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a parametric bootstrap procedure to select and test at the same time the selected tree
● Data Source:
CranContrib
● Keywords: documentation, test
● Alias: best.tree.bootstrap
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fit the PLTR model for a given tree. The tree is coerced into dummy covariates.
● Data Source:
CranContrib
● Keywords: documentation
● Alias: tree2glm
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prediction on new features using a pltr tree and the name of the confounding variable
● Data Source:
CranContrib
● Keywords: documentation
● Alias: predict_pltr
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Compute the AUC on the OOB samples of the bagging procedure for the binomial family. The true and false positive rates are also returned and could be helpfull for plotting the ROC curves.
● Data Source:
CranContrib
● Keywords: documentation
● Alias: bag.aucoob
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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.
● Data Source:
CranContrib
● Keywords: package
● Alias: GPLTR, GPLTR-package
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this function is set to prune back the maximal tree by using a K-fold cross-validation procedure.
● Data Source:
CranContrib
● Keywords: Machine Learning, documentation
● Alias: best.tree.CV
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Prediction on new features using a set of bagging pltr models
● Data Source:
CranContrib
● Keywords: Machine Learning, documentation
● Alias: predict_bagg.pltr
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Test weither the selected tree by either BIC, AIC or CV procedure is significantly associated to the dependent variable or not, while adjusting for a confounding effect.
● Data Source:
CranContrib
● Keywords: documentation, test
● Alias: p.val.tree
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a unified permutation test procedure to select and test at the same time the selected tree
● Data Source:
CranContrib
● Keywords: documentation, test
● Alias: best.tree.permute
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Several variable importance scores are computed: the deviance importance score (DIS), the permutation importance score (PIS), the depth deviance importance score (DDIS), the minimal depth importance score (MinDepth) and the occurence score (OCCUR).
● Data Source:
CranContrib
● Keywords: Machine Learning, documentation, variable selection
● Alias: VIMPBAG
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providing 
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