doubleStandardSampling_fit: Class for fitting a double-standard sampling

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

Class for fitting a double-standard sampling

Description

Class for fitting a double-standard sampling

Class for fitting a double-standard sampling

Super classes

missSBM::networkSampling -> missSBM::networkSamplingDyads_fit -> doubleStandardSampling_fit

Active bindings

vExpec

variational expectation of the sampling

Methods

Public methods

• doubleStandardSampling_fit$new()

• doubleStandardSampling_fit$update_parameters()

• doubleStandardSampling_fit$update_imputation()

• doubleStandardSampling_fit$clone()

Inherited methods

• missSBM::networkSampling$print()
• missSBM::networkSamplingDyads_fit$show()

---

Method new()

constructor

Usage

doubleStandardSampling_fit$new(partlyObservedNetwork, ...)

Arguments

partlyObservedNetwork

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

...

used for compatibility

---

Method update_parameters()

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

Usage

doubleStandardSampling_fit$update_parameters(nu, ...)

Arguments

nu

an adjacency matrix with imputed values (only)

...

use for compatibility

---

Method update_imputation()

a method to update the imputation of the missing entries.

Usage

doubleStandardSampling_fit$update_imputation(nu)

Arguments

nu

the matrix of (uncorrected) imputation for missing entries

---

Method clone()

The objects of this class are cloneable with this method.

Usage

doubleStandardSampling_fit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

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