eglatent: Learning extremal graph structure with latent variables

In sebastian-engelke/graphicalExtremes: Statistical Methodology for Graphical Extreme Value Models

Learning extremal graph structure with latent variables

Description

Following the methodology from \insertCiteengelkeTaeb2024;textualgraphicalExtremes, fits an extremal graph structure with latent variables.

Usage

eglatent(
  Gamma,
  lam1_list = c(0.1, 0.15, 0.19, 0.205),
  lam2_list = c(2),
  refit = TRUE,
  verbose = FALSE
)

Arguments

Gamma	conditionally negative semidefinite matrix. This will be typically the empirical variogram matrix.
lam1_list	Numeric vector of non-negative regularization parameters for eglatent. Default is lam1_list = c(0.1, 0.15, 0.19, 0.205).
lam2_list	Numeric vector of non-negative regularization parameters for eglatent. Default is lam2_list = c(2).
refit	Logical scalar, if TRUE then the model is refit on the estimated graph to obtain an estimate of the Gamma matrix on that graph. Default is refit = TRUE.
verbose	Logical scalar, indicating whether to print progress updates.

Value

The function fits one model for each combination of values in lam1_list and lam2_list. All returned objects have one entry per model. List consisting of:

graph	A list of igraph::graph objects representing the fitted graphs.
rk	Numeric vector containing the estimated ranks of the latent variables.
G_est	A list of numeric estimated \dxd variogram matrices \eGamma corresponding to the fitted graphs.
G_refit	A list of numeric estimated \dxd variogram matrices \eGamma refitted with fixed graphs corresponding to the fitted graphs.
lambdas	A list containing the values of lam1_list and lam2_list used for the model fit.

References

\insertAllCited

See Also

Other structure estimation methods: data2mpareto(), eglearn(), emst(), fit_graph_to_Theta()

sebastian-engelke/graphicalExtremes documentation built on Jan. 10, 2025, 10:02 a.m.