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PLNmodels (version 1.3.2)

PLNnetworkfit: An R6 Class to represent a PLNfit in a sparse inverse covariance framework

Description

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() and methods inherited from PLNfit.

Arguments

Super classes

PLNfit -> PLNfit_fixedcov -> PLNnetworkfit

Active bindings

vcov_model

character: the model used for the residual covariance

penalty

the global level of sparsity in the current model

penalty_weights

a matrix of weights controlling the amount of penalty element-wise.

n_edges

number of edges if the network (non null coefficient of the sparse precision matrix)

nb_param

number of parameters in the current PLN model

pen_loglik

variational lower bound of the l1-penalized loglikelihood

ebic_gamma

the tuning parameter gamma of the EBIC, between 0 and 1. Zero gives back the BIC; the default 0.5 is the value recommended by Foygel and Drton (2010). Assign to it to change the EBIC of this fit.

EBIC

variational lower bound of the extended BIC of Foygel and Drton (2010), that is the BIC with the additional penalty 2 gamma |E| log(p) on the edge set. Their criterion was designed for the graphical Lasso; up to and including version 1.3.2, PLNmodels used instead the approximation gamma |E| log(p (p - 1) / (2 |E|)) of the original EBIC of Chen and Chen (2008).

density

proportion of non-null edges in the network

criteria

a vector with loglik, penalized loglik, BIC, EBIC, ICL, R_squared, number of parameters, number of edges and graph density

Methods

Inherited methods

PLNnetworkfit$new()

Initialize a PLNnetworkfit object

Usage

PLNnetworkfit$new(data, control)

Arguments

data

a named list used internally to carry the data matrices

control

a list for controlling the optimization.

PLNnetworkfit$optimize()

Call to the C++ optimizer and update of the relevant fields

Usage

PLNnetworkfit$optimize(data, config)

Arguments

data

a named list used internally to carry the data matrices

config

a list for controlling the optimization

PLNnetworkfit$latent_network()

Extract interaction network in the latent space

Usage

PLNnetworkfit$latent_network(type = c("partial_cor", "support", "precision"))

Arguments

type

edge value in the network. Can be "support" (binary edges), "precision" (coefficient of the precision matrix) or "partial_cor" (partial correlation between species)

Returns

a square matrix of size PLNnetworkfit$n

PLNnetworkfit$plot_network()

plot the latent network.

Usage

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,
  edge.alpha = 0.2,
  plot = TRUE
)

Arguments

type

edge value in the network. Either "precision" (coefficient of the precision matrix) or "partial_cor" (partial correlation between species).

output

Output type. Either igraph (for the network) or corrplot (for the adjacency matrix)

edge.color

Length 2 color vector. Color for positive/negative edges. Default is c("#F8766D", "#00BFC4"). Only relevant for igraph output.

remove.isolated

if TRUE, isolated node are remove before plotting. Only relevant for igraph output.

node.labels

vector of character. The labels of the nodes. The default will use the column names ot the response matrix.

layout

an optional igraph layout. Only relevant for igraph output.

edge.alpha

opacity of the weakest edge, the strongest one being fully opaque, so that the strength of an edge can be read off a dense network. Default is 0.2. Set it to 1 for uniformly opaque edges. Only relevant for igraph output with type = "partial_cor".

plot

logical. Should the final network be displayed or only sent back to the user. Default is TRUE.

PLNnetworkfit$show()

User friendly print method

Usage

PLNnetworkfit$show()

PLNnetworkfit$clone()

The objects of this class are cloneable with this method.

Usage

PLNnetworkfit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

The function PLNnetwork(), the class PLNnetworkfamily

Examples

Run this code
if (FALSE) {
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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