PLNnetworkfamily {PLNmodels} | R Documentation |
An R6 Class to represent a collection of PLNnetworkfit
s
Description
The function PLNnetwork()
produces an instance of this class.
This class comes with a set of methods mostly used to compare
network fits (in terms of goodness of fit) or extract one from
the family (based on penalty parameter and/or goodness of it).
See the documentation for getBestModel()
,
getModel()
and plot() for the user-facing ones.
Super classes
PLNmodels::PLNfamily
-> PLNmodels::Networkfamily
-> PLNnetworkfamily
Methods
Public methods
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
PLNnetworkfamily$new(penalties, data, control)
Arguments
penalties
a vector of positive real number controlling the level of sparsity of the underlying network.
data
a named list used internally to carry the data matrices
control
a list for controlling the optimization.
Returns
Update current PLNnetworkfit
with smart starting values
Method stability_selection()
Compute the stability path by stability selection
Usage
PLNnetworkfamily$stability_selection( subsamples = NULL, control = PLNnetwork_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)
ifn >= 144
and0.8*n
otherwise following Liu et al. (2010) recommendations.control
a list controlling the main optimization process in each call to
PLNnetwork()
. SeePLNnetwork()
andPLN_param()
for details.
Method clone()
The objects of this class are cloneable with this method.
Usage
PLNnetworkfamily$clone(deep = FALSE)
Arguments
deep
Whether to make a deep clone.
See Also
The function PLNnetwork()
, the class PLNnetworkfit
Examples
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)
class(fits)