plot.atefit

A doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.

Thomas Debray

precmed

Precision Medicine

Lu Tian

Xiaotong Jiang

Gabrielle Simoneau

Biogen  MA  Inc. 

Stan Wijn

Joana Caldas

plot.atefit function

<dl><dt>x</dt>
<dd>An object of class <code>"atefit"</code>.</dd>
<dt>bins</dt>
<dd>Number of bins</dd>
<dt>alpha</dt>
<dd>Opacity</dd>
<dt>title</dt>
<dd>The text for the title</dd>
<dt>theme</dt>
<dd>Defaults to <code>theme_classic()</code>. Other options include <code>theme_grey()</code>, <code>theme_bw()</code>, <code>theme_light()</code>, <code>theme_dark()</code>, and <code>theme_void()</code></dd>
<dt>...</dt>
<dd>Other parameters</dd></dl>

Arguments

Author

Histogram of bootstrap estimates — plot.atefit

<dl>

<dt>x</dt>
<dd>An object of class <code>"atefit"</code>.</dd>


<dt>bins</dt>
<dd>Number of bins</dd>


<dt>alpha</dt>
<dd>Opacity</dd>


<dt>title</dt>
<dd>The text for the title</dd>


<dt>theme</dt>
<dd>Defaults to <code>theme_classic()</code>. Other options include <code>theme_grey()</code>, <code>theme_bw()</code>, <code>theme_light()</code>, <code>theme_dark()</code>, and <code>theme_void()</code></dd>


<dt>...</dt>
<dd>Other parameters</dd>

</dl>

Histogram of bootstrap estimates

plot.atefit: Histogram of bootstrap estimates

Description

Usage

Value

Arguments

Author

Details