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

In PLNmodels: Poisson Lognormal Models

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.

Super classes

PLNmodels::PLNfit -> PLNmodels::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

variational lower bound of the EBIC

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

Public methods

• PLNnetworkfit$new()

• PLNnetworkfit$update()

• PLNnetworkfit$optimize()

• PLNnetworkfit$latent_network()

• PLNnetworkfit$plot_network()

• PLNnetworkfit$show()

• PLNnetworkfit$clone()

Inherited methods

• PLNmodels::PLNfit$optimize_vestep()
• PLNmodels::PLNfit$predict()
• PLNmodels::PLNfit$predict_cond()
• PLNmodels::PLNfit$print()
• PLNmodels::PLNfit_fixedcov$postTreatment()

---

Method new()

Initialize a PLNnetworkfit object

Usage

PLNnetworkfit$new(
  penalty,
  penalty_weights,
  responses,
  covariates,
  offsets,
  weights,
  formula,
  control
)

Arguments

penalty

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

penalty_weights

either a single or a list of p x p matrix of weights (default filled with 1) to adapt the amount of shrinkage to each pairs of node. Must be symmetric with positive values.

responses

the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class

covariates

design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class

offsets

offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class

weights

an optional vector of observation weights to be used in the fitting process.

formula

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

control

a list for controlling the optimization.

---

Method update()

Update fields of a PLNnetworkfit object

Usage

PLNnetworkfit$update(
  penalty = NA,
  B = NA,
  Sigma = NA,
  Omega = NA,
  M = NA,
  S = NA,
  Z = NA,
  A = NA,
  Ji = NA,
  R2 = NA,
  monitoring = NA
)

Arguments

penalty

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

B

matrix of regression matrix

Sigma

variance-covariance matrix of the latent variables

Omega

precision matrix of the latent variables. Inverse of Sigma.

M

matrix of mean vectors for the variational approximation

S

matrix of variance vectors for the variational approximation

Z

matrix of latent vectors (includes covariates and offset effects)

A

matrix of fitted values

Ji

vector of variational lower bounds of the log-likelihoods (one value per sample)

R2

approximate R^2 goodness-of-fit criterion

monitoring

a list with optimization monitoring quantities

---

Method optimize()

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

Usage

PLNnetworkfit$optimize(responses, covariates, offsets, weights, config)

Arguments

responses

the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily-class

covariates

design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily-class

offsets

offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily-class

weights

an optional vector of observation weights to be used in the fitting process.

config

a list for controlling the optimization

---

Method 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

---

Method 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,
  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.

plot

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

---

Method show()

User friendly print method

Usage

PLNnetworkfit$show()

---

Method 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

## Not run: 
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
nets <- PLNnetwork(Abundance ~ 1, data = trichoptera)
myPLNnet <- getBestModel(nets)
class(myPLNnet)
print(myPLNnet)

## End(Not run)

PLNmodels documentation built on Aug. 24, 2023, 5:11 p.m.