R/PLNnetworkfamily-class.R
PLNnetworkfamily.RdThe 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()
The function PLNnetwork(), the class PLNnetworkfit
PLNmodels::PLNfamily -> PLNnetworkfamily
penaltiesthe sparsity level of the network in the successively fitted models
stability_paththe stability path of each edge as returned by the stars procedure
stabilitymean edge stability along the penalty path
criteriaa 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$new(
penalties,
responses,
covariates,
offsets,
weights,
formula,
control
)penaltiesa vector of positive real number controlling the level of sparsity of the underlying network.
responsesthe matrix of responses common to every models
covariatesthe matrix of covariates common to every models
offsetsthe matrix of offsets common to every models
weightsthe vector of observation weights
formulamodel formula used for fitting, extracted from the formula in the upper-level call
controla list for controlling the optimization.
Update current PLNnetworkfit with smart starting values
optimize()Call to the C++ optimizer on all models of the collection
stability_selection()Compute the stability path by stability selection
PLNnetworkfamily$stability_selection(
subsamples = NULL,
control = PLNnetwork_param()
)subsamplesa 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.
controla list controlling the main optimization process in each call to PLNnetwork. See PLNnetwork() for details.
coefficient_path()Extract the regularization path of a PLNnetworkfamily
getBestModel()Extract the best network in the family according to some criteria
PLNnetworkfamily$getBestModel(
crit = c("BIC", "EBIC", "StARS"),
stability = 0.9
)plot()Display various outputs (goodness-of-fit criteria, robustness, diagnostic) associated with a collection of PLNnetwork fits (a PLNnetworkfamily)
PLNnetworkfamily$plot(
criteria = c("loglik", "pen_loglik", "BIC", "EBIC"),
reverse = FALSE,
log.x = TRUE
)criteriavector of characters. The criteria to plot in c("loglik", "pen_loglik", "BIC", "EBIC"). Defaults to all of them.
reverseA 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.xlogical: should the x-axis be represented in log-scale? Default is TRUE.
plot_stars()Plot stability path
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 = 7.541317
sparsifying penalty = 6.965695
sparsifying penalty = 6.43401
sparsifying penalty = 5.942907
sparsifying penalty = 5.48929
sparsifying penalty = 5.070297
sparsifying penalty = 4.683286
sparsifying penalty = 4.325815
sparsifying penalty = 3.995629
sparsifying penalty = 3.690646
sparsifying penalty = 3.408942
sparsifying penalty = 3.148741
sparsifying penalty = 2.9084
sparsifying penalty = 2.686404
sparsifying penalty = 2.481353
sparsifying penalty = 2.291954
sparsifying penalty = 2.117011
sparsifying penalty = 1.955421
sparsifying penalty = 1.806166
sparsifying penalty = 1.668303
sparsifying penalty = 1.540962
sparsifying penalty = 1.423342
sparsifying penalty = 1.3147
sparsifying penalty = 1.21435
sparsifying penalty = 1.121659
sparsifying penalty = 1.036044
sparsifying penalty = 0.9569638
sparsifying penalty = 0.8839195
sparsifying penalty = 0.8164507
sparsifying penalty = 0.7541317
#> Post-treatments
#> DONE!
class(fits)
#> [1] "PLNnetworkfamily" "PLNfamily" "R6"