ZIPLNnetworkfamily | R Documentation |

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()

`PLNmodels::PLNfamily`

-> `PLNmodels::Networkfamily`

-> `ZIPLNnetworkfamily`

`covariates0`

the matrix of covariates included in the ZI component

`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()`

`new()`

Initialize all models in the collection

ZIPLNnetworkfamily$new(penalties, data, control)

`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.

Update current `PLNnetworkfit`

with smart starting values

`stability_selection()`

Compute the stability path by stability selection

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

`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.

`clone()`

The objects of this class are cloneable with this method.

ZIPLNnetworkfamily$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

The function `ZIPLNnetwork()`

, the class `ZIPLNfit_sparse`

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

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