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R6 abstract class for a generic sparse Normal Block model

Source: R/NB-Class.R
NB.Rd

R6 abstract class for a generic sparse Normal Block model

R6 abstract class for a generic sparse Normal Block model

Public fields

data

object of NBData class, with responses and design matrix

Active bindings

inference_method

inference procedure used (heuristic or integrated with EM)

n

number of samples

p

number of responses per sample

d

number of variables (dimensions in X)

d0

number of zi variables (dimensions in X0)

Q

number of blocks

n_edges

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

model_par

a list with the matrices of the model parameters: B (covariates), dm1 (species variance), OmegaQ (groups precision matrix))

nb_param

number of parameters in the model

objective

evolution of the objective function during (V)EM algorithm

loglik

(or its variational lower bound)

deviance

(or its variational lower bound)

BIC

(or its variational lower bound)

entropy

Entropy of the conditional distribution when applicable

ICL

variational lower bound of the ICL

EBIC

variational lower bound of the EBIC

criteria

a vector with loglik, BIC and number of parameters

sparsity

(overall sparsity parameter)

sparsity_weights

(weights associated to each pair of groups)

sparsity_term

(sparsity_term term in log-likelihood due to sparsity)

get_res_covariance

whether the residual covariance is diagonal or spherical

memberships

cluster memberships

clustering

given as the list of elements contained in each cluster

cluster_sizes

given as a vector of cluster sizes

elements_per_cluster

given as the list of elements contained in each cluster

Methods

Public methods

Method new()

Create a new [`NB`] object.

Usage

NB$new(data, Q, sparsity = 0, control = NB_control())

Arguments

data

object of NBData class, with responses and design matrix

Q

number of block/cluster

sparsity

sparsity penalty on the network density

control

structured list of more specific parameters, to generate with NB_control

Returns

A new [`NB`] object

Method update()

Update a [`NB`] object

All possible parameters of the child classes

Usage

NB$update(
  B = NA,
  dm1 = NA,
  C = NA,
  OmegaQ = NA,
  gamma = NA,
  mu = NA,
  kappa = NA,
  alpha = NA,
  M = NA,
  S = NA,
  ll_list = NA
)

Arguments

B

regression matrix

dm1

diagonal vector of inverse variance matrix (variables level)

C

the matrix of groups memberships (posterior probabilities)

OmegaQ

groups inverse variance matrix

gamma

variance of posterior distribution of W

mu

mean for posterior distribution of W

kappa

vector of zero-inflation probabilities

alpha

vector of groups probabilities

M

variational mean for posterior distribution of W

S

variational diagonal of variances for posterior distribution of W

ll_list

list of log-lik (elbo) values

Returns

Update the current [`normal`] object

Method optimize()

calls optimization (EM or heuristic) and updates relevant fields

Usage

NB$optimize(control = list(niter = 100, threshold = 1e-04))

Arguments

control

a list for controlling the optimization proces

Returns

optimizes the model and updates its parameters

Method split()

Create a clone of the current [`NB`] object after splitting cluster `cl` We split the cluster according to the species variances

Usage

NB$split(index, in_place = FALSE)

Arguments

index

index (integer) of the cluster to split

in_place

should the split applied to the object itself, or should a copy be sent? default FALSE (send a copy)

Returns

A new [`NB`] object

Method candidates_split()

generate and select a set of candidate models by splitting the clusters of the current model

Usage

NB$candidates_split()

Method candidates_merge()

generate and select a set of candidate models by merging the clusters of the current model

Usage

NB$candidates_merge()

Method merge()

Create a clone of the current [`NB`] object after merging clusters `cl1` and `cl2`

Usage

NB$merge(indices, in_place = FALSE)

Arguments

indices

indices (couple of integer) of the clusters to merge

in_place

should the split applied to the object itself, or should a copy be sent? default FALSE (send a copy)

Returns

A new [`NB`] object

Method plot_loglik()

plots log-likelihood values during model optimization

Usage

NB$plot_loglik(type = "b", log = "xy", neg = TRUE)

Arguments

type

char for line type (see plot.default)

log

char for logarithmic axes (see plot.default)

neg

boolean plot negative log-likelihood (useful when log="y")

Method latent_network()

Extract interaction network in the latent space

Usage

NB$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 `NB_fixed_blocks_class$Q`

Method plot_network()

plot the latent network.

Usage

NB$plot_network(
  type = c("partial_cor", "support"),
  output = c("igraph", "corrplot"),
  edge.color = c("#F8766D", "#00BFC4"),
  remove.isolated = FALSE,
  node.labels = NULL,
  layout = igraph::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 clone()

The objects of this class are cloneable with this method.

Usage

NB$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.