Package: logicDT 1.0.5

logicDT: Identifying Interactions Between Binary Predictors

A statistical learning method that tries to find the best set of predictors and interactions between predictors for modeling binary or quantitative response data in a decision tree. Several search algorithms and ensembling techniques are implemented allowing for finetuning the method to the specific problem. Interactions with quantitative covariables can be properly taken into account by fitting local regression models. Moreover, a variable importance measure for assessing marginal and interaction effects is provided. Implements the procedures proposed by Lau et al. (2024, <doi:10.1007/s10994-023-06488-6>).

Authors:Michael Lau [aut, cre]

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logicDT.pdf |logicDT.html✨
logicDT/json (API)

# Install 'logicDT' in R:
install.packages('logicDT', repos = c('https://michlau.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.30 score 2 stars 2 scripts 313 downloads 30 exports 10 dependencies

Last updated 2 months agofrom:e9a2eeff79. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 23 2024
R-4.5-win-x86_64OKNov 23 2024
R-4.5-linux-x86_64OKNov 23 2024
R-4.4-win-x86_64OKNov 23 2024
R-4.4-mac-x86_64OKNov 23 2024
R-4.4-mac-aarch64OKNov 23 2024
R-4.3-win-x86_64OKNov 23 2024
R-4.3-mac-x86_64OKNov 23 2024
R-4.3-mac-aarch64OKNov 23 2024

Exports:bestBoostingItercalcAUCcalcBriercalcDevcalcMiscalcMSEcalcNCEcalcNRMSEcooling.schedulecv.prunefancy.plotfit4plModelfitLinearBoostingModelfitLinearLogicModelfitLinearModelget.ideal.penaltygetDesignMatrixgxe.testgxe.test.boostingimportance.test.boostinglogicDTlogicDT.bagginglogicDT.boostingpartial.predictpruneprune.pathrefitTreessplitSNPstree.controlvim

Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppEigenshapesurvival

Readme and manuals

Help Manual

Help pageTopics
Get the best number of boosting iterationsbestBoostingIter
Fast computation of the AUC w.r.t. to the ROCcalcAUC
Calculate the Brier scorecalcBrier
Calculate the deviancecalcDev
Calculate the misclassification ratecalcMis
Calculate the MSEcalcMSE
Calculate the normalized cross entropycalcNCE
Calculate the NRMSEcalcNRMSE
Define the cooling schedule for simulated annealingcooling.schedule
Optimal pruning via cross-validationcv.prune
Fitting 4pL modelsfit4plModel
Linear models based on boosted modelsfitLinearBoostingModel
Linear models based on logic termsfitLinearLogicModel
Fitting linear modelsfitLinearModel
Tuning the LASSO regularization parameterget.ideal.penalty
Design matrix for the set of conjunctionsgetDesignMatrix
Gene-environment interaction testgxe.test
Gene-environment (GxE) interaction test based on boosted linear modelsgxe.test.boosting
Term importance test based on boosted linear modelsimportance.test.boosting
Fitting logic decision treeslogicDT logicDT.default logicDT.formula
Fitting bagged logicDT modelslogicDT.bagging logicDT.bagging.default logicDT.bagging.formula
Fitting boosted logicDT modelslogicDT.boosting logicDT.boosting.default logicDT.boosting.formula
Partial prediction for boosted modelspartial.predict
Plot a logic decision treefancy.plot plot.logicDT
Plot calculated VIMsplot.vim
Prediction for 4pL modelspredict.4pl
Prediction for linear modelspredict.linear
Prediction for 'linear.logic' modelspredict.linear.logic
Prediction for logicDT modelspredict.genetic.logicDT predict.logic.bagged predict.logic.boosted predict.logicDT
Post-pruning using a fixed complexity penaltyprune
Pruning path of a logic decision treeprune.path
Refit the logic decision treesrefitTrees
Split biallelic SNPs into binary variablessplitSNPs
Control parameters for fitting decision treestree.control
Variable Importance Measures (VIMs)vim