The evaluation of the design candidate is independent of the optimization algorithm used.
evaluate_design_candidate( utility, design_candidate, prior_values, design_env, model, dudx, return_all, significance )
A utility function
The current design candidate
a list or vector of assumed priors
A design environment in which to evaluate the the function to derive the variance-covariance matrix.
A character string indicating the model to optimize the design for. Currently the only model programmed is the 'mnl' model and this is also set as the default.
A character string giving the name of the parameter in the denominator. Must be specified when optimizing for 'c-error'
If `TRUE` return a K or K-1 vector with parameter specific error measures. Default is `FALSE`.
A t-value corresponding to the desired level of significance. The default is significance at the 5 t-value of 1.96.
A named vector with efficiency criteria of the current design candidate. If Bayesian prior_values are used, then it returns the average error.