Man pages for logicDT
Identifying Interactions Between Binary Predictors
| 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 |
| fit4plModel | Fitting 4pL models |
| fitLinearBoostingModel | Linear models based on boosted models |
| fitLinearLogicModel | Linear models based on logic terms |
| fitLinearModel | Fitting linear models |
| getDesignMatrix | Design matrix for the set of conjunctions |
| get.ideal.penalty | Tuning the LASSO regularization parameter |
| gxe.test | Gene-environment interaction test |
| gxe.test.boosting | Gene-environment (GxE) interaction test based on boosted... |
| importance.test.boosting | Term importance test based on boosted linear models |
| logicDT | Fitting logic decision trees |
| logicDT.bagging | Fitting bagged logicDT models |
| logicDT.boosting | Fitting boosted logicDT models |
| 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.linear | Prediction for linear models |
| predict.linear.logic | Prediction for 'linear.logic' 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) |
