simfuture.logis.cont: Allocate continuous treatments according to an information...

In mst1g15/biasedcoin:

Description

Allocate continuous treatments according to an information matrix based optimality criterion allowing for a pseudo-nonmyopic approach. We assume a logistic model for the response.

Usage

simfuture.logis.cont(covar, true.beta, init, k, sim, z.probs, int = NULL,
  lossfunc, same.start = NULL, rand.start = NULL, bayes = T, u = NULL,
  true.bvcov = NULL, ...)

Arguments

covar	a dataframe for the covariates
true.beta	the true parameter values of the data generating mechanism
init	the number of units in the initial design
k	number of "outer loops" in the coordinate exchange algorithm
sim	number of trajectories to simulate
z.probs	vector of probabilities for each level of covariate z
int	set to T if you allow for treatment-covariate interactions in the model, NULL otherwise
lossfunc	the objective function to minimize
same.start	the design matrix to be used for the initial design. If set to NULL, function generates initial design.
rand.start	If set to T, function generates an initial design randomly. Else, coordinate exchange is used.
bayes	set to T if bayesglm is used instead of glm. Default prior assumed.
u	vector of uniform random numbers for generating responses. If set to NULL, responses generated from the binomial distribution.
true.bvcov	if set to T, use the true parameter values to compute obejctive function. If set to NULL, use estimated parameter values.
...	further arguments to be passed to <lossfunc>

Value

Design matrix D, value of the objective function

mst1g15/biasedcoin documentation built on Nov. 26, 2019, 4:01 a.m.