.fix	Fixed effects. A two-sided linear formula describing the fixed effects part of the model, with the outcome on the LHS and the fixed effects separated by + operators on the RHS.
L1.re	Random effects. A named list object, with the random effects providing the names of the list elements and ~ 1 being the list elements.
data.train	Training data. A data.frame containing the training data used to train the model.
lambda	Tuning parameter. Lambda is the penalty parameter that controls the shrinkage of fixed effects.
model.family	Model family. A variable indicating the model family to be used by glmmLasso. Defaults to binomial(link = "probit").
y	Outcome variable. A character vector containing the column names of the outcome variable. A character scalar containing the column name of the outcome variable in survey.
verbose	Verbose output. A logical vector indicating whether or not verbose output should be printed.

Value

A multilevel lasso model. An glmmLasso object.

autoMrP documentation built on May 29, 2024, 6:40 a.m.