Constructs an ensemble of logic regression models using bagging for classification and identification of important predictors and predictor interactions
logforest(resp, Xs, nBSXVars, anneal.params, nBS=100, h=0.5, norm=TRUE, numout=5, nleaves)An object of class "logforest" including a list of values
numeric vector of binary response values
matrix or dataframe of zeros and ones for all predictor variables
integer for the number of predictors used to construct each logic regression model. The default value is all predictors in the data.
a list containing the parameters for simulated annealing. See the help file for the function logreg.anneal.control in the LogicReg package. If missing, default annealing parameters are set at start=1, end=-2, and iter=50000.
number of logic regression trees to be fit in the logic forest model.
a number between 0 and 1 for the minimum proportion of trees in the logic forest that must predict a 1 for the prediction to be one.
logical. If FALSE, predictor and interaction scores in model output are not normalized to range between zero and one.
number of predictors and interactions to be included in model output
the maximum number of end nodes generated for each tree