# PCEV

R package which implements Principal components of explained variance
(PCEV).

PCEV is a statistical tool for the analysis of a mutivariate response
vector. It is a dimension-reduction technique, similar to Principal
Components Analysis (PCA), that seeks to maximize the proportion of
variance (in the response vector) being explained by a set of
covariates. It implements three versions:

- the classic version, when p < n;
- the singular version, when p > n;
- the block version, our extension of the algorithm for the case of a
high number of data points (p>>n).

For the first two versions, we provide hypothesis testing based on
Roy’s largest root.

For more information you can look at the vignette.
Alternatively, if you have already installed the package along with the
vignette, you can access the vignette from within `R`

by
using the following command:

## Installation

This package is available on CRAN. Alternatively,
you can install from GitHub using the devtools
package:

```
library(devtools)
devtools::install_github('GreenwoodLab/pcev', build_vignettes = TRUE)
```

The main function is `computePCEV`

, and indeed most users
will only need this one function. See the documentation for more
information about its parameters and for some examples.

## References

- Turgeon, M., Oualkacha, K., Ciampi, A., Miftah, H., Dehghan, G.,
Zanke, B.W., Benedet, A.L., Rosa-Neto, P., Greenwood, C.M.T., Labbe, A.,
for the Alzheimer’s Disease Neuroimaging Initiative. “Principal
component of explained variance: an efficient and optimal data dimension
reduction framework for association studies”. To appear in Statistical Methods in
Medical Research.