# TR: Individualized treatment regime based on MPL. In ITRLearn: Statistical Learning for Individualized Treatment Regime

## Description

Recommend individualized treatment regime for future patients, based on the maximin projection learning method.

## Usage

 `1` ```TR(object, x) ```

## Arguments

 `object` Fitted object of class "MPL". `x` A matrix consisting of future patients baseline covariates. If there's only one group of patients in the observed data fitted by "MPL", then future patients are assuming to coming from this group and there is no restrictions on the number of observations in `x`. Otherwise, we require the number of observations in `x` to be larger than or equal to the dimension of `x`.

## Value

A vector of individualized treatments tailored for future patients.

Chengchun Shi

`MPL`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20``` ```X <- matrix(rnorm(1600), 800, 2) A <- rbinom(800, 1, 0.5) h <- 1+sin(0.5*pi*X[,1]+0.5*pi*X[,2]) tau <- rep(0, 800) B <- matrix(0, 2, 4) B[,1] <- c(2,0) B[,2] <- 2*c(cos(15*pi/180), sin(15*pi/180)) B[,3] <- 2*c(cos(70*pi/180), sin(70*pi/180)) B[,4] <- c(0,2) for (g in 1:4){ tau[((g-1)*200+1):(g*200)] <- X[((g-1)*200+1):(g*200),]%*%B[,g] } ## mean and scale of the subgroup covariates are allowed to be different X[1:200,1] <- X[1:200,1]+1 X[201:400,2] <- 2*X[201:400,2]-1 X[601:800,] <- X[601:800,]/2 Y <- h+A*tau+0.5*rnorm(800) G <- c(rep(1,200), rep(2,200), rep(3,200), rep(4,200)) result <- MPL(Y~X|A|G) ITR <- TR(result, matrix(rnorm(200), 100, 2)) ```

ITRLearn documentation built on May 2, 2019, 11:03 a.m.