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Random Covariance Model

randCov.Rd

This function implements the Random Covariance Model (RCM) for joint estimation of multiple sparse precision matrices. Optimization is conducted using block coordinate descent.

Usage

randCov(x, lambda1, lambda2, lambda3 = 0)

Arguments

x

List of \(K\) data matrices each of dimension \(n_k\) x \(p\).

lambda1

Non-negative scalar. Induces sparsity in subject-level matrices.

lambda2

Non-negative scalar. Induces similarity between subject-level matrices and group-level matrix.

lambda3

Non-negative scalar. Induces sparsity in group-level matrix.

Value

A list of length 2 containing:

  1. Group-level precision matrix estimate (Omega0).

  2. \(p\) x \(p\) x \(K\) array of \(K\) subject-level precision matrix estimates (Omegas).

References

Zhang, Lin, Andrew DiLernia, Karina Quevedo, Jazmin Camchong, Kelvin Lim, and Wei Pan. "A Random Covariance Model for Bi-level Graphical Modeling with Application to Resting-state FMRI Data." 2019. https://arxiv.org/pdf/1910.00103.pdf

Author

Lin Zhang