Compare matrices via Krzanowski Correlation
Calculates covariance matrix correlation via Krzanowski Correlation
KrzCor(cov.x, cov.y, ...) ## Default S3 method: KrzCor(cov.x, cov.y, ret.dim = NULL, ...) ## S3 method for class 'list' KrzCor( cov.x, cov.y = NULL, ret.dim = NULL, repeat.vector = NULL, parallel = FALSE, ... ) ## S3 method for class 'mcmc_sample' KrzCor(cov.x, cov.y, ret.dim = NULL, parallel = FALSE, ...)
cov.x |
Single covariance matrix or list of covariance matrices. If single matrix is suplied, it is compared to cov.y. If list is suplied and no cov.y is suplied, all matrices are compared to each other. If cov.y is suplied, all matrices in list are compared to it. |
cov.y |
First argument is compared to cov.y. Optional if cov.x is a list. |
... |
aditional arguments passed to other methods |
ret.dim |
number of retained dimensions in the comparison, default for nxn matrix is n/2-1 |
repeat.vector |
Vector of repeatabilities for correlation correction. |
parallel |
if TRUE and a list is passed, computations are done in parallel. Some foreach backend must be registered, like doParallel or doMC. |
If cov.x and cov.y are passed, returns Kzranowski correlation
If cov.x is a list and cov.y is passed, same as above, but for all matrices in cov.x.
If only a list is passed to cov.x, a matrix of Kzranowski correlation values. If repeat.vector is passed, comparison matrix is corrected above diagonal and repeatabilities returned in diagonal.
Diogo Melo, Guilherme Garcia
Krzanowski, W. J. (1979). Between-Groups Comparison of Principal Components. Journal of the American Statistical Association, 74(367), 703. doi:10.2307/2286995
c1 <- RandomMatrix(10, 1, 1, 10) c2 <- RandomMatrix(10, 1, 1, 10) c3 <- RandomMatrix(10, 1, 1, 10) KrzCor(c1, c2) KrzCor(list(c1, c2, c3)) ## Not run: reps <- unlist(lapply(list(c1, c2, c3), MonteCarloRep, 10, KrzCor, iterations = 10)) KrzCor(list(c1, c2, c3), repeat.vector = reps) c4 <- RandomMatrix(10) KrzCor(list(c1, c2, c3), c4) ## End(Not run) #Multiple threads can be used with some foreach backend library, like doMC or doParallel #library(doParallel) ##Windows: #cl <- makeCluster(2) #registerDoParallel(cl) ##Mac and Linux: #registerDoParallel(cores = 2) #KrzCor(list(c1, c2, c3), parallel = TRUE)
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