logit.nonmy: Allocate treatments according to an information matrix based...
In mst1g15/biasedcoin:

View source: R/logistic nonmyopic.R View source: R/logistic_nonmyopic.R

Allocate treatments according to an information matrix based optimality criterion allowing for a non-myopic approach. We assume a logistic model for the response and simulate responses sequentially.

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logit.nonmy(covar, true.beta, init, z.probs, N, int = NULL,
  lossfunc = calc.y.D, same.start = NULL, rand.start = NULL, stoc = T,
  bayes = T, u = NULL, true.bvcov = NULL, dyn = NULL, ...)

`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
`z.probs`	probabilities for each covariate value being 1
`N`	natural number greater than 0 for horizon
`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.
`stoc`	set to T if treatments are allocated using a stochastic method where the probability is determined by the optimality crtierion. Set to F if treatments are allocated deterministically.
`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.
`dyn`	set to T if there is a dynamic covariate
`...`	further arguments to be passed to <lossfunc>