blockNodeSampling_fit: Class for fitting a block-node sampling

In jchiquet/missSBM: Handling Missing Data in Stochastic Block Models

Class for fitting a block-node sampling

Description

Class for fitting a block-node sampling

Class for fitting a block-node sampling

Super classes

missSBM::networkSampling -> missSBM::networkSamplingNodes_fit -> blockNodeSampling_fit

Active bindings

vExpec

variational expectation of the sampling

log_lambda

double, term for adjusting the imputation step which depends on the type of sampling

Methods

Public methods

• blockNodeSampling_fit$new()

• blockNodeSampling_fit$update_parameters()

• blockNodeSampling_fit$clone()

Inherited methods

• missSBM::networkSampling$print()
• missSBM::networkSamplingNodes_fit$show()
• missSBM::networkSamplingNodes_fit$update_imputation()

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Method new()

constructor

Usage

blockNodeSampling_fit$new(partlyObservedNetwork, blockInit)

Arguments

partlyObservedNetwork

a object with class partlyObservedNetwork representing the observed data with possibly missing entries

blockInit

n x Q matrix of initial block indicators

---

Method update_parameters()

a method to update the estimation of the parameters. By default, nothing to do (corresponds to MAR sampling)

Usage

blockNodeSampling_fit$update_parameters(imputedNet, Z)

Arguments

imputedNet

an adjacency matrix where missing values have been imputed

Z

indicator of blocks

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Method clone()

The objects of this class are cloneable with this method.

Usage

blockNodeSampling_fit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

jchiquet/missSBM documentation built on Oct. 25, 2023, 5:30 a.m.