Description Usage Arguments Value References Examples

This function uses the cross-validation to train the best tuning parameters lambda_n and gamma_n for Residual Jump Q-learning.

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`sample` |
The training dataset (Y,A,X), where Y is the patientâ€™s associated response/outcome, A is the dose level received by each patient, and X is the patientâ€™s baseline covariates. |

`cm` |
The constent cm in m=n/cm, where m is the number of total subinterval that diverges with sample size n. The default value is 6. |

`Gamma.list` |
The candidate tuning paramter space for c1 in penalty term gamma=c1 log(n)/n. The default value is seq(from=1,to=20,by=2)/5. |

`Lambda.list` |
The candidate tuning paramter space for c2 in penalty term lambda=c2 log(n)/n. The default value is seq(from=1,to=20,by=2)/5. |

`RF_A.list` |
The candidate tuning paramter space for A in fitted E(Y|A=a,X) by Random Forest Regression for method 'RJQL' only. The default value is c(0,0.25,0.5,0.75,1). |

`folds_num` |
The number of the folds in the cross-validation process. The default value is 5. |

`best_gamma` |
The best tuning parameter gamma by minimuming the least square loss function. |

`best_lambda` |
The best tuning parameter lambda by minimuming the least square loss function. |

`best_a` |
The best tuning parameter a to fit random forest by minimuming the least square loss function. |

Jump Q-learning for Individualized Interval-valued Dose Rule.

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