Internal class for a binary SBM fit under MAR sampling designs with covariates.

It is not designed to be called directly by the user.

Super classes

sbm::SBM -> sbm::SimpleSBM -> SimpleSBM_fit -> SimpleSBM_fit_withCov

Active bindings

imputation

the matrix of imputed values

vExpec

double: variational approximation of the expectation complete log-likelihood

vExpec_corrected

double: variational approximation of the expectation complete log-likelihood with correction to be comparable with MNAR criteria

Methods

Public methods

• SimpleSBM_fit_withCov$update_parameters()

• SimpleSBM_fit_withCov$update_blocks()

• SimpleSBM_fit_withCov$clone()

Inherited methods

• sbm::SBM$print()
• sbm::SBM$rNetwork()
• sbm::SimpleSBM$plot()
• sbm::SimpleSBM$predict()
• sbm::SimpleSBM$rEdges()
• sbm::SimpleSBM$rMemberships()
• sbm::SimpleSBM$show()
• SimpleSBM_fit$doVEM()
• SimpleSBM_fit$get_state()
• SimpleSBM_fit$initialize()
• SimpleSBM_fit$reorder()
• SimpleSBM_fit$set_state()

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SimpleSBM_fit_withCov$update_parameters()

update parameters estimation (M-step) via Newton-Raphson: the M-step objective is a weighted logistic regression (concave), so Newton converges in a handful of iterations – no external optimizer is required.

Usage

SimpleSBM_fit_withCov$update_parameters(...)

Arguments

...

use for compatibility

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SimpleSBM_fit_withCov$update_blocks()

update variational estimation of blocks (VE-step)

Usage

SimpleSBM_fit_withCov$update_blocks(...)

Arguments

...

use for compatibility

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SimpleSBM_fit_withCov$clone()

The objects of this class are cloneable with this method.

Usage

SimpleSBM_fit_withCov$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.