Extract edge selection frequency in bootstrap subsamples

Extracts edge selection frequency in networks reconstructed from bootstrap subsamples during the stars stability selection procedure, as either a matrix or a named vector. In the latter case, edge names follow igraph naming convention.

Usage

extract_probs(
  Robject,
  penalty = NULL,
  index = NULL,
  crit = c("StARS", "BIC", "EBIC"),
  format = c("matrix", "vector"),
  tol = 1e-05
)

Arguments

Robject

an object with class PLNnetworkfamily, i.e. an output from PLNnetwork()

penalty

penalty used for the bootstrap subsamples

index

Integer index of the model to be returned. Only the first value is taken into account.

crit

a character for the criterion used to performed the selection. Either "BIC", "ICL", "EBIC", "StARS", "R_squared". Default is ICL for PLNPCA, and BIC for PLNnetwork. If StARS (Stability Approach to Regularization Selection) is chosen and stability selection was not yet performed, the function will call the method stability_selection() with default argument.

format

output format. Either a matrix (default) or a named vector.

tol

tolerance for rounding error when comparing penalties.

Value

Either a matrix or named vector of edge-wise probabilities. In the latter case, edge names follow igraph convention.

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
nets <- PLNnetwork(Abundance ~ 1 + offset(log(Offset)), data = trichoptera)
#> 
#>  Initialization...
#>  Adjusting 30 PLN with sparse inverse covariance estimation
#> 	Joint optimization alternating gradient descent and graphical-lasso
#> 	sparsifying penalty = 0.3985143 
	sparsifying penalty = 0.368096 
	sparsifying penalty = 0.3399996 
	sparsifying penalty = 0.3140477 
	sparsifying penalty = 0.2900767 
	sparsifying penalty = 0.2679354 
	sparsifying penalty = 0.2474841 
	sparsifying penalty = 0.2285939 
	sparsifying penalty = 0.2111455 
	sparsifying penalty = 0.1950289 
	sparsifying penalty = 0.1801425 
	sparsifying penalty = 0.1663924 
	sparsifying penalty = 0.1536918 
	sparsifying penalty = 0.1419607 
	sparsifying penalty = 0.1311249 
	sparsifying penalty = 0.1211163 
	sparsifying penalty = 0.1118716 
	sparsifying penalty = 0.1033325 
	sparsifying penalty = 0.09544523 
	sparsifying penalty = 0.08815998 
	sparsifying penalty = 0.0814308 
	sparsifying penalty = 0.07521526 
	sparsifying penalty = 0.06947414 
	sparsifying penalty = 0.06417124 
	sparsifying penalty = 0.0592731 
	sparsifying penalty = 0.05474884 
	sparsifying penalty = 0.05056991 
	sparsifying penalty = 0.04670995 
	sparsifying penalty = 0.04314462 
	sparsifying penalty = 0.03985143 

#>  Post-treatments
#>  DONE!
if (FALSE) { # \dontrun{
stability_selection(nets)
probs <- extract_probs(nets, crit = "StARS", format = "vector")
probs
} # }

if (FALSE) { # \dontrun{
## Add edge attributes to graph using igraph
net_stars <- getBestModel(nets, "StARS")
g <- plot(net_stars, type = "partial_cor", plot=F)
library(igraph)
E(g)$prob <- probs[as_ids(E(g))]
g
} # }