plot.predict.fcrr

Plots predicted cumulative incidence function

In competing risks regression, the proportional subdistribution hazards (PSH) model is popular for its direct assessment of covariate effects on the cumulative incidence function. This package allows for both penalized and unpenalized PSH regression in linear time using a novel forward-backward scan. Penalties include Ridge, Lease Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Plus (MCP), and elastic net <doi: 10.32614/RJ-2021-010>.

Eric Kawaguchi

fastcmprsk

Fine-Gray Regression via Forward-Backward Scan

plot.predict.fcrr function

<dl><dt>x</dt>
<dd><code>predict.fcrr</code> object (output from <code>predict(fcrr x)</code>)</dd>
<dt>...</dt>
<dd>additional arguments to <code><a href="/link/plot()?package=fastcmprsk&version=1.26.1" data-mini-rdoc="fastcmprsk::plot()">plot()</a></code></dd></dl>

Arguments

Plots predicted cumulative incidence function — plot.predict.fcrr

<dl>

<dt>x</dt>
<dd><code>predict.fcrr</code> object (output from <code>predict(fcrr x)</code>)</dd>


<dt>...</dt>
<dd>additional arguments to <code><a href='https://rdrr.io/r/graphics/plot.default.html'>plot()</a></code></dd>

</dl>

plot.predict.fcrr: Plots predicted cumulative incidence function

Description

Usage

Value

Arguments