mestimate: The Mean-Optimal Treatment Regime Wrapper Function
In quantoptr: Algorithms for Quantile- And Mean-Optimal Treatment Regimes

Description Usage Arguments References See Also

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

1 2	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)

`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`).