select_coglasso.md
In coglasso: Collaborative Graphical Lasso - Multi-Omics Network Reconstruction

print.select_coglasso works with all functions

Code
  cg <- coglasso(multi_omics_sd_micro, p = 4, nlambda_w = 3, nlambda_b = 3, nc = 2,
    verbose = FALSE)
  sel_cg <- select_coglasso(cg, method = "ebic", verbose = FALSE)
  print(sel_cg)
Output
  Selected network estimated with collaborative graphical lasso

  The call was:
  select_coglasso(coglasso_obj = cg, method = "ebic", verbose = FALSE)

  The model selection method was:
  ebic
  The density of the selected network is:
  0.4666667

  Networks are made of 2 omics layers, for a total of 6 nodes
  For each layer they have: 4 and 2 nodes, respectively

  The selected value for lambda within is:
  0.0874
  The selected value for lambda between is:
  0.4816
  The selected value for c is:
  0.01

  The total number of hyperparameter combinations explored was:
  18
  The values explored for lambda within were:
  0.8743, 0.6852, 0.0874
  The values explored for lambda between were:
  0.4816, 0.3775, 0.0482
  The values explored for c were:
  0.01, 100

  Plot the selected network with:
  plot(sel_cg)
Code
  sel_cg <- select_coglasso(cg, method = "xestars", rep_num = 3, verbose = FALSE)
  print(sel_cg)
Output
  Selected network estimated with collaborative graphical lasso

  The call was:
  select_coglasso(coglasso_obj = cg, method = "xestars", rep_num = 3, 
      verbose = FALSE)

  The model selection method was:
  xestars
  The density of the selected network is:
  0

  Networks are made of 2 omics layers, for a total of 6 nodes
  For each layer they have: 4 and 2 nodes, respectively

  The selected value for lambda within is:
  0.8743
  The selected value for lambda between is:
  0.4816
  The selected value for c is:
  0.01

  The total number of hyperparameter combinations explored was:
  18
  The values explored for lambda within were:
  0.8743, 0.6852, 0.0874
  The values explored for lambda between were:
  0.4816, 0.3775, 0.0482
  The values explored for c were:
  0.01, 100

  Plot the selected network with:
  plot(sel_cg)
Code
  sel_cg <- xstars(cg, rep_num = 3, verbose = FALSE)
  print(sel_cg)
Output
  Selected network estimated with collaborative graphical lasso

  The call was:
  xstars(coglasso_obj = cg, rep_num = 3, verbose = FALSE)

  The model selection method was:
  xstars
  The density of the selected network is:
  0

  Networks are made of 2 omics layers, for a total of 6 nodes
  For each layer they have: 4 and 2 nodes, respectively

  The selected value for lambda within is:
  0.8743
  The selected value for lambda between is:
  0.4816
  The selected value for c is:
  0.01

  The total number of hyperparameter combinations explored was:
  18
  The values explored for lambda within were:
  0.8743, 0.6852, 0.0874
  The values explored for lambda between were:
  0.4816, 0.3775, 0.0482
  The values explored for c were:
  0.01, 100

  Plot the selected network with:
  plot(sel_cg)
Code
  sel_cg <- bs(multi_omics_sd_micro, p = 4, nlambda_w = 3, nlambda_b = 3, nc = 2,
    rep_num = 3, verbose = FALSE)
  print(sel_cg)
Output
  Selected network estimated with collaborative graphical lasso

  The call was:
  bs(data = multi_omics_sd_micro, p = 4, nlambda_w = 3, nlambda_b = 3, 
      nc = 2, rep_num = 3, verbose = FALSE)

  The model selection method was:
  xestars
  The density of the selected network is:
  0

  Networks are made of 2 omics layers, for a total of 6 nodes
  For each layer they have: 4 and 2 nodes, respectively

  The selected value for lambda within is:
  0.8743
  The selected value for lambda between is:
  0.4816
  The selected value for c is:
  0.01

  The total number of hyperparameter combinations explored was:
  18
  The values explored for lambda within were:
  0.8743, 0.6852, 0.0874
  The values explored for lambda between were:
  0.4816, 0.3775, 0.0482
  The values explored for c were:
  0.01, 100

  Plot the selected network with:
  plot(sel_cg)