cv.psiITR: Cross validation for psiLearn learning
In mylzwq/psi-learning-for-ITR: An Implementation of psiLearn on Individualized Treatment Rule

Description Usage Arguments Value Author(s) Examples

Return the psi-learning models with best tuning parameters.

1	cv.psiITR(X,A,R,m=5,kernel='linear',sigma=NULL,kappa.ratio=0.01,kappa.max=1.5,nkappa=10,tau=0.1,maxit=100,tol=1e-5,res=FALSE)

`X`	n by p input matrix.
`A`	a vector of n entries coded 1 and -1 for the treatment assignments.
`R`	a vector of outcome variable, larger is more desirable.
`m`	m-folds cross validation
`kernel`	kernel function used in the decision function
`sigma`	bindwidth for 'rbf' kernel it can be provided by the user, if not, it can be estimated from `Sig_est`
`kappa.ratio`	the ratio between the max kappa and the min kappa which controls the complexity of the decision function.
`kappa.max`	max kappa in the tunning parameter seq
`nkappa`	num of kappa for grid search
`tau`	tuning parameter for the loss function in psi-Learn
`maxit`	number of max iteration used in `psiITR`
`tol`	tolerance used in `psiITR`
`res`	Whether to estimate the residual as the outcome for interaction effect, default is FALSE

It returns the estimated coefficients in the decision funcion after cross validation

`w`	the coefficent for the decision function.
`bias`	the intercept in both the linear case and the kernel case.
`sigma`	if the kernel is rbf then the optimal sigma is returned

MingyangLiu <[email protected]>

          n=100;p=5
          X=replicate(p,runif(n, min = -1, max = 1))
          A=2*rbinom(n, 1, 0.5)-1
          T=cbind(rep(1,n,1),X)%*%c(1,2,1,0.5,rep(0,1,p-3))
          T0=(cbind(rep(1,n,1),X)%*%c(0.54,-1.8,-1.8,rep(0,1,p-2)))*A
          R=as.vector(rnorm(n,mean=0,sd=1)+T+T0)
          cv_psi_Linear<-cv.psiITR(X,A,R,m=5,kernel='linear',kappa.ratio=0.01,kappa.max=1.5,nkappa=10,tau=0.1,maxit=100, tol=1e-5)
          cv_psi_Rbf<-cv.psiITR(X,A,R,m=5,kernel='rbf',kappa.ratio=0.01,kappa.max=1,nkappa=10,tau=0.1,maxit=100, tol=1e-5)