Compare matrices via Modified Krzanowski Correlation
Calculates the modified Krzanowski correlation between matrices, projecting the variance in each principal components of the first matrix in to the ret.dim.2 components of the second matrix.
KrzProjection(cov.x, cov.y, ...) ## Default S3 method: KrzProjection(cov.x, cov.y, ret.dim.1 = NULL, ret.dim.2 = NULL, ...) ## S3 method for class 'list' KrzProjection( cov.x, cov.y = NULL, ret.dim.1 = NULL, ret.dim.2 = NULL, parallel = FALSE, full.results = FALSE, ... )
cov.x |
Single covariance matrix ou list of covariance matrices. If cov.x is a single matrix is suplied, it is compared to cov.y. If cov.x is a list of matrices is suplied and no cov.y is suplied, all matrices are compared between each other. If cov.x is a list of matrices and a single cov.y matrix is suplied, all matrices in list are compared to it. |
cov.y |
First argument is compared to cov.y. If cov.x is a list, every element in cov.x is projected in cov.y. |
... |
aditional arguments passed to other methods |
ret.dim.1 |
number of retained dimensions for first matrix in comparison, default for nxn matrix is n/2-1 |
ret.dim.2 |
number of retained dimensions for second matrix in comparison, default for nxn matrix is n/2-1 |
parallel |
if TRUE computations are done in parallel. Some foreach backend must be registered, like doParallel or doMC. |
full.results |
if FALSE returns only total variance, if TRUE also per PC variance. |
Ratio of projected variance to total variance, and ratio of projected total in each PC
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) c2 <- RandomMatrix(10) KrzProjection(c1, c2) ## Not run: m.list <- RandomMatrix(10, 3) KrzProjection(m.list) KrzProjection(m.list, full.results = TRUE) KrzProjection(m.list, ret.dim.1 = 5, ret.dim.2 = 4) KrzProjection(m.list, ret.dim.1 = 4, ret.dim.2 = 5) KrzProjection(m.list, c1) KrzProjection(m.list, c1, full.results = TRUE) ## 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) #KrzProjection(m.list, parallel = TRUE)
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