Inferring high-dimensional Gaussian graphical networks that change with multiple discrete covariates.
References
Dijkstra L, Godt A, Foraita R Inferring High-Dimensional Dynamic Networks Changing with Multiple Covariates (2024), Arxiv, https://arxiv.org/abs/2407.19978.
Author
Maintainer: Ronja Foraita foraita@leibniz-bips.de (ORCID)
Authors:
Louis Dijkstra (ORCID)
Other contributors:
Lukas Burk (ORCID) [contributor]
DFG [funder]
Leibniz Institute for Prevention Research and Epidemiology - BIPS R@leibniz-bips.de (ROR) [copyright holder]
Examples
data(grid)
W <- create_weight_matrix(type = "grid", k=3, l=3, plot = FALSE)
cvn <- CVN(grid, W, lambda1 = 1, lambda2 = 1:2,
n_cores = 1,
eps = 1e-2, maxiter = 1000, verbose = TRUE)
#> Estimating a CVN with 9 graphs...
#>
#> Number of cores: 1
#> Uses a warmstart...
#>
#> -------------------------
#> iteration 1 | 2.180956
#> iteration 2 | 0.115992
#> iteration 3 | 0.085702
#> iteration 4 | 0.030387
#> iteration 5 | 0.024326
#> iteration 6 | 0.016685
#> iteration 7 | 0.013629
#> iteration 8 | 0.012361
#> iteration 9 | 0.011060
#> iteration 10 | 0.009923
#> -------------------------
#> iteration 1 | 2.180956
#> iteration 2 | 0.115683
#> iteration 3 | 0.085927
#> iteration 4 | 0.029249
#> iteration 5 | 0.022338
#> iteration 6 | 0.017805
#> iteration 7 | 0.017968
#> iteration 8 | 0.015503
#> iteration 9 | 0.014200
#> iteration 10 | 0.012371
#> -------------------------
#> iteration 11 | 0.011107
#> iteration 12 | 0.010068
#> iteration 13 | 0.009907