An R6 Class to represent a collection of ZIPLNnetwork

The function ZIPLNnetwork() produces an instance of this class.

This class comes with a set of methods, some of them being useful for the user: See the documentation for getBestModel(), getModel() and plot()

See also

The function ZIPLNnetwork(), the class ZIPLNfit_sparse

Super classes

PLNmodels::PLNfamily -> PLNmodels::Networkfamily -> ZIPLNnetworkfamily

Public fields

covariates0

the matrix of covariates included in the ZI component

Methods

Public methods

• ZIPLNnetworkfamily$new()

• ZIPLNnetworkfamily$stability_selection()

• ZIPLNnetworkfamily$clone()

Inherited methods

• PLNmodels::PLNfamily$getModel()
• PLNmodels::PLNfamily$postTreatment()
• PLNmodels::PLNfamily$print()
• PLNmodels::Networkfamily$coefficient_path()
• PLNmodels::Networkfamily$getBestModel()
• PLNmodels::Networkfamily$optimize()
• PLNmodels::Networkfamily$plot()
• PLNmodels::Networkfamily$plot_objective()
• PLNmodels::Networkfamily$plot_stars()
• PLNmodels::Networkfamily$show()

---

Method new()

Initialize all models in the collection

Usage

ZIPLNnetworkfamily$new(penalties, data, control)

Arguments

penalties

a vector of positive real number controlling the level of sparsity of the underlying network.

data

a named list used internally to carry the data matrices

control

a list for controlling the optimization.

Returns

Update current PLNnetworkfit with smart starting values

---

Method stability_selection()

Compute the stability path by stability selection

Usage

ZIPLNnetworkfamily$stability_selection(
  subsamples = NULL,
  control = ZIPLNnetwork_param()
)

Arguments

subsamples

a list of vectors describing the subsamples. The number of vectors (or list length) determines the 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(). See ZIPLNnetwork() and ZIPLN_param() for details.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ZIPLNnetworkfamily$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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 = 7.264447 
	sparsifying penalty = 6.709958 
	sparsifying penalty = 6.197792 
	sparsifying penalty = 5.72472 
	sparsifying penalty = 5.287757 
	sparsifying penalty = 4.884147 
	sparsifying penalty = 4.511344 
	sparsifying penalty = 4.166997 
	sparsifying penalty = 3.848934 
	sparsifying penalty = 3.555148 
	sparsifying penalty = 3.283787 
	sparsifying penalty = 3.033138 
	sparsifying penalty = 2.801621 
	sparsifying penalty = 2.587776 
	sparsifying penalty = 2.390253 
	sparsifying penalty = 2.207807 
	sparsifying penalty = 2.039287 
	sparsifying penalty = 1.88363 
	sparsifying penalty = 1.739854 
	sparsifying penalty = 1.607053 
	sparsifying penalty = 1.484388 
	sparsifying penalty = 1.371086 
	sparsifying penalty = 1.266432 
	sparsifying penalty = 1.169766 
	sparsifying penalty = 1.080479 
	sparsifying penalty = 0.998007 
	sparsifying penalty = 0.9218299 
	sparsifying penalty = 0.8514675 
	sparsifying penalty = 0.7864757 
	sparsifying penalty = 0.7264447 

#>  Post-treatments
#>  DONE!
class(fits)
#> [1] "PLNnetworkfamily" "Networkfamily"    "PLNfamily"        "R6"