Construct a low-rank covariance matrix with specified eigenvalues, where the eigenvectors are simulated from uniform distributions.
rcovmat(eigs = k:1, m = 10, k = 2, perc = list(c(0.4, 0.2, 0.4), c(0.2,
0.4, 0.4)), limits = list(l1 = c(0.5, 1), l2 = c(-1, -0.5), l3 = c(-0.1,
0.1)), random = TRUE)Vector of $k$ eigenvalues.
Integer; the number of rows and columns of the matrix.
Integer; the rank of the matrix.
List of $k$ vectors giving the sampling proportions for the uniform sampling of the eigenvectors, for each dimension.
List of length 2 vectors, one for each uniform sample, giving the lower and upper bounds of the uniform distribution.
Logical; randomize the order of the loading per dimension or not.