compare_gvar: Compare two Bayesian GVAR models

In tsnet: Fitting, Comparing, and Visualizing Networks Based on Time Series Data

Compare two Bayesian GVAR models

Description

This function compares two Bayesian Graphical Vector Autoregressive models using matrix norms to test if the observed differences between two models is reliable. It computes the empirical distance between two models based on their point estimates and compares them using reference distributions created from their posterior distributions. Returns the p-value for the comparison based on a decision rule specified by the user. Details are available in Siepe, Kloft & Heck (2024) <doi:10.31234/osf.io/uwfjc>.

Usage

compare_gvar(
  fit_a,
  fit_b,
  cutoff = 5,
  dec_rule = "or",
  n_draws = 1000,
  comp = "frob",
  return_all = FALSE,
  sampling_method = "random",
  indices = NULL,
  burnin = 0
)

Arguments

fit_a	Fitted model object for Model A. This can be a tsnet_fit object (obtained from stan_gvar, a BGGM object (obtained from var_estimate, or extracted posterior samples (obtained from stan_fit_convert).
fit_b	Fitted model object for Model B. This can be a tsnet_fit object (obtained from stan_gvar, a BGGM object (obtained from var_estimate, or extracted posterior samples (obtained from stan_fit_convert).
cutoff	The percentage level of the test (default: 5) as integer.
dec_rule	The decision rule to be used. Currently supports default "or" (comparing against two reference distributions) and "comb" (combining the reference distributions). The use of "or" is recommended, as "comb" is less stable.
n_draws	The number of draws to use for reference distributions (default: 1000).
comp	The distance metric to use. Should be one of "frob" (Frobenius norm), "maxdiff" (maximum difference), or "l1" (L1 norm) (default: "frob"). The use of the Frobenius norm is recommended.
return_all	Logical indicating whether to return all distributions (default: FALSE). Has to be set to TRUE for plotting the results.
sampling_method	Draw sequential pairs of samples from the posterior, with certain distance between them ("sequential") or randomly from two halves of the posterior ("random"). The "random" method is preferred to account for potential autocorrelation between subsequent samples. Default: "random".
indices	A list of "beta" and "pcor" indices specifying which elements of the matrices to consider when calculating distances. If NULL (default), all elements of both matrices are considered. If provided, only the elements at these indices are considered. If only one of the matrices should have indices, the other one should be NULL. This can be useful if you want to calculate distances based on a subset of the elements in the matrices.
burnin	The number of burn-in iterations to discard (default: 0).

Value

A list (of class compare_gvar) containing the results of the comparison. The list includes:

sig_beta	Binary decision on whether there is a significant difference between the temporal networks of A and B
sig_pcor	Binary decision on whether there is a significant difference between the contemporaneous networks of A and B
res_beta	The null distribution for the temporal networks for both models
res_pcor	The null distribution for the contemporaneous networks for both models
emp_beta	The empirical distance between the two temporal networks
emp_pcor	The empirical distance between the two contemporaneous networks
larger_beta	The number of reference distances larger than the empirical distance for the temporal network
larger_pcor	The number of reference distances larger than the empirical distance for the temporal network
arguments	The arguments used in the function call

Examples

# use internal fit data of two individuals
data(fit_data)
test_res <- compare_gvar(fit_data[[1]],
fit_data[[2]],
n_draws = 100,
return_all = TRUE)
print(test_res)

tsnet documentation built on June 20, 2025, 9:08 a.m.