R: Identifying Interactions Between Binary Predictors

# Identifying Interactions Between Binary Predictors

## Documentation for package ‘logicDT’ version 1.0.5

- DESCRIPTION file
- Package README

## Help Pages

bestBoostingIter | Get the best number of boosting iterations
calcAUC | Fast computation of the AUC w.r.t. to the ROC
calcBrier | Calculate the Brier score
calcDev | Calculate the deviance
calcMis | Calculate the misclassification rate
calcMSE | Calculate the MSE
calcNCE | Calculate the normalized cross entropy
calcNRMSE | Calculate the NRMSE
cooling.schedule | Define the cooling schedule for simulated annealing
cv.prune | Optimal pruning via cross-validation
fancy.plot | Plot a logic decision tree
fit4plModel | Fitting 4pL models
fitLinearBoostingModel | Linear models based on boosted models
fitLinearLogicModel | Linear models based on logic terms
fitLinearModel | Fitting linear models
get.ideal.penalty | Tuning the LASSO regularization parameter
getDesignMatrix | Design matrix for the set of conjunctions
gxe.test | Gene-environment interaction test
gxe.test.boosting | Gene-environment (GxE) interaction test based on boosted linear models
importance.test.boosting | Term importance test based on boosted linear models
logicDT | Fitting logic decision trees
logicDT.bagging | Fitting bagged logicDT models
logicDT.bagging.default | Fitting bagged logicDT models
logicDT.bagging.formula | Fitting bagged logicDT models
logicDT.boosting | Fitting boosted logicDT models
logicDT.boosting.default | Fitting boosted logicDT models
logicDT.boosting.formula | Fitting boosted logicDT models
logicDT.default | Fitting logic decision trees
logicDT.formula | Fitting logic decision trees
partial.predict | Partial prediction for boosted models
plot.logicDT | Plot a logic decision tree
plot.vim | Plot calculated VIMs
predict.4pl | Prediction for 4pL models
predict.genetic.logicDT | Prediction for logicDT models
predict.linear | Prediction for linear models
predict.linear.logic | Prediction for 'linear.logic' models
predict.logic.bagged | Prediction for logicDT models
predict.logic.boosted | Prediction for logicDT models
predict.logicDT | Prediction for logicDT models
prune | Post-pruning using a fixed complexity penalty
prune.path | Pruning path of a logic decision tree
refitTrees | Refit the logic decision trees
splitSNPs | Split biallelic SNPs into binary variables
tree.control | Control parameters for fitting decision trees
vim | Variable Importance Measures (VIMs)