summary.cjbart

<code>summary</code> method for class "cjbart"

A tool for analyzing conjoint experiments using Bayesian Additive Regression Trees ('BART'), a machine learning method developed by Chipman, George and McCulloch (2010) <doi:10.1214/09-AOAS285>. This tool focuses specifically on estimating, identifying, and visualizing the heterogeneity within marginal component effects, at the observation- and individual-level. It uses a variable importance measure ('VIMP') with delete-d jackknife variance estimation, following Ishwaran and Lu (2019) <doi:10.1002/sim.7803>, to obtain bias-corrected estimates of which variables drive heterogeneity in the predicted individual-level effects.

Thomas Robinson

cjbart

Heterogeneous Effects Analysis of Conjoint Experiments

Raymond Duch

summary.cjbart function

<dl><dt>object</dt>
<dd>Object of class <code>cjbart</code>, the result of running <code>IMCE()</code></dd>
<dt>...</dt>
<dd>Further arguments (not currently used)</dd></dl>

Arguments

Summarizing cjbart Marginal Component Effect Estimates — summary.cjbart

<dl>

<dt>object</dt>
<dd>Object of class <code>cjbart</code>, the result of running <code>IMCE()</code></dd>


<dt>...</dt>
<dd>Further arguments (not currently used)</dd>

</dl>

summary.cjbart: Summarizing `cjbart` Marginal Component Effect Estimates

Description

Usage

Value

Arguments

See Also