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.

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 = 3.665314 
	sparsifying penalty = 3.385544 
	sparsifying penalty = 3.127128 
	sparsifying penalty = 2.888437 
	sparsifying penalty = 2.667965 
	sparsifying penalty = 2.464322 
	sparsifying penalty = 2.276222 
	sparsifying penalty = 2.10248 
	sparsifying penalty = 1.942 
	sparsifying penalty = 1.793768 
	sparsifying penalty = 1.656852 
	sparsifying penalty = 1.530386 
	sparsifying penalty = 1.413573 
	sparsifying penalty = 1.305676 
	sparsifying penalty = 1.206015 
	sparsifying penalty = 1.113961 
	sparsifying penalty = 1.028933 
	sparsifying penalty = 0.9503954 
	sparsifying penalty = 0.8778525 
	sparsifying penalty = 0.8108468 
	sparsifying penalty = 0.7489555 
	sparsifying penalty = 0.6917884 
	sparsifying penalty = 0.6389848 
	sparsifying penalty = 0.5902116 
	sparsifying penalty = 0.5451613 
	sparsifying penalty = 0.5035496 
	sparsifying penalty = 0.4651141 
	sparsifying penalty = 0.4296123 
	sparsifying penalty = 0.3968204 
	sparsifying penalty = 0.3665314 

#>  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
} # }