README.Rmd

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output: github_document
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# mcvis: visualisation of multicollinearity in data

![R-CMD-check](https://github.com/kevinwang09/mcvis/workflows/R-CMD-check/badge.svg)
[![Codecov test coverage](https://codecov.io/gh/kevinwang09/mcvis/branch/master/graph/badge.svg)](https://codecov.io/gh/kevinwang09/mcvis?branch=master)

<img src="https://github.com/kevinwang09/mcvis/raw/master/inst/mcvis_logo.png" align="right" width="200" />

## Introduction

`mcvis` is a R package for visualising multicollinearity in a data design matrix. The underlying methodology uses resampling techniques to identify groups of variables that causes multicollinearity. 

You can learn more about `mcvis` from [this vignette](https://kevinwang09.github.io/mcvis/articles/mcvis.html). 


## Installation 

`mcvis` can be installed using the `devtools` package. 

```{r, eval = FALSE}
devtools::install_github("kevinwang09/mcvis")
```


## A quick example

Using a `mcvis` bipartite plot, variables (bottom row) that cause strong collinearity are visualised as bolded lines connecting with our "tau" statistics (top row). 

```{r, warning = FALSE, message = FALSE, cache = TRUE}
library(mcvis)
library(ggplot2)

set.seed(1)
p = 10
n = 100

X = matrix(rnorm(n*p), ncol = p)
## Inducing collinearity into the design matrix
X[,1] = X[,2] + rnorm(n, 0, 0.1) 


mcvis_result = mcvis(X)
plot(mcvis_result)
```



# Reference

+ *Lin, C., Wang, K. Y. X., & Mueller, S. (2020). mcvis: A new framework for collinearity discovery, diagnostic and visualization. Journal of Computational and Graphical Statistics, In Press.*