higlasso.df

This synthetic data is taken from the linear interaction simulations from
the higlasso paper. The data generating model is:
$$Y = X_1 + X_2 + X_3 + X_4 + X_5 + X_1 X_2 + X_1 X_3 + X_2 X_3$$
$$ + X_1 X_4 + X_2 X_4 + X_3 X_4 + X_1 X_5$$
$$+ X_2 X_5 + X_3 X_5 + X_4 X_5 + \epsilon$$

datasets

Environmental health studies are increasingly measuring multiple pollutants
to characterize the joint health effects attributable to exposure mixtures.
However, the underlying dose-response relationship between toxicants and
health outcomes of interest may be highly nonlinear, with possible nonlinear
interaction effects. Hierarchical integrative group least absolute shrinkage
and selection operator (HiGLASSO), developed by Boss et al (2020)
<arXiv:2003.12844>, is a general framework to identify noteworthy nonlinear
main and interaction effects in the presence of group structures among a set
of exposures.

Alexander Rix

higlasso

Hierarchical Integrative Group LASSO

Jonathan Boss

higlasso.df function

A data.frame with 500 observations on 11 variables:<dl class="dl-horizontal">
<dt>Y</dt><dd>Continuous response.</dd>
<dt>X1-X10</dt><dd>Covariates.</dd>
</dl>

Format

Synthetic Example Data For Higlasso — higlasso.df

A data.frame with 500 observations on 11 variables:<dl class='dl-horizontal'>
<dt>Y</dt><dd>Continuous response.</dd>
<dt>X1-X10</dt><dd>Covariates.</dd>
</dl>

higlasso.df: Synthetic Example Data For Higlasso

Description

Usage

Arguments

Format