sce: Computes the structured covariance matrix estimator (SCE)

In scov: Structured Covariances Estimators for Pairwise and Spatial Covariates

Computes the structured covariance matrix estimator (SCE)

Description

This function computes the SCE estimator for large covariances in the presence of pairwise and spatial covariates from Metodiev et al. (2024).

Usage

sce(
  pairwise_covariate_matrices,
  adj_matrix,
  dataset,
  mean_estim = NULL,
  sd_estim = NULL,
  grid_size = 100,
  parallelize = TRUE,
  ncores = 8,
  adj_positions = 1:nrow(adj_matrix),
  interaction_effects = list(),
  init = NULL,
  verbose = TRUE,
  joint_estimation = FALSE
)

Arguments

pairwise_covariate_matrices	named list of square matrices
adj_matrix	adjacency matrix of the spatial covariate
dataset	the dataset given in matrix form
mean_estim	mean vector estimate
sd_estim	standard deviation vector estimate
grid_size	grid-size for spatial effect
parallelize	uses parallel-processing if TRUE
ncores	number of cores for the parallelization
adj_positions	positions within the adjacency matrix
interaction_effects	list of interaction effects
init	the initialization parameter vector
verbose	prints progress if TRUE
joint_estimation	estimates everything jointly if TRUE, uses a 2 step procedure if FALSE

Value

Returns a named list with the following elements:

parm, estimated parameters of pairwise, spatial effects, average_effects, average effects of the covariates, corrmat_estim, estimator of the correlation matrix, covmat_estim, estimator of the covariance matrix, bic, the Bayesian information criterion (BIC)

References

Metodiev, M., Perrot-Dockès, M., Ouadah, S., Fosdick, B. K., Robin, S., Latouche, P., & Raftery, A. E. (2024). A Structured Estimator for large Covariance Matrices in the Presence of Pairwise and Spatial Covariates. arXiv preprint arXiv:2411.04520.

scov documentation built on March 18, 2026, 5:08 p.m.