plot.cjbart.vimp

Plots a heatmap of variable importance, across predicted IMCEs. By default, all attribute-levels and covariates in the model are plotted.

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

plot.cjbart.vimp function

<dl><dt>x</dt>
<dd>Object of class <code>cjbart.vimp</code>, the result of running <code>het_vimp()</code></dd>
<dt>covars</dt>
<dd>Optional vector of covariate names to plot. By default, all included covariates are shown.</dd>
<dt>att_levels</dt>
<dd>Optional vector of attribute-levels to plot. By default, all attribute-levels are shown.</dd>
<dt>...</dt>
<dd>Additional arguments (not currently used)</dd></dl>

Arguments

Plot Variable Importance Matrix for Heterogeneity Analysis — plot.cjbart.vimp

<dl>

<dt>x</dt>
<dd>Object of class <code>cjbart.vimp</code>, the result of running <code>het_vimp()</code></dd>


<dt>covars</dt>
<dd>Optional vector of covariate names to plot. By default, all included covariates are shown.</dd>


<dt>att_levels</dt>
<dd>Optional vector of attribute-levels to plot. By default, all attribute-levels are shown.</dd>


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

</dl>

plot.cjbart.vimp: Plot Variable Importance Matrix for Heterogeneity Analysis

Description

Usage

Value

Arguments