infer_from_coded_columns

Infer categorical structure from dummy/effects coded columns

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

infer_from_coded_columns function

<dl><dt>data</dt>
<dd>A data.frame</dd>
<dt>attr_cols</dt>
<dd>Column names to analyze</dd>
<dt>encoding</dt>
<dd>Encoding type</dd></dl>

Arguments

Infer categorical structure from dummy/effects coded columns — infer_from_coded_columns

<dl>

<dt>data</dt>
<dd>A data.frame</dd>


<dt>attr_cols</dt>
<dd>Column names to analyze</dd>


<dt>encoding</dt>
<dd>Encoding type</dd>

</dl>

infer_from_coded_columns: Infer categorical structure from dummy/effects coded columns

Description

Usage

Value

Arguments