sim.xy

<p>Simmulated data for the high-5-sized scenario and low-sized. It contains a list
  with the who cases, each of them with the following fields:
  * <code>x</code> X matrix for training set
  * <code>y</code> Y vector for training set
  * <code>xval</code> X matrix for validation set
  * <code>yval</code> Y vector for validation set
  * <code>beta</code> true beta array</p>

datasets

A novel feature selection algorithm for linear regression called
BOSO (Bilevel Optimization Selector Operator). The main contribution is the
use a bilevel optimization problem to select the variables in the training
problem that minimize the error in the validation set. Preprint available:
[Valcarcel, L. V., San Jose-Eneriz, E., Cendoya, X., Rubio, A., Agirre, X.,
Prosper, F., & Planes, F. J. (2020). "BOSO: a novel feature selection
algorithm for linear regression with high-dimensional data." bioRxiv.
<doi:10.1101/2020.11.18.388579>].
In order to run the vignette, it is recommended to install the 'bestsubset' package,
using the following command: devtools::install_github(repo="ryantibs/best-subset", subdir="bestsubset").
If you do not have gurobi, run devtools::install_github(repo="lvalcarcel/best-subset", subdir="bestsubset").
Moreover, to install cplexAPI you can check <https://github.com/lvalcarcel/cplexAPI>.

Luis V. Valcarcel

BOSO

Bilevel Optimization Selector Operator

Edurne San Jose-Eneriz

Xabier Cendoya

Angel Rubio

Xabier Agirre

Felipe Prósper

Francisco J. Planes

sim.xy function

High-5 and Low setting data — sim.xy

High-5 and Low setting data

sim.xy: High-5 and Low setting data

Description

Usage

Arguments

References