plot.ncvsurv.func

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

Fits regularization paths for linear regression, GLM, and Cox
regression models using lasso or nonconvex penalties, in particular the
minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD)
penalty, with options for additional L2 penalties (the "elastic net" idea).
Utilities for carrying out cross-validation as well as post-fitting
visualization, summarization, inference, and prediction are also provided.
For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388>
or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.

Patrick Breheny

ncvreg

Regularization Paths for SCAD and MCP Penalized Regression
Models

plot.ncvsurv.func function

<dl><dt>x</dt>
<dd>A <code>'ncvsurv.func'</code> object, which is returned by
<code>predict.ncvsurv</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 ncvsurv model — plot.ncvsurv.func

<dl>

<dt>x</dt>
<dd>A <code>'ncvsurv.func'</code> object, which is returned by
<code>predict.ncvsurv</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.ncvsurv.func: Plot survival curve for ncvsurv model

Description

Usage

Arguments

Author

See Also

Examples