---
output: rmarkdown::html_document
title : READ ME
author : Marwan EL HOMSI
---


## Overview

The Elja package allows to perform Environment-Wide Association Studies in a 
simplified way and to produce results in the form of tables and graphs.

The Elja package provides two types of outputs for the results. 

First, a data frame including for each tested variable : 

* The value of the estimator (odd ratio or coefficients), the 95% Confidence 
Interval and the associated p-value 
* The number of observations taken into account in the model
* The Akaike Information Criterion of the model

In addition, two types of Manhattan plot can also be displayed, both with a 
visual representation of the alpha threshold at 0.05 and the corrected alpha 
threshold according to the Bonferroni method (1) and the Benjamini-Hochberg 
False Discovery Rate (FDR) method (2) :

* A Manhattan plot representing all the variables of the EWAS analysis
* A Manhattan plot representing only the significant results (p<0.05).



### Installation

You can install this package from CRAN or from GitHub.

```{r, eval = FALSE}

install.packages("Elja")
install_github("EHMarwan/Elja")

```


## Usage

```{r, message = FALSE, eval=FALSE}

library(Elja)

ELJAglm(var, var_adjust = NULL, family = binomial(link = "logit"), data, 
        manplot = TRUE, nbvalmanplot = 100, Bonferroni = FALSE, FDR = FALSE,
        manplotsign = FALSE)

ELJAlinear(var, var_adjust = NULL, data, manplot = TRUE, nbvalmanplot = 100, 
           Bonferroni = FALSE, FDR = FALSE, manplotsign = FALSE)

ELJAlogistic(var, var_adjust = NULL, data, manplot = TRUE, nbvalmanplot = 100, 
             Bonferroni = FALSE, FDR = FALSE, manplotsign = FALSE)

```

## Help

If you encounter a bug or have any question, please submit an issue with a 
reproducible example to [GitHub] <https://github.com/EHMarwan/Elja/issues>. 

### References

* Dunn OJ. Multiple Comparisons Among Means. Journal of the American 
Statistical Association. 1961;56(293):52‑64.
* Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical 
and Powerful Approach to Multiple Testing. Journal of the Royal Statistical 
Society: Series B (Methodological). 1995;57(1):289‑300.