Generalized Partially Linear Tree-Based Regression Model
prediction
tree to GLM
From a tree to indicators (or dummy variables)
score of importance for variables
AUC on the Out Of Bag samples
bagging pltr models
Prunning the Maximal tree
parametric bootstrap on a pltr model
Prunning the Maximal tree
permutation test on a pltr model
Fit a generalized partially linear tree-based regression model
compute the nested trees
Compute the p-value
Partially tree-based regression model function
prediction on new features
Combining a generalized linear model with an additional tree part on the same scale. A four-step procedure is proposed to fit the model and test the joint effect of the selected tree part while adjusting on confounding factors. We also proposed an ensemble procedure based on the bagging to improve prediction accuracy and computed several scores of importance for variable selection. See 'Cyprien Mbogning et al.'(2014)<doi:10.1186/2043-9113-4-6> and 'Cyprien Mbogning et al.'(2015)<doi:10.1159/000380850> for an overview of all the methods implemented in this package.