Compute the stability path by stability selection

This function computes the StARS stability criteria over a path of penalties. If a path has already been computed, the functions stops with a message unless force = TRUE has been specified.

Usage

stability_selection(
  Robject,
  subsamples = NULL,
  control = PLNnetwork_param(),
  force = FALSE
)

Arguments

Robject

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

subsamples

a list of vectors describing the subsamples. The number of vectors (or list length) determines th number of subsamples used in the stability selection. Automatically set to 20 subsamples with size 10*sqrt(n) if n >= 144 and 0.8*n otherwise following Liu et al. (2010) recommendations.

control

a list controlling the main optimization process in each call to PLNnetwork() or ZIPLNnetwork(). See PLN_param() or ZIPLN_param() for details.

force

force computation of the stability path, even if a previous one has been detected.

Value

the list of subsamples. The estimated probabilities of selection of the edges are stored in the fields stability_path of the initial Robject with class Networkfamily

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)
#> 
#>  Initialization...
#>  Adjusting 30 PLN with sparse inverse covariance estimation
#> 	Joint optimization alternating gradient descent and graphical-lasso
#> 	sparsifying penalty = 4.479926 
	sparsifying penalty = 4.137977 
	sparsifying penalty = 3.822129 
	sparsifying penalty = 3.530389 
	sparsifying penalty = 3.260917 
	sparsifying penalty = 3.012014 
	sparsifying penalty = 2.78211 
	sparsifying penalty = 2.569754 
	sparsifying penalty = 2.373607 
	sparsifying penalty = 2.192431 
	sparsifying penalty = 2.025085 
	sparsifying penalty = 1.870512 
	sparsifying penalty = 1.727737 
	sparsifying penalty = 1.595861 
	sparsifying penalty = 1.47405 
	sparsifying penalty = 1.361537 
	sparsifying penalty = 1.257612 
	sparsifying penalty = 1.16162 
	sparsifying penalty = 1.072954 
	sparsifying penalty = 0.9910565 
	sparsifying penalty = 0.91541 
	sparsifying penalty = 0.8455375 
	sparsifying penalty = 0.7809984 
	sparsifying penalty = 0.7213854 
	sparsifying penalty = 0.6663227 
	sparsifying penalty = 0.6154629 
	sparsifying penalty = 0.5684851 
	sparsifying penalty = 0.5250931 
	sparsifying penalty = 0.4850132 
	sparsifying penalty = 0.4479926 

#>  Post-treatments
#>  DONE!
if (FALSE) { # \dontrun{
n <- nrow(trichoptera)
subs <- replicate(10, sample.int(n, size = n/2), simplify = FALSE)
stability_selection(nets, subsamples = subs)
} # }