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GPLTR (version 1.2)

Generalized Partially Linear Tree-Based Regression Model

Description

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.

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Version

Install

install.packages('GPLTR')

Monthly Downloads

248

Version

1.2

License

GPL (>= 2.0)

Maintainer

Cyprien Mbogning

Last Published

June 18th, 2015

Functions in GPLTR (1.2)

bag.aucoob

AUC on the Out Of Bag samples
GPLTR-package

Fit a generalized partially linear tree-based regression model
data_pltr

gpltr data example
burn

burn dataset
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
nested.trees

compute the nested trees
best.tree.BIC.AIC

Prunning the Maximal tree
bagging.pltr

bagging pltr models
pltr.glm

Partially tree-based regression model function
predict_bagg.pltr

prediction on new features
tree2indicators

From a tree to indicators (or dummy variables)
p.val.tree

Compute the p-value
VIMPBAG

score of importance for variables
predict_pltr

prediction
tree2glm

tree to GLM