df_gaussian

datasets

Performs projection predictive feature selection for generalized linear
models (Piironen, Paasiniemi, and Vehtari, 2020, <doi:10.1214/20-EJS1711>)
with or without multilevel or additive terms (Catalina, Bürkner, and
Vehtari, 2022, <https://proceedings.mlr.press/v151/catalina22a.html>), for
some ordinal and nominal regression models (Weber, Glass, and Vehtari, 2025,
<doi:10.1007/s00180-024-01506-0>), and for many other regression models
(using the latent projection by Catalina, Bürkner, and Vehtari, 2021,
<doi:10.48550/arXiv.2109.04702>, which can also be applied to most of the
former models). The package is compatible with the 'rstanarm' and 'brms'
packages, but other reference models can also be used. See the vignettes and
the documentation for more information and examples.

Osvaldo Martin

projpred

Projection Predictive Feature Selection

Juho Piironen

Markus Paasiniemi

Alejandro Catalina

Frank Weber

Aki Vehtari

Jonah Gabry

Marco Colombo

Paul-Christian Bürkner

Hamada S. Badr

Brian Sullivan

Sölvi Rögnvaldsson

 The LME4 Authors

Yann McLatchie

Juho Timonen

df_gaussian function

A simulated regression dataset containing 100 observations.<dl>
<dt>y</dt>
<dd>response, real-valued.</dd><dt>x</dt>
<dd>predictors, 20 in total. Mean and SD are approximately 0 and 1,
respectively.</dd>
</dl>

Format

Gaussian toy example — df_gaussian

A simulated regression dataset containing 100 observations.<dl>
<dt>y</dt>
<dd>response, real-valued.</dd>

<dt>x</dt>
<dd>predictors, 20 in total. Mean and SD are approximately 0 and 1,
respectively.</dd>


</dl>

Gaussian toy example

df_gaussian: Gaussian toy example

Description

Usage

Arguments

Format