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:
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:
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.
Fixed to remove warnings due to conditions of length greater than one and recycling arrays in vector-array arithmetic
Updated CITATION file
estimation = "singular"; this option uses reduced-rank SVD for computing the component and the largest roo.
Expanded BLK data with multiple cell types.
Fixed the Johnstone approximation for Roy's largest root test.
Removed VIMPblock, as it is meaningless.
Added the possibility of computing multiple components (only for
estimation = "all").
Changed how default values for
computePCEV are handled internally.
Throw a meaningful error when there is missing data.
Added vignette and examples.
Changed the exact test when using shrinkage estimator.