PLNnetworkfamily | R Documentation |

The function `PLNnetwork()`

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`

-> `PLNnetworkfamily`

`penalties`

the sparsity level of the network in the successively fitted models

`stability_path`

the stability path of each edge as returned by the stars procedure

`stability`

mean edge stability along the penalty path

`criteria`

a data frame with the values of some criteria (approximated log-likelihood, (E)BIC, ICL and R2, stability) for the collection of models / fits BIC, ICL and EBIC are defined so that they are on the same scale as the model log-likelihood, i.e. with the form, loglik - 0.5 penalty

`new()`

Initialize all models in the collection

PLNnetworkfamily$new( penalties, responses, covariates, offsets, weights, formula, control )

`penalties`

a vector of positive real number controlling the level of sparsity of the underlying network.

`responses`

the matrix of responses common to every models

`covariates`

the matrix of covariates common to every models

`offsets`

the matrix of offsets common to every models

`weights`

the vector of observation weights

`formula`

model formula used for fitting, extracted from the formula in the upper-level call

`control`

a list for controlling the optimization.

Update current `PLNnetworkfit`

with smart starting values

`optimize()`

Call to the C++ optimizer on all models of the collection

PLNnetworkfamily$optimize(config)

`config`

a list for controlling the optimization.

`stability_selection()`

Compute the stability path by stability selection

PLNnetworkfamily$stability_selection( subsamples = NULL, control = PLNnetwork_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

`PLNnetwork()`

for details.

`coefficient_path()`

Extract the regularization path of a `PLNnetworkfamily`

PLNnetworkfamily$coefficient_path(precision = TRUE, corr = TRUE)

`precision`

Logical. Should the regularization path be extracted from the precision matrix Omega (

`TRUE`

, default) or from the variance matrix Sigma (`FALSE`

)`corr`

Logical. Should the matrix be transformed to (partial) correlation matrix before extraction? Defaults to

`TRUE`

`getBestModel()`

Extract the best network in the family according to some criteria

PLNnetworkfamily$getBestModel( crit = c("BIC", "EBIC", "StARS"), stability = 0.9 )

`crit`

character. Criterion used to perform the selection. Is "StARS" is chosen but

`$stability`

field is empty, will compute stability path.`stability`

Only used for "StARS" criterion. A scalar indicating the target stability (= 1 - 2 beta) at which the network is selected. Default is

`0.9`

.

`plot()`

Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of PLNnetwork fits (a `PLNnetworkfamily`

)

PLNnetworkfamily$plot( criteria = c("loglik", "pen_loglik", "BIC", "EBIC"), reverse = FALSE, log.x = TRUE )

`criteria`

vector of characters. The criteria to plot in

`c("loglik", "pen_loglik", "BIC", "EBIC")`

. Defaults to all of them.`reverse`

A logical indicating whether to plot the value of the criteria in the "natural" direction (loglik - 0.5 penalty) or in the "reverse" direction (-2 loglik + penalty). Default to FALSE, i.e use the natural direction, on the same scale as the log-likelihood..

`log.x`

logical: should the x-axis be represented in log-scale? Default is

`TRUE`

.

a `ggplot`

graph

`plot_stars()`

Plot stability path

PLNnetworkfamily$plot_stars(stability = 0.9, log.x = TRUE)

`stability`

scalar: the targeted level of stability in stability plot. Default is

`0.9`

.`log.x`

logical: should the x-axis be represented in log-scale? Default is

`TRUE`

.

a `ggplot`

graph

`plot_objective()`

Plot objective value of the optimization problem along the penalty path

PLNnetworkfamily$plot_objective()

a `ggplot`

graph

`show()`

User friendly print method

PLNnetworkfamily$show()

`clone()`

The objects of this class are cloneable with this method.

PLNnetworkfamily$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

The function `PLNnetwork()`

, the class `PLNnetworkfit`

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

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