psiITR_VS: psi-Learning in individulized treatment rule in the linear...

Description Usage Arguments Value Author(s) See Also Examples

View source: R/psi_lin_VS.R

Description

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

Usage

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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)

Arguments

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

Value

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+X*w and in the kernel case fit=bias+K(X,X)w.

Author(s)

MingyangLiu <[email protected]>

See Also

psi_Init

Examples

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          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)

mylzwq/psi-learning-for-ITR documentation built on Sept. 23, 2018, 12:56 a.m.