Principal Component of Explained Variance

Principal component of explained variance (PCEV) is a statistical tool for the analysis of a multivariate response vector. It is a dimension- reduction technique, similar to Principal component analysis (PCA), that seeks to maximize the proportion of variance (in the response vector) being explained by a set of covariates.

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:

vignette("pcev")

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.