plot.select_coglasso: Plot selected `coglasso` networks

Description

plot.select_coglasso() creates an annotated plot of a coglasso selected network from an object of S3 class select_coglasso. Variables from different data sets will have different color coding. To plot the network, it's enough to use plot() call on the select_coglasso object.

plot.coglasso() has the same functioning as select_coglasso.plot(), but from an object of S3 class coglasso. In this case, it is compulsory to specify index_c, index_lw, and index_lb.

Usage

# S3 method for select_coglasso
plot(
  x,
  index_c = NULL,
  index_lw = NULL,
  index_lb = NULL,
  node_labels = TRUE,
  hide_isolated = TRUE,
  ...
)
# S3 method for coglasso
plot(
  x,
  index_c,
  index_lw,
  index_lb,
  node_labels = TRUE,
  hide_isolated = TRUE,
  ...
)

Value

Returns NULL, invisibly.

Arguments

x: The object of S3 class select_coglasso.
index_c: The index of the \(c\) value different from the one selected by model selection. To set only if the desired network is not the selected one.
index_lw: The index of the \(\lambda_w\) value of the chosen non-optimal network. To set only if the desired network is not the selected one.
index_lb: The index of the \(\lambda_b\) value of the chosen non-optimal network. To set only if the desired network is not the selected one.
node_labels: Show node names in the network. Defaults to TRUE.
hide_isolated: Hide nodes that are not connected to any other node. Defaults to TRUE.
...: System required, not used here.

Details

If the input is a coglasso object, it is necessary to specify all the indexes to extract the chosen network.
If the input is a select_coglasso object, it extracts by default the selected network. If the selection method was "ebic", and you want to extract a different network than the selected one, specify all indexes. Otherwise, if the objective is to extract the optimal network for a specific \(c\) value different than the selected one, set index_c to your chosen one. Also here it is possible to extract a specific non-optimal network by setting all the indexes to the chosen ones.

Examples

Run this code


# \donttest{
sel_cg <- bs(multi_omics_sd_small, p = c(14, 5), nlambda_w = 15, nlambda_b = 15,
             nc = 3, lambda_w_min_ratio = 0.6, verbose = FALSE)
plot(sel_cg)
# }