GPLTR-package |
Fit a generalized partially linear tree-based regression model |
bag.aucoob |
AUC on the Out Of Bag samples |
bagging.pltr |
bagging pltr models |
best.tree.BIC.AIC |
Prunning the Maximal tree |
best.tree.bootstrap |
parametric bootstrap on a pltr model |
best.tree.CV |
Prunning the Maximal tree |
best.tree.permute |
permutation test on a pltr model |
burn |
burn dataset |
data_pltr |
gpltr data example |
GPLTR |
Fit a generalized partially linear tree-based regression model |
nested.trees |
compute the nested trees |
p.val.tree |
Compute the p-value |
pltr.glm |
Partially tree-based regression model function |
predict_bagg.pltr |
prediction on new features |
predict_pltr |
prediction |
tree2glm |
tree to GLM |
tree2indicators |
From a tree to indicators (or dummy variables) |
VIMPBAG |
score of importance for variables |