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 = 2.123258 
	sparsifying penalty = 1.961192 
	sparsifying penalty = 1.811496 
	sparsifying penalty = 1.673226 
	sparsifying penalty = 1.54551 
	sparsifying penalty = 1.427542 
	sparsifying penalty = 1.318579 
	sparsifying penalty = 1.217933 
	sparsifying penalty = 1.124969 
	sparsifying penalty = 1.039101 
	sparsifying penalty = 0.9597877 
	sparsifying penalty = 0.886528 
	sparsifying penalty = 0.81886 
	sparsifying penalty = 0.7563572 
	sparsifying penalty = 0.6986251 
	sparsifying penalty = 0.6452996 
	sparsifying penalty = 0.5960444 
	sparsifying penalty = 0.5505489 
	sparsifying penalty = 0.508526 
	sparsifying penalty = 0.4697106 
	sparsifying penalty = 0.433858 
	sparsifying penalty = 0.400742 
	sparsifying penalty = 0.3701537 
	sparsifying penalty = 0.3419002 
	sparsifying penalty = 0.3158032 
	sparsifying penalty = 0.2916982 
	sparsifying penalty = 0.2694332 
	sparsifying penalty = 0.2488676 
	sparsifying penalty = 0.2298717 
	sparsifying penalty = 0.2123258 

#>  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
} # }