dce_toolbox: Generate Efficient Optimal and Bayesian DCEs

In DCEtool: Efficient and Accessible Discrete Choice Experiments

Generate Efficient Optimal and Bayesian DCEs

Description

Generates experimental designs for DCEs. (Backend of DCEtool) .

Usage

  dce_toolbox(attributes, csets, alts, nochoice, priors, alg)

Arguments

attributes	A vector where each number represents an attribute and its values the number of levels.
csets	An integer indicating the number of sets in the DCE.
alts	An integer indicating the number of alternatives in each set.
nochoice	A boolean indicating whether there is an opt-out option (TRUE) or not (FALSE)
priors	A vector indicating the prior parameters of the conditional logit model.
alg	A string indicating the optimization algorithm: "cea" or "fedorov".

Value

design	The design matrix
DB-error	The Bayesian D-error if the optimization algorithm is "cea"
D-error	The D-error if the optimization algorithm is "fedorov"
details	A string compiling the details of the procedure

Examples

## Not run: 
  dce_toolbox(attributes = c(2,3), csets = 12,
                              alts = 2, nochoice = FALSE,
                              priors = c(0,0,0), alg = "fedorov")

## End(Not run)

DCEtool documentation built on July 9, 2023, 5:54 p.m.