networkSamplingDyads_fit: Virtual class used to define a family of...

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

Virtual class used to define a family of networkSamplingDyads_fit

Description

Virtual class used to define a family of networkSamplingDyads_fit

Virtual class used to define a family of networkSamplingDyads_fit

Super class

missSBM::networkSampling -> networkSamplingDyads_fit

Active bindings

penalty

double, value of the penalty term in ICL

log_lambda

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

Methods

Public methods

• networkSamplingDyads_fit$new()

• networkSamplingDyads_fit$show()

• networkSamplingDyads_fit$update_parameters()

• networkSamplingDyads_fit$update_imputation()

• networkSamplingDyads_fit$clone()

Inherited methods

• missSBM::networkSampling$print()

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Method new()

constructor for networkSampling_fit

Usage

networkSamplingDyads_fit$new(partlyObservedNetwork, name)

Arguments

partlyObservedNetwork

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

name

a character for the name of sampling to fit on the partlyObservedNetwork

---

Method show()

show method

Usage

networkSamplingDyads_fit$show()

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Method update_parameters()

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

Usage

networkSamplingDyads_fit$update_parameters(...)

Arguments

...

use for compatibility

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Method update_imputation()

a method to update the imputation of the missing entries.

Usage

networkSamplingDyads_fit$update_imputation(nu)

Arguments

nu

the matrix of (uncorrected) imputation for missing entries

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Method clone()

The objects of this class are cloneable with this method.

Usage

networkSamplingDyads_fit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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