cor_spec

Create a correlation specification for random parameters

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

cor_spec function

<dl><dt>with</dt>
<dd>Character. Name of attribute to correlate with</dd>
<dt>value</dt>
<dd>Numeric. Correlation value between -1 and 1</dd>
<dt>level</dt>
<dd>Character. For categorical variables, specific level to correlate from</dd>
<dt>with_level</dt>
<dd>Character. For categorical variables, specific level to correlate with</dd></dl>

Arguments

Create a correlation specification for random parameters — cor_spec

<dl>

<dt>with</dt>
<dd>Character. Name of attribute to correlate with</dd>


<dt>value</dt>
<dd>Numeric. Correlation value between -1 and 1</dd>


<dt>level</dt>
<dd>Character. For categorical variables, specific level to correlate from</dd>


<dt>with_level</dt>
<dd>Character. For categorical variables, specific level to correlate with</dd>

</dl>

cor_spec: Create a correlation specification for random parameters

Description

Usage

Value

Arguments