Package: GPLTR 1.5
GPLTR: Generalized Partially Linear Tree-Based Regression Model
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.
Authors:
GPLTR_1.5.tar.gz
GPLTR_1.5.zip(r-4.5)GPLTR_1.5.zip(r-4.4)GPLTR_1.5.zip(r-4.3)
GPLTR_1.5.tgz(r-4.4-any)GPLTR_1.5.tgz(r-4.3-any)
GPLTR_1.5.tar.gz(r-4.5-noble)GPLTR_1.5.tar.gz(r-4.4-noble)
GPLTR_1.5.tgz(r-4.4-emscripten)GPLTR_1.5.tgz(r-4.3-emscripten)
GPLTR.pdf |GPLTR.html✨
GPLTR/json (API)
| # Install 'GPLTR' in R: |
| install.packages('GPLTR', repos = c('https://cyprien84.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 months agofrom:2f77ea59a6. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 25 2024 |
| R-4.5-win | OK | Oct 25 2024 |
| R-4.5-linux | OK | Oct 25 2024 |
| R-4.4-win | OK | Oct 25 2024 |
| R-4.4-mac | OK | Oct 25 2024 |
| R-4.3-win | OK | Oct 25 2024 |
| R-4.3-mac | OK | Oct 25 2024 |
Exports:bag.aucoobbagging.pltrbest.tree.BIC.AICbest.tree.bootstrapbest.tree.CVbest.tree.permutenested.treesp.val.treepltr.glmpredict_bagg.pltrpredict_pltrtree2glmtree2indicatorsVIMPBAG
Dependencies:rpart
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fit a generalized partially linear tree-based regression model | GPLTR-package GPLTR |
| AUC on the Out Of Bag samples | bag.aucoob |
| bagging pltr models | bagging.pltr |
| Prunning the Maximal tree | best.tree.BIC.AIC |
| parametric bootstrap on a pltr model | best.tree.bootstrap |
| Prunning the Maximal tree | best.tree.CV |
| permutation test on a pltr model | best.tree.permute |
| burn dataset | burn |
| gpltr data example | data_pltr |
| compute the nested trees | nested.trees |
| Compute the p-value | p.val.tree |
| Partially tree-based regression model function | pltr.glm |
| prediction on new features | predict_bagg.pltr |
| prediction | predict_pltr |
| tree to GLM | tree2glm |
| From a tree to indicators (or dummy variables) | tree2indicators |
| score of importance for variables | VIMPBAG |
