This function uses a fitted logic.boosted model for fitting
a linear or logistic (depending on the type of outcome) regression model.
fitLinearBoostingModel(model, n.iter, type = "standard", s = NULL, ...)A linear.logic model. This is a list containing
the logic terms used as predictors in the model and the fitted glm
model.
Fitted logic.boosted model
Number of boosting iterations to be used
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).
Regularization parameter. Only used if type = "lasso" is
set.
Additional parameters passed to glmnet or cv.glmnet
if the corresponding model type was chosen.
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.