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
responsesthe matrix of responses common to every models
covariatesthe matrix of covariates common to every models
offsetsthe matrix of offsets common to every models
weightsthe vector of observation weights
modelsa list of PLNnetworkfit object, one per penalty.
inceptiona PLNfit object, obtained when no sparsifying penalty is applied.
responsesthe matrix of responses common to every models
covariatesthe matrix of covariates common to every models
offsetsthe matrix of offsets common to every models
weightsthe vector of observation weights
penaltiesthe sparsity level of the network in the successively fitted models
modelsa list of PLNnetworkfit object, one per penalty.
inceptiona PLNfit object, obtained when no sparsifying penalty is applied.
criteriaa data frame with the values of some criteria (variational lower bound J, BIC, ICL and R2) for the different models.
new()PLNnetworkfamily$new( penalties, responses, covariates, offsets, weights, model, control )
optimize()PLNnetworkfamily$optimize(control)
stability_selection()PLNnetworkfamily$stability_selection( subsamples = NULL, control = list(), mc.cores = 1 )
coefficient_path()PLNnetworkfamily$coefficient_path(precision = TRUE, corr = TRUE)
getBestModel()PLNnetworkfamily$getBestModel(
crit = c("BIC", "loglik", "R_squared", "EBIC", "StARS"),
stability = 0.9
)plot()PLNnetworkfamily$plot(
criteria = c("loglik", "pen_loglik", "BIC", "EBIC"),
log.x = TRUE,
annotate
)plot_stars()PLNnetworkfamily$plot_stars(stability = 0.9, log.x = TRUE)
plot_objective()PLNnetworkfamily$plot_objective()
show()PLNnetworkfamily$show()
clone()The objects of this class are cloneable with this method.
PLNnetworkfamily$clone(deep = FALSE)
deepWhether to make a deep clone.
The function PLNnetwork, the class PLNnetworkfit
# NOT RUN {
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
fits <- PLNnetwork(Abundance ~ 1, data = trichoptera)
class(fits)
# }
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