plot.grpsurv.func

Plot survival curve for a model that has been fit using <code>grpsurv</code>
followed by a prediction of the survival function using
<code>predict.grpsurv</code>

Efficient algorithms for fitting the regularization path of linear
regression, GLM, and Cox regression models with grouped penalties. This
includes group selection methods such as group lasso, group MCP, and
group SCAD as well as bi-level selection methods such as the group
exponential lasso, the composite MCP, and the group bridge. For more
information, see Breheny and Huang (2009) <doi:10.4310/sii.2009.v2.n3.a10>,
Huang, Breheny, and Ma (2012) <doi:10.1214/12-sts392>, Breheny and Huang
(2015) <doi:10.1007/s11222-013-9424-2>, and Breheny (2015)
<doi:10.1111/biom.12300>, or visit the package homepage
<https://pbreheny.github.io/grpreg/>.

Patrick Breheny

grpreg

Regularization Paths for Regression Models with Grouped
Covariates

Yaohui Zeng

Ryan Kurth

plot.grpsurv.func function

<dl><dt>x</dt>
<dd>A <code>'grpsurv.func'</code> object, which is returned by
<code>predict.grpsurv</code> if <code>type='survival'</code> is specified. See
examples.</dd>
<dt>alpha</dt>
<dd>Controls alpha-blending (i.e., transparency). Useful if many
overlapping lines are present.</dd>
<dt>...</dt>
<dd>Other graphical parameters to pass to <code>plot</code></dd></dl>

Arguments

Author

Plot survival curve for grpsurv model — plot.grpsurv.func

<dl>

<dt>x</dt>
<dd>A <code>'grpsurv.func'</code> object, which is returned by
<code>predict.grpsurv</code> if <code>type='survival'</code> is specified. See
examples.</dd>


<dt>alpha</dt>
<dd>Controls alpha-blending (i.e., transparency). Useful if many
overlapping lines are present.</dd>


<dt>...</dt>
<dd>Other graphical parameters to pass to <code>plot</code></dd>

</dl>

plot.grpsurv.func: Plot survival curve for grpsurv model

Description

Usage

Arguments

Author

See Also

Examples