cbc_decode

This function is depreciated. Use <code>cbc_encode()</code> instead

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

cbc_decode function

<dl><dt>data</dt>
<dd>A <code>cbc_design</code> or <code>cbc_choices</code> object with dummy-coded categorical variables</dd></dl>

Arguments

Convert dummy-coded CBC data back to categorical format — cbc_decode

<dl>

<dt>data</dt>
<dd>A <code>cbc_design</code> or <code>cbc_choices</code> object with dummy-coded categorical variables</dd>

</dl>

cbc_decode: Convert dummy-coded CBC data back to categorical format

Description

Usage

Value

Arguments