logit.Lbnon: Allocate treatments according to a weighted L-optimal...

In mst1g15/biasedcoin:

Description

Allocate treatments according to a weighted L-optimal criterion allowing for a non-myopic approach. We assume a logistic model for the response and simulate responses sequentially.

Usage

logit.Lbnon(covar, true.beta, threshold, kappa, init, z.probs, N,
  prior.scale = 100, same.start = NULL, rand.start = NULL, stoc = T,
  bayes = T, u = NULL, true.bvcov = NULL, ...)

Arguments

covar	a dataframe for the covariates
true.beta	the true parameter values of the regression coefficients
threshold	the cut-off value for hypothesis tests
kappa	the value of probability at which weights are set at zero
init	the number of units in the initial design
z.probs	vector of probabilities for each level of covariate z
N	natural number greater than 0 for horizon
prior.scale	the prior scale parameter
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.
...	further arguments to be passed to <lossfunc>

Value

Design matrix D, all estimates of beta, final estimate of beta, responses y

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