mestimate {quantoptr}

The Mean-Optimal Treatment Regime Wrapper Function

Description

The wrapper function for mean-optimal treatment regime that calls a genetic algorithm. This function supports the IPWE_Mopt function.

Usage

mestimate(x, y, a, prob, p_level, nvars, hard_limit = FALSE, max = TRUE,
  cl.setup = 1, s.tol = 1e-04, it.num = 8, pop.size = 3000)

Arguments

x

a matrix of observed covariates from the sample. Notice that we assumed the class of treatment regimes is linear.

y

a vector, the observed responses from a sample

a

a vector of 0s and 1s, the observed treatments from a sample

prob

a vector, the propensity scores of getting treatment 1 in the samples

p_level

choose between 0,1,2,3 to indicate different levels of output from the genetic function. Specifically, 0 (minimal printing), 1 (normal), 2 (detailed), and 3 (debug.)

nvars

an integer. The number of parameters indexing a treatment regime.

hard_limit

logical. This logical variable determines if the max.generations variable is a binding constraint for genoud.

max

logical. If max=TRUE, it indicates we wish to maximize the marginal mean; If max=FALSE, we wish to minimize the marginal mean. The default is TRUE.

cl.setup

the number of nodes. >1 indicates choosing parallel computing option in rgenoud::genoud. Default is 1.

s.tol

This is the tolerance level used by genoud. Default is 10^{-5} times the difference between the largest and the smallest value in the observed responses. This is particularly important when it comes to evaluating it.num.

it.num

integer > 1. This argument will be used in rgeound::geound function. If there is no improvement in the objective function in this number of generations, rgenoud::genoud will think that it has found the optimum.

pop.size

an integer with the default set to be 3000. This is the population number for the first generation in the genetic algorithm (rgenoud::genoud).

References

Zhang B, Tsiatis AA, Laber EB and Davidian M (2012). “A robust method for estimating optimal treatment regimes.” Biometrics, 68(4), pp. 1010–1018.

See Also

The function IPWE_Mopt is based on this function.