InternalFunctions

standardize

ICscoreCalculation

Trace

Internal functions used by BOSO in the different steps of the algorithm
Centering and scaling convenience function
based on the function from bestSubset

internal

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

InternalFunctions function

<dl><dt>object</dt>
<dd>Fitted 'BOSO' or object</dd>
<dt>IC</dt>
<dd>information criteria to be used</dd>
<dt>n</dt>
<dd>Fitted 'BOSO' object</dd>
<dt>p</dt>
<dd>Fitted 'BOSO' object</dd>
<dt>mat</dt>
<dd>a numeric matrix</dd></dl>

Arguments

BOSO Internal Functions — InternalFunctions

<dl>

<dt>object</dt>
<dd>Fitted 'BOSO' or object</dd>


<dt>IC</dt>
<dd>information criteria to be used</dd>


<dt>n</dt>
<dd>Fitted 'BOSO' object</dd>


<dt>p</dt>
<dd>Fitted 'BOSO' object</dd>


<dt>mat</dt>
<dd>a numeric matrix</dd>

</dl>

BOSO Internal Functions

InternalFunctions: BOSO Internal Functions

Description

Usage

Value

Arguments