# tune.JQL: Tuning function via k-fold cross vaidation for Jump... In JQL: Jump Q-Learning for Individualized Interval-Valued Dose Rule

## Description

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

## Usage

 ```1 2``` ```tune.JQL(sample,cm=6,Gamma.list=seq(from=1,to=20,by=2)/5, Lambda.list=seq(from=1,to=20,by=2)/5,folds_num=5) ```

## Arguments

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

## Value

 `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.

## References

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

## Examples

 ```1 2 3 4 5 6 7``` ```n=50 d=4 x=matrix(runif(n*(d-1),-1,1),nrow=n,ncol=d-1) a=runif(n,0,1) y=(1+x[,1])*(a>=0&a<0.35)+(x[,1]-x[,2])*(a>=0.35&a<0.65)+(1-x[,2])*(a>=0.65&a<=1)+rnorm(n,0,1) sample=data.frame(y=y,a=a,x=x) tune.JQL(sample) ```

JQL documentation built on Nov. 16, 2019, 1:07 a.m.