rf_vimp

Estimates random forest variable importance scores for a single attribute-level of a conjoint experiment. This function is for advanced use. Users should typically use the <code>het_vimp()</code> function.

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

rf_vimp function

<dl><dt>model</dt>
<dd>Object of class <code>cjbart</code>, the result of running <code>IMCE()</code></dd>
<dt>outcome</dt>
<dd>Character string detailing the covariate over which to analyze heterogeneous effects</dd>
<dt>covars</dt>
<dd>An optional vector of covariates to include in the importance metric check. When <code>covars = NULL</code> (the default), all covariates are included in the importance model.</dd></dl>

Arguments

Estimate a Single Variable Importance Metric for cjbart Object — rf_vimp

<dl>

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


<dt>outcome</dt>
<dd>Character string detailing the covariate over which to analyze heterogeneous effects</dd>


<dt>covars</dt>
<dd>An optional vector of covariates to include in the importance metric check. When <code>covars = NULL</code> (the default), all covariates are included in the importance model.</dd>

</dl>

rf_vimp: Estimate a Single Variable Importance Metric for `cjbart` Object

Description

Usage

Value

Arguments