rcovmat: Construct a Structured Covariance Matrix for Simulations

In cds: Constrained Dual Scaling for Detecting Response Styles

Construct a Structured Covariance Matrix for Simulations

Description

Construct a low-rank covariance matrix with specified eigenvalues, where the eigenvectors are simulated from uniform distributions.

Usage

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
)

Arguments

eigs	Vector of $k$ eigenvalues.
m	Integer; the number of rows and columns of the matrix.
k	Integer; the rank of the matrix.
perc	List of $k$ vectors giving the sampling proportions for the uniform sampling of the eigenvectors, for each dimension.
limits	List of length 2 vectors, one for each uniform sample, giving the lower and upper bounds of the uniform distribution.
random	Logical; randomize the order of the loading per dimension or not.

cds documentation built on April 16, 2025, 1:10 a.m.