cr.des: Assuming a linear model for the response, allocate treatment...

In mst1g15/biasedcoin:

Description

Assuming a linear model for the response, allocate treatment sequentially based on an optimality criterion for linear combinations of parameters. Responses are simulated assuming the true parameter values.

Usage

cr.des(covar, true.beta, true.sigma, threshold, kappa, init, cr.lossfunc, k, wt,
  int = T, prior.scale = 100, same.start = NULL, rand.start = NULL,
  stoc = T, bayes = T, prior.default = T, u = NULL, ...)

Arguments

covar	a dataframe for the covariates
true.beta	the true parameter values of the regression coefficients
true.sigma	the true parameter value for the standard deviation
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
cr.lossfunc	loss function appropriate for linear combinations of parameters
k	the number of "outer loops" in the coordinate exchange algorithm
wt	set to T if the above lossfunction is weighted, NULL otherwise
int	set to T if you allow for treatment-covariate interactions in the model, NULL otherwise
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.
prior.default	set to T if default priors for bayesglm is used. If set to False and bayes=T, normal priors used.
u	vector of uniform random numbers for generating responses. If set to NULL, responses generated from the binomial distribution.
...	further arguments to be passed to <lossfunc>

Value

design matrix D, responses y, all estimates of betas, final estimate of beta, all weights, all estimates of standard deviation, beta, probabilities for treatment assignment, all values of optimalities (weighted L, DA, weighted DA), proportion of treatment=1, proportion of covariate in each group Type 1 error, true value of power, empirical value of power.

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