The function PLNnetwork produces a collection of models which are instances of object with class PLNnetworkfit.
This class comes with a set of methods, some of them being useful for the user:
See the documentation for plot.PLNnetworkfit + methods inherited from PLNfit.
PLNmodels::PLNfit -> PLNnetworkfit
var_para list with two matrices, M and S, which are the estimated parameters in the variational approximation
latenta matrix: values of the latent vector (Z in the model)
optim_para list with parameters useful for monitoring the optimization
loglikvariational lower bound of the loglikelihood
BICvariational lower bound of the BIC
ICLvariational lower bound of the ICL
R_squaredapproximated goodness-of-fit criterion
penaltythe level of sparsity in the current model
model_para list with the matrices associated with the estimated parameters of the pPCA model: Theta (covariates), Sigma (latent covariance) and Theta (latent precision matrix). Note Omega and Sigma are inverse of each other.
var_para list with two matrices, M and S, which are the estimated parameters in the variational approximation
latenta matrix: values of the latent vector (Z in the model)
optim_para list with parameters useful for monitoring the optimization
loglikvariational lower bound of the loglikelihood
pen_loglikvariational lower bound of the l1-penalized loglikelihood
BICvariational lower bound of the BIC
EBICvariational lower bound of the EBIC
ICLvariational lower bound of the ICL
R_squaredapproximated goodness-of-fit criterion
nb_paramnumber of parameters in the current PLN model
densityproportion of non-null edges in the network
criteriaa vector with loglik, BIC, ICL, R_squared and number of parameters
new()PLNnetworkfit$new( penalty, responses, covariates, offsets, weights, model, control )
update()PLNnetworkfit$update( penalty = NA, Theta = NA, Sigma = NA, Omega = NA, M = NA, S = NA, Z = NA, A = NA, Ji = NA, R2 = NA, monitoring = NA )
optimize()PLNnetworkfit$optimize(responses, covariates, offsets, weights, control)
postTreatment()PLNnetworkfit$postTreatment(responses, covariates, offsets, weights, nullModel)
latent_network()PLNnetworkfit$latent_network(type = c("partial_cor", "support", "precision"))plot_network()PLNnetworkfit$plot_network(
type = c("partial_cor", "support"),
output = c("igraph", "corrplot"),
edge.color = c("#F8766D", "#00BFC4"),
remove.isolated = FALSE,
node.labels = NULL,
layout = layout_in_circle,
plot = TRUE
)show()PLNnetworkfit$show()
clone()The objects of this class are cloneable with this method.
PLNnetworkfit$clone(deep = FALSE)
deepWhether to make a deep clone.
The function PLNnetwork, the class PLNnetworkfamily
# NOT RUN {
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
nets <- PLNnetwork(Abundance ~ 1, data = trichoptera)
myPLNnet <- getBestModel(nets)
class(myPLNnet)
print(myPLNnet)
# }
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