predicted.missSBM_fit

predict.missSBM_fit

Prediction of a <code>missSBM_fit</code> (i.e. network with imputed missing dyads)

When a network is partially observed (here, NAs in the adjacency matrix rather than 1 or 0
due to missing information between node pairs), it is possible to account for the underlying process
that generates those NAs. 'missSBM', presented in 'Barbillon, Chiquet and Tabouy' (2022) <doi:10.18637/jss.v101.i12>,
adjusts the popular stochastic block model from network data sampled under various missing data conditions,
as described in 'Tabouy, Barbillon and Chiquet' (2019) <doi:10.1080/01621459.2018.1562934>.

Julien Chiquet

missSBM

Handling Missing Data in Stochastic Block Models

Pierre Barbillon

Timothée Tabouy

Jean-Benoist Léger

François Gindraud

großBM team 

predicted.missSBM_fit function

<dl><dt>object</dt>
<dd>an R6 object with class <code>missSBM_fit</code></dd>
<dt>...</dt>
<dd>additional parameters for S3 compatibility.</dd></dl>

Arguments

Prediction of a missSBM_fit (i.e. network with imputed missing dyads) — predicted.missSBM_fit

<dl>

<dt>object</dt>
<dd>an R6 object with class <code>missSBM_fit</code></dd>


<dt>...</dt>
<dd>additional parameters for S3 compatibility.</dd>

</dl>

predicted.missSBM_fit: Prediction of a `missSBM_fit` (i.e. network with imputed missing dyads)

Description

Usage

Value

Arguments