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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 = 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!
class(fits)
#> [1] "PLNnetworkfamily" "Networkfamily"    "PLNfamily"        "R6"