README.Rmd

---
title: "readme"
author: "X. Bry, G. Cornu, F. Mortier and C. Trottier"
date: "25 juin 2018"
output: 
  md_document:
    variant: gfm
link-citations: true
bibliography: assets/SCGLR.bib
---
<!-- File generated from README.Rmd. Changes must be done from there -->

# SCGLR <img src="man/figures/SCGLR_small.jpg" align="right">

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/SCGLR)](https://cran.r-project.org/package=SCGLR)

## Introduction

**SCGLR** is an open source implementation of the Supervised Component Generalized Linear Regression 
[@bry13;-@bry16;-@bry18], which identifies, among a large set of potentially multicolinear predictors, the strong dimensions  most predictive of a set of responses. 

**SCGLR** is an extension of partial least square regression (PLSR) to the uni- and multivariate generalized linear framework. PLSR is particularly well suited for analyzing a large array of explanatory variables and many studies have demonstrated its predictive performance in various biological fields such as genetics [@boulesteix07] or ecology [@carrascal09].  While PLSR is well adapted for continuous variables, maximizing the covariance between linear combination of dependent variables, and linear combinations of covariates, **SCGLR** is suited for non-Gaussian outcomes and non-continuous covariates.

**SCGLR** is a model-based approach that extends PLS [@tenenhaus98], PCA on instrumental variables [@sabatier89], canonical correspondence analysis [@terbraak87], and other related empirical methods, by capturing the trade-off between goodness-of-fit and common structural relevance of explanatory components. The notion of structural relevance has been introduced [@bry15].

**SCGLR** can deal with covariates partitioned in several groups called "themes", plus a group of additional covariates. Each theme is searched for orthogonal components representing its variables in the model, whereas the additional covariates appear directly in the model, without the mediation of a component [@bry18].

**SCGLR** works also for mixed models using an extension of the Schall's algorithm to combine Supervised-Component regression with GLMM estimation in the multivariate context.


## Installation

``` r
# Install release version from CRAN
install.packages("SCGLR")

# Install development version from GitHub
remotes::install_github("SCnext/SCGLR")
```

## Main functions and works in progress
**SCGLR** is designed to deal with outcomes from multiple distributions: Gaussian, Bernoulli, binomial and Poisson separately or simultaneously [@bry13]. Moreover **SCGLR** is also able to deal with multiple conceptually homogeneous explanatory variable groups [@bry18].  

**SCGLR** is a set of **R** functions illustrated on a floristic data set, _genus_. `scglr` and `scglrTheme` are respectively dedicated to fitting the model with one or more thematic group of regressors. `scglrCrossVal` and `scglrThemeBackward` are respectively dedicated to selecting the number of components. `print`, `summary` and `plot` methods are also available for the `scglr` and `scglrTheme` function results.

Different works are in progress both dealing for instance with the inclusion of random effects extending **SCGLR** to the generalized linear mixed model framework [@chauvet18;-@chauvet18b], or the Cox regression model.

## Fundings
The GAMBAS project funded by the Agence Nationale pour la Recherche (ANR-18-CE02-0025). <https://gambas.cirad.fr/>

<img src="man/figures/logo_gambas.jpg">

## References