rcormat: Randomly Generate Low-Rank Correlation Matrix

In cds: Constrained Dual Scaling for Detecting Response Styles

Randomly Generate Low-Rank Correlation Matrix

Description

Generate a correlation matrix as R = LL' where the rows of L are of length 1, L is of rank r and the matrix L is sparse (depending on sparse.prop. The loadings in L are sampled from a standard normal distribution, after which sparse.prop is used to set a randomly chosen number of loadings in each row equal to zero. To ensure that a correlation matrix results, the rows are normalized.

Usage

rcormat(m, r = 3L, sparse.prop = 0.5)

Arguments

m	integer; the number of variables.
r	integer; the required rank.
sparse.prop	the proportion of zeros in the rows of the matrix.

Value

A list with the following components:

R	The sampled correlation matrix
L	The loading matrix

Examples

R <- rcormat(m = 10)$R
eigen(R)

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