gen_nonlinear_data

Sample size (numeric; default = 100).

Number of features (numeric; default = 16).

Change to get different random data sets (numeric; default = 1).

seed

Mainly intended to demonstrate the use of basis expansion models for sparse additive modeling; intended for use with <code>expand_spline()</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

gen_nonlinear_data function

<dl><dt>n</dt>
<dd>Sample size (numeric; default = 100).</dd>
<dt>p</dt>
<dd>Number of features (numeric; default = 16).</dd>
<dt>seed</dt>
<dd>Change to get different random data sets (numeric; default = 1).</dd></dl>

Arguments

Generate nonlinear example data — gen_nonlinear_data

<dl>

<dt>n</dt>
<dd>Sample size (numeric; default = 100).</dd>


<dt>p</dt>
<dd>Number of features (numeric; default = 16).</dd>


<dt>seed</dt>
<dd>Change to get different random data sets (numeric; default = 1).</dd>

</dl>

gen_nonlinear_data: Generate nonlinear example data

Description

Usage

Arguments

Examples