glm.gaussian

A GLM family object specifies the type of model fit, provides the appropriate response object and makes sure it is represented in the right form for the model family, and allows for optional parameters such as a weight vector.

Extremely efficient procedures for fitting the entire group lasso and group elastic net regularization path for GLMs, multinomial, the Cox model and multi-task Gaussian models. Similar to the R package 'glmnet' in scope of models, and in computational speed. This package provides R bindings to the C++ code underlying the corresponding Python package 'adelie'. These bindings offer a general purpose group elastic net solver,
a wide range of matrix classes that can exploit special structure
to allow large-scale inputs, and an assortment of
generalized linear model classes for fitting various types of data.
The package is an implementation of Yang, J. and Hastie, T. (2024) <doi:10.48550/arXiv.2405.08631>.

Trevor Hastie

adelie

Group Lasso and Elastic Net Solver for Generalized Linear Models

James Yang

Balasubramanian Narasimhan

glm.gaussian function

<dl><dt>y</dt>
<dd>Response vector.</dd>
<dt>weights</dt>
<dd>Observation weight vector, with default <code>NULL</code>.</dd>
<dt>opt</dt>
<dd>If <code>TRUE</code> (default), an optimized routine is run.</dd></dl>

Arguments

James Yang, Trevor Hastie, and Balasubramanian Narasimhan Maintainer: Trevor Hastie <a href="/link/hastie%40stanford.edu?package=adelie&version=1.0.7" data-mini-rdoc="adelie::hastie@stanford.edu">hastie@stanford.edu</a>

Author

Creates a Gaussian GLM family object. — glm.gaussian

<dl>

<dt>y</dt>
<dd>Response vector.</dd>


<dt>weights</dt>
<dd>Observation weight vector, with default <code>NULL</code>.</dd>


<dt>opt</dt>
<dd>If <code>TRUE</code> (default), an optimized routine is run.</dd>

</dl>

James Yang, Trevor Hastie, and Balasubramanian Narasimhan Maintainer: Trevor Hastie <a href='mailto:hastie@stanford.edu'>hastie@stanford.edu</a>

glm.gaussian: Creates a Gaussian GLM family object.

Description

Usage

Value

Arguments

Author

See Also

Examples