fitLinearBoostingModel: Linear models based on boosted models

In logicDT: Identifying Interactions Between Binary Predictors

Linear models based on boosted models

Description

This function uses a fitted logic.boosted model for fitting a linear or logistic (depending on the type of outcome) regression model.

Usage

fitLinearBoostingModel(model, n.iter, type = "standard", s = NULL, ...)

Arguments

model	Fitted logic.boosted model
n.iter	Number of boosting iterations to be used
type	Type of linear model to be fitted. Either "standard" (without regularization), "lasso" (LASSO) or "cv.lasso" (LASSO with cross-validation for automatically configuring the complexity penalty).
s	Regularization parameter. Only used if type = "lasso" is set.
...	Additional parameters passed to glmnet or cv.glmnet if the corresponding model type was chosen.

Details

In this procedure, the logic terms are extracted from the individual logicDT models and the set of unique terms are used as predictors in a regression model. For incorporating a continuous covariable the covariable itself as well as products of the covariable with the extracted logic terms are included as predictors in the regression model.

For more details on the possible types of linear models, see fitLinearLogicModel.

Value

A linear.logic model. This is a list containing the logic terms used as predictors in the model and the fitted glm model.

logicDT documentation built on Jan. 14, 2023, 5:06 p.m.