Function that gives an overview about the number of edges in each subgraph over all fitted CVN models.
Value
A data.frame showing the number of edges in each subgraph, the number of edges present in all graphs (core edges) and the number of edges that are unique for a subgraph
Examples
data(grid)
W <- create_weight_matrix("grid", 3, 3)
# lambdas:
lambda1 = c(1, 1.5)
lambda2 = .2
fit <- CVN(grid, W, lambda1 = lambda1, lambda2 = lambda2, n_cores = 1)
#> Estimating a CVN with 9 graphs...
#>
#> Number of cores: 1
#> Uses a warmstart...
#>
#> -------------------------
#> iteration 1 | 2.180956
#> iteration 2 | 0.115992
#> iteration 3 | 0.074886
#> iteration 4 | 0.048989
#> iteration 5 | 0.026376
#> iteration 6 | 0.009400
#> iteration 7 | 0.006532
#> iteration 8 | 0.004188
#> iteration 9 | 0.003427
#> iteration 10 | 0.002602
#> -------------------------
#> iteration 11 | 0.002151
#> iteration 12 | 0.002050
#> iteration 13 | 0.001641
#> iteration 14 | 0.001472
#> iteration 15 | 0.001493
#> iteration 16 | 0.001085
#> iteration 17 | 0.000864
#> iteration 18 | 0.000816
#> iteration 19 | 0.000768
#> iteration 20 | 0.000608
#> -------------------------
#> iteration 21 | 0.000582
#> iteration 22 | 0.000563
#> iteration 23 | 0.000575
#> iteration 24 | 0.000516
#> iteration 25 | 0.000486
#> iteration 26 | 0.000449
#> iteration 27 | 0.000419
#> iteration 28 | 0.000404
#> iteration 29 | 0.000372
#> iteration 30 | 0.000355
#> -------------------------
#> iteration 31 | 0.000335
#> iteration 32 | 0.000322
#> iteration 33 | 0.000300
#> iteration 34 | 0.000281
#> iteration 35 | 0.000272
#> iteration 36 | 0.000262
#> iteration 37 | 0.000257
#> iteration 38 | 0.000245
#> iteration 39 | 0.000236
#> iteration 40 | 0.000227
#> -------------------------
#> iteration 41 | 0.000213
#> iteration 42 | 0.000205
#> iteration 43 | 0.000193
#> iteration 44 | 0.000183
#> iteration 45 | 0.000175
#> iteration 46 | 0.000169
#> iteration 47 | 0.000163
#> iteration 48 | 0.000157
#> iteration 49 | 0.000152
#> iteration 50 | 0.000140
#> -------------------------
#> iteration 51 | 0.000136
#> iteration 52 | 0.000109
#> iteration 53 | 0.000114
#> iteration 54 | 0.000102
#> iteration 55 | 0.000101
#> iteration 56 | 0.000093
#> -------------------------
#> iteration 1 | 1.887193
#> iteration 2 | 0.135032
#> iteration 3 | 0.091797
#> iteration 4 | 0.051830
#> iteration 5 | 0.029767
#> iteration 6 | 0.023468
#> iteration 7 | 0.011319
#> iteration 8 | 0.007583
#> iteration 9 | 0.006705
#> iteration 10 | 0.004910
#> -------------------------
#> iteration 11 | 0.004122
#> iteration 12 | 0.003544
#> iteration 13 | 0.003412
#> iteration 14 | 0.002673
#> iteration 15 | 0.002175
#> iteration 16 | 0.001636
#> iteration 17 | 0.001523
#> iteration 18 | 0.001362
#> iteration 19 | 0.001236
#> iteration 20 | 0.001114
#> -------------------------
#> iteration 21 | 0.001057
#> iteration 22 | 0.001067
#> iteration 23 | 0.000890
#> iteration 24 | 0.000790
#> iteration 25 | 0.000728
#> iteration 26 | 0.000669
#> iteration 27 | 0.000636
#> iteration 28 | 0.000607
#> iteration 29 | 0.000556
#> iteration 30 | 0.000526
#> -------------------------
#> iteration 31 | 0.000482
#> iteration 32 | 0.000464
#> iteration 33 | 0.000436
#> iteration 34 | 0.000424
#> iteration 35 | 0.000383
#> iteration 36 | 0.000331
#> iteration 37 | 0.000316
#> iteration 38 | 0.000308
#> iteration 39 | 0.000319
#> iteration 40 | 0.000290
#> -------------------------
#> iteration 41 | 0.000277
#> iteration 42 | 0.000259
#> iteration 43 | 0.000251
#> iteration 44 | 0.000218
#> iteration 45 | 0.000194
#> iteration 46 | 0.000216
#> iteration 47 | 0.000192
#> iteration 48 | 0.000183
#> iteration 49 | 0.000177
#> iteration 50 | 0.000171
#> -------------------------
#> iteration 51 | 0.000165
#> iteration 52 | 0.000160
#> iteration 53 | 0.000151
#> iteration 54 | 0.000130
#> iteration 55 | 0.000115
#> iteration 56 | 0.000106
#> iteration 57 | 0.000103
#> iteration 58 | 0.000100
# Edge summary for a list of CVN models
cvn_edge_summary(fit)
#> E(g1) E(g2) E(g3) E(g4) E(g5) E(g6) E(g7) E(g8) E(g9) E(core) E(g1_u) E(g2_u)
#> 1 84 86 84 82 84 88 88 90 84 34 0 0
#> 2 86 84 86 82 80 82 86 90 82 32 0 0
#> E(g3_u) E(g4_u) E(g5_u) E(g6_u) E(g7_u) E(g8_u) E(g9_u)
#> 1 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0
# Edge summary for a single CVN
fit2 <- extract_cvn(fit, id = 2)
cvn_edge_summary(fit2)
#> edges core_edges unique_edges
#> 1 86 32 0
#> 2 84 32 0
#> 3 86 32 0
#> 4 82 32 0
#> 5 80 32 0
#> 6 82 32 0
#> 7 86 32 0
#> 8 90 32 0
#> 9 82 32 0