psiITR_VS: psi-Learning in individulized treatment rule in the linear...
In mylzwq/psi-learning-for-ITR: An Implementation of psiLearn on Individualized Treatment Rule

Description Usage Arguments Value Author(s) See Also Examples

Given the tunning parameters return the psiLearning model to estimate the optimal ITR with variable selection

1	psiITR_VS(X,A,R,w0=NULL,tau=0.1,kappa=0.2,lambda=0.8,maxit=100,tol=1e-4,tau2=0.2,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.
`w0`	Inital estimate for the coefficients from `psi_Init` or can be provided by the user.
`tau`	tuning parameter for the loss function in psi-Learn
`kappa`	tunning parameter to control the complexity of the decision function in the ridge penaly
`lambda`	tunning parameter to control the complexity of the decision function in the TLP penalty
`maxit`	maximum iterations allowed
`tol`	tolerance error bound
`tau2`	tunning parameter to control the margin used in TLP penalty
`res`	Whether to estimate the residual as the outcome for interaction effect, default is FALSE

It returns the estimated coefficients in the decision funcion and the fitted value

`w`	the coefficent for the decision function, if in the linear case it is p-dimension and if in the rbf kernel case, it is n-dimension.
`bias`	the intercept in both the linear case and the kernel case.
`fit`	a vector of estimated values for \hat{f(x)} in training data, in the linear case it is fit=bias+Xw* and in the kernel case fit=bias+K(X,X)w.

MingyangLiu <[email protected]>

psi_Init

          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)
          w0.Linear=psi_Init(X,A,R,kernel='linear')
          psi_Linear<-psiITR_VS(X,A,R,w0.Linear,tau=0.1,kappa=0.3,lambda=1,maxit=100,tol=1e-4,tau2=0.2)