reconstruct_design_attributes

Reconstruct minimal design attributes from a data frame

internal

Design and evaluate choice-based conjoint survey experiments. Generate a variety of survey designs, including random designs, frequency-based designs, and D-optimal designs, as well as "labeled" designs (also known as "alternative-specific designs"), designs with "no choice" options, and designs with dominant alternatives removed. Conveniently inspect and compare designs using a variety of metrics, including design balance, overlap, and D-error, and simulate choice data for a survey design either randomly or according to a utility model defined by user-provided prior parameters. Conduct a power analysis for a given survey design by estimating the same model on different subsets of the data to simulate different sample sizes. Bayesian D-efficient designs using the 'cea' and 'modfed' methods are obtained using the 'idefix' package by Traets et al (2020) <doi:10.18637/jss.v096.i03>. Choice simulation and model estimation in power analyses are handled using the 'logitr' package by Helveston (2023) <doi:10.18637/jss.v105.i10>.

John Helveston

cbcTools

Design and Analyze Choice-Based Conjoint Experiments

reconstruct_design_attributes function

<dl><dt>data</dt>
<dd>A data.frame with valid CBC design structure</dd></dl>

Arguments

Reconstruct minimal design attributes from a data frame — reconstruct_design_attributes

<dl>

<dt>data</dt>
<dd>A data.frame with valid CBC design structure</dd>

</dl>

reconstruct_design_attributes: Reconstruct minimal design attributes from a data frame

Description

Usage

Value

Arguments