ZIPLNnetworkfamily: An R6 Class to represent a collection of ZIPLNnetwork

In PLN-team/PLNmodels: Poisson Lognormal Models

An R6 Class to represent a collection of ZIPLNnetwork

Description

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()

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.

See Also

The function ZIPLNnetwork(), the class ZIPLNfit_sparse

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)
class(fits)

PLN-team/PLNmodels documentation built on April 15, 2024, 9:01 a.m.