datasets

cleveland

hungarian

irlcs_radon_syn

switzerland

Detrano data sets (cleveland, hungarian, switzerland, va);
The Iowa Radon Lung Cancer Study (irlcs_radon_syn): Data simulated to resemble the IRLCS study;
Sheddon survival data (Z: clinical covariates, S:survival outcome)

An implementation of ranked sparsity methods, including
penalized regression methods such as the sparsity-ranked lasso, its
non-convex alternatives, and elastic net, as well as the sparsity-ranked
Bayesian Information Criterion. As described in Peterson and
Cavanaugh (2022) <doi:10.1007/s10182-021-00431-7>, ranked
sparsity is a philosophy with methods primarily useful for
variable selection in the presence of prior informational
asymmetry, which occurs in the context of trying to perform variable
selection in the presence of interactions and/or polynomials. Ultimately,
this package attempts to facilitate dealing with cumbersome interactions
and polynomials while not avoiding them entirely. Typically, models
selected under ranked sparsity principles will also be more transparent,
having fewer falsely selected interactions and polynomials than other
methods.

Ryan Andrew Peterson

sparseR

Variable Selection under Ranked Sparsity Principles for
Interactions and Polynomials

datasets function

An object of class <code>data.frame</code> with 303 rows and 14 columns.
An object of class <code>data.frame</code> with 294 rows and 14 columns.
An object of class <code>data.frame</code> with 123 rows and 14 columns.
An object of class <code>data.frame</code> with 200 rows and 14 columns.
An object of class <code>data.frame</code> with 1027 rows and 16 columns.
An object of class <code>data.frame</code> with 442 rows and 6 columns.
An object of class <code>Surv</code> with 442 rows and 2 columns.

Format

Data sets — datasets

Data sets

datasets: Data sets

Description

Usage

Arguments

Format

References