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.

Fine-Gray Regression via Forward-Backward Scan

package-ubuntu-20.04-r-4.0.5#cran#fastcmprsk-1.1.1

<p>Plots predicted cumulative incidence function
</p>

## S3 method for class 'predict.fcrr'
plot(x, ...)

<table summary="R argblock">
<tr valign="top">
<td><code>x</code></td>
<td>
<p><code>predict.fcrr</code> object (output from <code>predict(fcrr x)</code>)</p>
</td>
</tr>
<tr valign="top">
<td><code>...</code></td>
<td>
<p>additional arguments to <code>plot()</code></p>
</td>
</tr>
</table>

<p>A plot of the estimated cumulative incidence function.
</p>

package-ubuntu-20.04-r-4.0.5#cran#fastcmprsk-1.1.1#docs#plot.predict.fcrr

`x`	`predict.fcrr` object (output from `predict(fcrr x)`)
`...`	additional arguments to `plot()`

plot.predict.fcrr

Description

Usage

Arguments

Value

fastcmprsk

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