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An R6 Class to represent a collection of ZIPLNnetwork

Source: R/PLNnetworkfamily-class.R
ZIPLNnetworkfamily.Rd

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

PLNfamily -> Networkfamily -> ZIPLNnetworkfamily

Public fields

covariates0

the matrix of covariates included in the ZI component

Methods

Public methods

Inherited methods

ZIPLNnetworkfamily$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

ZIPLNnetworkfamily$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.

ZIPLNnetworkfamily$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 = 0.5246191 
	sparsifying penalty = 0.4845754 
	sparsifying penalty = 0.4475881 
	sparsifying penalty = 0.4134241 
	sparsifying penalty = 0.3818678 
	sparsifying penalty = 0.3527202 
	sparsifying penalty = 0.3257973 
	sparsifying penalty = 0.3009295 
	sparsifying penalty = 0.2779598 
	sparsifying penalty = 0.2567434 
	sparsifying penalty = 0.2371464 
	sparsifying penalty = 0.2190452 
	sparsifying penalty = 0.2023257 
	sparsifying penalty = 0.1868823 
	sparsifying penalty = 0.1726178 
	sparsifying penalty = 0.159442 
	sparsifying penalty = 0.1472719 
	sparsifying penalty = 0.1360308 
	sparsifying penalty = 0.1256477 
	sparsifying penalty = 0.1160571 
	sparsifying penalty = 0.1071986 
	sparsifying penalty = 0.09901618 
	sparsifying penalty = 0.09145836 
	sparsifying penalty = 0.08447742 
	sparsifying penalty = 0.07802933 
	sparsifying penalty = 0.07207342 
	sparsifying penalty = 0.06657212 
	sparsifying penalty = 0.06149072 
	sparsifying penalty = 0.05679719 
	sparsifying penalty = 0.05246191 

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