find.I2DR: Estimating the Individualized Interval-valued Dose Rule via...

In JQL: Jump Q-Learning for Individualized Interval-Valued Dose Rule

Description

This function estimates the optimal Individualized Interval-valued Dose Rule (I2DR), and calculates a Wald-type confidence interval for the value function under the estimated optimal I2DR via Bootstrap.

Usage

  find.I2DR(Y,A,X,cm=6,method='JQL',Gamma.list=seq(from=1,to=20,by=2)/5,
  Lambda.list=seq(from=1,to=20,by=2)/5,RF_A.list=c(0,0.25,0.5,0.75,1),
  folds_num=5,alpha=0.95,nboots=500)
  

Arguments

Y	The patient’s associated response/outcome, the larger the better by convention.
A	The dose level received by each patient, should be continuous.
X	The patient’s baseline covariates, could be a matrix, including continous or discrete 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.
method	Two methods are available, Jump Q-learning ('JQL') and Residual Jump Q-learning ('RJQL'). The default method is 'JQL'.
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. If the length of Gamma.list is 1, then the tuning process will be skipped.
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. If the length of Lambda.list is 1, then the tuning process will be skipped.
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). If the length of RF_A.list is 1, then the tuning process will be skipped.
folds_num	The number of the folds in the cross-validation process. The default value is 5.
alpha	The Confidence level. The default level is 0.95.
nboots	The number of Bootstrap. The default number is 500.

Value

An object of class "I2DR" which is a list with components:

Partition	A partition of the entire dose range.
Beta	The regression coefficients for each partition.
Value	The estimated value function under our proposed I2DR.
low_bd	The lower bound of the confidence interval.
up_bd	The upper bound of the confidence interval.
method	The method used to find the I2DR.

References

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

Examples

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)
find.I2DR(Y=y,A=a,X=x)

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