| mat.sq.dist | R Documentation |
Computes the squared distance matrix of a set of covariance matrices
mat.sq.dist(Sm, dist. = "Riemannian", method = 0, pa = 0)
Sm |
a (p x p x m) array of covariance matrices, where p is the number of variables and m the number of groups. |
dist. |
"Riemannian" or "Euclidean" |
method |
an integer for the method of matrix inversion |
pa |
an integer for the parameter of matrix inversion |
The matrix of squared Riemannian or Euclidean distances
See minv for the method and the parameter used for the matrix inversion
# Data matrix of 2D landmark coordinates
data("Tropheus.IK.coord")
coords <- which(names(Tropheus.IK.coord) == "X1"):which(names(Tropheus.IK.coord) == "Y19")
proc.coord <- as.matrix(Tropheus.IK.coord[coords])
# Data reduction
phen.pca <- prcomp(proc.coord, rank. = 5, tol = sqrt(.Machine$double.eps))
pc.scores <- phen.pca$x
# Covariance matrix of each population
S.phen.pop <- cov.group(pc.scores, groups = Tropheus.IK.coord$POP.ID)
# Squared Riemannian distance matrix of the covariance matrices of all populations
eigen.phen.r <- mat.sq.dist(S.phen.pop, dist. = "Riemannian")
# Squared Euclidean distance matrix of the covariance matrices of all populations
eigen.phen.e <- mat.sq.dist(S.phen.pop, dist. = "Euclidean")