PCAsimilarity {evolqg}

Compare matrices using PCA similarity factor

Description

Compare matrices using PCA similarity factor

Usage

PCAsimilarity(cov.x, cov.y, ...)

## Default S3 method:
PCAsimilarity(cov.x, cov.y, ret.dim = NULL, ...)

## S3 method for class 'list'
PCAsimilarity(cov.x, cov.y = NULL, ..., repeat.vector = NULL, parallel = FALSE)

## S3 method for class 'mcmc_sample'
PCAsimilarity(cov.x, cov.y, ..., parallel = FALSE)

Arguments

cov.x

Single covariance matrix or list of covariance matrices. If cov.x is a single matrix, it is compared to cov.y. If cov.x is a list and no cov.y is supplied, all matrices are compared to each other. If cov.x is a list and cov.y is supplied, all matrices in cov.x are compared to cov.y.

cov.y

First argument is compared to cov.y.

...

additional arguments passed to other methods

ret.dim

number of retained dimensions in the comparison. Defaults to all.

repeat.vector

Vector of repeatabilities for correlation correction.

parallel

if TRUE computations are done in parallel. Some foreach back-end must be registered, like doParallel or doMC.

Value

Ratio of projected variance to total variance

Author(s)

Edgar Zanella Alvarenga

References

Singhal, A. and Seborg, D. E. (2005), Clustering multivariate time-series data. J. Chemometrics, 19: 427-438. doi: 10.1002/cem.945

See Also

KrzProjection,KrzCor,RandomSkewers,MantelCor

Examples

c1 <- RandomMatrix(10)
c2 <- RandomMatrix(10)
PCAsimilarity(c1, c2)

m.list <- RandomMatrix(10, 3)
PCAsimilarity(m.list)

PCAsimilarity(m.list, c1)