networkSampler: Definition of R6 Class 'networkSampling_sampler'

In missSBM: Handling Missing Data in Stochastic Block Models

Definition of R6 Class 'networkSampling_sampler'

Description

This class is use to define a sampling model for a network. Inherits from 'networkSampling'. Owns a rSampling method which takes an adjacency matrix as an input and send back an object with class partlyObservedNetwork.

Super class

networkSampling -> networkSampler

Active bindings

samplingMatrix

a matrix of logical indicating observed entries

Methods

Public methods

• networkSampler$new()

• networkSampler$rSamplingMatrix()

• networkSampler$clone()

Inherited methods

• networkSampling$print()
• networkSampling$show()

---

networkSampler$new()

constructor for networkSampling

Usage

networkSampler$new(type = NA, parameters = NA, nbNodes = NA, directed = FALSE)

Arguments

type

character for the type of sampling. must be in ("dyad", "covar-dyad", "node", "covar-node", "block-node", "block-dyad", "double-standard", "degree")

parameters

the vector of parameters associated to the sampling at play

nbNodes

number of nodes in the network

directed

logical, directed network of not

---

networkSampler$rSamplingMatrix()

a method for drawing a sampling matrix according to the current sampling design

Usage

networkSampler$rSamplingMatrix()

---

networkSampler$clone()

The objects of this class are cloneable with this method.

Usage

networkSampler$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

partlyObservedNetwork

missSBM documentation built on July 22, 2026, 1:07 a.m.