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GPLTR (version 1.5)

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. See 'Cyprien Mbogning et al.'(2014) and 'Cyprien Mbogning et al.'(2015) for an overview of all the methods implemented in this package.

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Version

Install

install.packages('GPLTR')

Monthly Downloads

218

Version

1.5

License

GPL (>= 2.0)

Maintainer

Cyprien Mbogning

Last Published

March 28th, 2024

Functions in GPLTR (1.5)

predict_pltr

prediction
nested.trees

compute the nested trees
tree2glm

tree to GLM
tree2indicators

From a tree to indicators (or dummy variables)
predict_bagg.pltr

prediction on new features
pltr.glm

Partially tree-based regression model function
p.val.tree

Compute the p-value
best.tree.permute

permutation test on a pltr model
burn

burn dataset
GPLTR-package

Fit a generalized partially linear tree-based regression model
best.tree.bootstrap

parametric bootstrap on a pltr model
best.tree.BIC.AIC

Prunning the Maximal tree
VIMPBAG

score of importance for variables
bag.aucoob

AUC on the Out Of Bag samples
data_pltr

gpltr data example
bagging.pltr

bagging pltr models
best.tree.CV

Prunning the Maximal tree