Description Usage Arguments Value
Assuming a logistic model for the response, allocate treatment sequentially based on an information matrix-based optimality criterion. Responses are simulated assuming the true parameter values.
1 2 3 |
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 |
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. |
... |
further arguments to be passed to <lossfunc> |
design matrix D, responses y, all estimates of betas, final beta, probabilities for treatment assignment, proportion of Y=1, proportion tmt=1, values of objective function
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.