Generates a random design by sampling from the candidate set each update of the algorithm.
random(
design_object,
model,
efficiency_criteria,
utility,
prior_values,
dudx,
candidate_set,
rows,
control
)A list of class 'spdesign' created within the
generate_design function
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 efficiency criteria to optimize for. One of 'a-error', 'c-error', 'd-error' or 's-error'. No default is set and argument must be specified. Optimizing for multiple criteria is not yet implemented and will result in an error.
A named list of utility functions. See the examples and the vignette for examples of how to define these correctly for different types of experimental designs.
A list of priors
A character string giving the name of the prior in the denominator. Must be specified when optimizing for 'c-error'
A matrix or data frame in the "wide" format containing all permitted combinations of attributes. The default is NULL. If no candidate set is provided, then the full factorial subject to specified exclusions will be used. This is passed in as an object and not a character string. The candidate set will be expanded to include zero columns to consider alternative specific attributes.
An integer giving the number of rows in the final design
A list of control options
A list of class 'spdesign'
With no restrictions placed, this type of design will only consider efficiency. There is no guarantee that you will achieve attribute level balance, nor that all attribute levels will be present. More efficient designs tend to have more extreme trade-offs.