start_resids: Sample covariance matrices for each mode.

In dcgerard/tensr: Covariance Inference and Decompositions for Tensor Datasets

Sample covariance matrices for each mode.

Description

Scaled Cholesky square roots of the sample covariance matrix and its inverse.

Usage

start_resids(Y, mode_rep = NULL)

Arguments

Y	An array of numeric data.
mode_rep	A vector of integers. The modes specified by mode_rep will be given an identity matrix instead of a sample-based matrix.

Details

This function will take the sample covariance matrix of the ith matricization of an input array Y and will return (1) its lower-triangular Cholesky square root scaled down to have determinant 1 and (2) the inverse of its lower-triangular Cholesky square root scaled down to have determinant 1. This function is primarily used to obtain starting values for the Gibbs sampler implemented in equi_mcmc.

Value

Sig A list where Sig[[i]] is the lower-triangular Cholesky square root of the sample covariance matrix of the ith mode, scaled down to have determinant 1.

Sig_inv A list where Sig_inv[[i]] is the inverse of the lower-triangular Cholesky square root of the sample covariance matrix of the ith mode, scaled down to have determinant 1.

If mode_rep is not NULL, then the list elements in Sig and Sig_inv specified in mode_rep will be the identity matrix instead of sample-based matrices.

Author(s)

David Gerard.

See Also

equi_mcmc.

dcgerard/tensr documentation built on Oct. 4, 2022, 5:58 p.m.