set.family

Check if the model family is allowed and return it eventually with a
different family name for compatibility with the <code>C++</code> implementation

internal

Efficient framework to estimate high-dimensional generalized matrix factorization models using penalized maximum likelihood under a dispersion exponential family specification. Either deterministic and stochastic methods are implemented for the numerical maximization. In particular, the package implements the stochastic gradient descent algorithm with a block-wise mini-batch strategy to speed up the computations and an efficient adaptive learning rate schedule to stabilize the convergence. All the theoretical details can be found in Castiglione et al. (2024, <doi:10.48550/arXiv.2412.20509>). Other methods considered for the optimization are the alternated iterative re-weighted least squares and the quasi-Newton method with diagonal approximation of the Fisher information matrix discussed in Kidzinski et al. (2022, <http://jmlr.org/papers/v23/20-1104.html>).

Cristian Castiglione

sgdGMF

Estimation of Generalized Matrix Factorization Models via
Stochastic Gradient Descent

Davide Risso

Alexandre Segers

set.family function

<dl><dt>family</dt>
<dd>a <code>glm</code> family (see <code><a href="/link/family?package=sgdGMF&version=1.0" data-mini-rdoc="sgdGMF::family">family</a></code> for more details)</dd></dl>

Arguments

Check and set the model family — set.family

<dl>

<dt>family</dt>
<dd>a <code>glm</code> family (see <code><a href='https://rdrr.io/r/stats/family.html'>family</a></code> for more details)</dd>

</dl>

set.family: Check and set the model family

Description

Usage

Arguments