htetree.anova

Intermediate function for <code>causalTree</code>

Estimating heterogeneous treatment effects with tree-based machine
learning algorithms and visualizing estimated results in flexible and
presentation-ready ways. For more information, see Brand, Xu, Koch,
and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package
first started as a fork of the 'causalTree' package on 'GitHub' and we
greatly appreciate the authors for their extremely useful and free package.

Jiahui Xu

htetree

Causal Inference with Tree-Based Machine Learning Algorithms

Tanvi Shinkre

Jennie Brand

htetree.anova function

<dl><dt>y</dt>
<dd>outcome variable</dd>
<dt>offset</dt>
<dd>this can be used to specify an a priori known
component
to be included in the linear predictor during fitting. This should be
<code>NULL</code> or a numeric vector of length equal to the number of cases.
One or more <a href="/link/offset?package=htetree&version=0.1.20" data-mini-rdoc="htetree::offset">offset</a> terms can be included in the formula instead or as
well, and
if more than one is specified their sum is used. See <a href="/link/model.offset?package=htetree&version=0.1.20" data-mini-rdoc="htetree::model.offset">model.offset</a>.</dd>
<dt>wt</dt>
<dd>optional weights</dd></dl>

Arguments

Intermediate function for causalTree — htetree.anova

<dl>

<dt>y</dt>
<dd>outcome variable</dd>


<dt>offset</dt>
<dd>this can be used to specify an a priori known
component
to be included in the linear predictor during fitting. This should be
<code>NULL</code> or a numeric vector of length equal to the number of cases.
One or more <a href='https://rdrr.io/r/stats/offset.html'>offset</a> terms can be included in the formula instead or as
well, and
if more than one is specified their sum is used. See <a href='https://rdrr.io/r/stats/model.extract.html'>model.offset</a>.</dd>


<dt>wt</dt>
<dd>optional weights</dd>

</dl>

htetree.anova: Intermediate function for `causalTree`

Description

Usage

Value

Arguments