FitGenMLVSBM: An R6 Class object, a fitted generalized multilevel network...

In Chabert-Liddell/MLVSBM: A Stochastic Block Model for Multilevel Networks

An R6 Class object, a fitted generalized multilevel network once $dovem() is done

Description

An R6 Class object, a fitted generalized multilevel network once $dovem() is done

An R6 Class object, a fitted generalized multilevel network once $dovem() is done

Public fields

vbound

The vector of variational bound for monitoring convergence

Active bindings

affiliation_matrix

Get the affiliation matrix

adjacency_matrix

Get the list of adjacency matrices

nb_nodes

Get the list of the number of nodes

nb_clusters

Get the list of the number of blocks

nb_levels

Get the number of levels

block_proportions

Get the block proportions of each level

parameters

Get the list of the model parameters

membership

Get the list of the variational parameters

independent

Are the levels independent?

distribution

Emission distribution of each level

directed

Are the levels directed?

entropy

Get the entropy of the model

bound

Get the variational bound of the model

df_mixture

Get the degrees of freedom of the mixture parameters

df_connect

Get the degrees of freedom of the connection parameters

connect

Get the number of possible observed connections

ICL

Get the ICL model selection criterion of the model

full_penalty

Get the penalty used to compute the ICL

Z

Get the list of block memberships (vector form)

X_hat

Get the list of the matrices of probability connection predictions

map

Get the list of block memberships (matrix form)

penalty

Get the ICL penalty

likelihood

Compute the likelihood of both levels

complete_likelihood

Get the complete likelihood of the model

Methods

Public methods

• FitGenMLVSBM$new()

• FitGenMLVSBM$update_alpha()

• FitGenMLVSBM$update_pi()

• FitGenMLVSBM$update_gamma()

• FitGenMLVSBM$init_clustering()

• FitGenMLVSBM$clear()

• FitGenMLVSBM$m_step()

• FitGenMLVSBM$ve_step()

• FitGenMLVSBM$ve_step2()

• FitGenMLVSBM$update_mqr()

• FitGenMLVSBM$do_vem()

• FitGenMLVSBM$permute_empty_class()

• FitGenMLVSBM$xz_loglikelihood()

• FitGenMLVSBM$za_loglikelihood()

• FitGenMLVSBM$reorder()

• FitGenMLVSBM$plot()

• FitGenMLVSBM$show()

• FitGenMLVSBM$print()

• FitGenMLVSBM$clone()

---

Method new()

Constructor for the FitMLVSBM class

Usage

FitGenMLVSBM$new(
  Q = NULL,
  A = NULL,
  X = NULL,
  M = NULL,
  directed = NULL,
  distribution = NULL,
  independent = FALSE,
  no_affiliation = NULL
)

Arguments

Q

Vector with the number of blocks

A

List of affiliation matrice

X

List of adjacency matrices

M

List of Mask matrices

directed

Vector of boolean

distribution

Vector of string

independent

Boolean

no_affiliation

A vector of boolean. For each level, are there any nodes with no affiliations?

Returns

A FitGenMLVSBM object

---

Method update_alpha()

Update the connection parameters for the M step

Usage

FitGenMLVSBM$update_alpha(m, safeguard = 2 * .Machine$double.eps)

Arguments

safeguard

Parameter live in a compact [safeguard, 1-safeguard]

---

Method update_pi()

Update the mixture parameter for the M step of level m

Usage

FitGenMLVSBM$update_pi(m, safeguard = 0.001)

Arguments

safeguard

Parameter live in a compact [safeguard, 1-safeguard]

---

Method update_gamma()

Update the hierarchical mixture parameter for the M step

Usage

FitGenMLVSBM$update_gamma(m, safeguard = 1e-06)

Arguments

safeguard

Parameter live in a compact [safeguard, 1-safeguard]

---

Method init_clustering()

init_clustering Initial clustering for VEM algorithm

Usage

FitGenMLVSBM$init_clustering(
  safeguard = 2 * .Machine$double.eps,
  method = "hierarchical",
  Z = NULL
)

Arguments

safeguard

Parameter live in a compact [safeguard, 1-safeguard]

method

Algorithm used to initiate the clustering, either "spectral", "hierarchical" or "merge_split" (if Z is provided)

Z

Initial clustering if provided

---

Method clear()

Reset all parameters

Usage

FitGenMLVSBM$clear()

---

Method m_step()

m_step Compute the M step of the VEM algorithm

Usage

FitGenMLVSBM$m_step(m, safeguard = 1e-06)

Arguments

safeguard

Parameter live in a compact [safeguard, 1-safeguard]

---

Method ve_step()

Compute the VE step of the VEM algorithm

Usage

FitGenMLVSBM$ve_step(m, threshold = 1e-06, fixPointIter = 3, safeguard = 1e-06)

Arguments

m

The level to be updated

threshold

The convergence threshold

fixPointIter

The maximum number of fixed point iterations

safeguard

Parameter live in a compact [safeguard, 1-safeguard]

---

Method ve_step2()

Compute the VE step of the VEM algorithm

Usage

FitGenMLVSBM$ve_step2(threshold = 1e-06, fixPointIter = 5, safeguard = 1e-06)

Arguments

threshold

The convergence threshold

fixPointIter

The maximum number of fixed point iterations

safeguard

Parameter live in a compact [safeguard, 1-safeguard]

---

Method update_mqr()

Usage

FitGenMLVSBM$update_mqr(m)

---

Method do_vem()

Launch a Variational EM algorithm

Usage

FitGenMLVSBM$do_vem(
  init = "hierarchical",
  threshold = 1e-06,
  maxIter = 1000,
  fixPointIter = 10,
  safeguard = 1e-06,
  Z = NULL
)

Arguments

init

The method for self$init_clustering

threshold

The convergence threshold

maxIter

The max number of VEM iterations

fixPointIter

The max number of fixed point iterations for VE step

safeguard

Parameter live in a compact [safeguard, 1-safeguard]

Z

Initial clustering if provided

---

Method permute_empty_class()

permute_empty_class Put empty blocks numbers at the end

Usage

FitGenMLVSBM$permute_empty_class(m)

---

Method xz_loglikelihood()

Usage

FitGenMLVSBM$xz_loglikelihood(m)

---

Method za_loglikelihood()

Usage

FitGenMLVSBM$za_loglikelihood(m)

---

Method reorder()

Reorder the block memberships and parameters of the networks

Usage

FitGenMLVSBM$reorder(order = "affiliation")

Arguments

order

One of c("affiliation", "degree")

---

Method plot()

Plot of FitMLVSBM objects

Usage

FitGenMLVSBM$plot(type = c("matrix"), ...)

Arguments

type

A string for the type of plot, just "matrix" for now

Returns

a ggplot2 object

---

Method show()

print method

Usage

FitGenMLVSBM$show(type = "Multilevel Stochastic Block Model")

Arguments

type

character to tune the displayed name

---

Method print()

print method

Usage

FitGenMLVSBM$print()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

FitGenMLVSBM$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Chabert-Liddell/MLVSBM documentation built on March 25, 2023, 11:45 a.m.