Fit the multivariate Poisson lognormal model with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets, weights).

The Poisson-lognormal model and variants (Chiquet,
Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be
used for a variety of multivariate problems when count data are at
play, including principal component analysis for count data,
discriminant analysis, model-based clustering and network inference.
Implements variational algorithms to fit such models accompanied with
a set of functions for visualization and diagnostic.

Julien Chiquet

PLNmodels

Poisson Lognormal Models

Mahendra Mariadassou

Stéphane Robin

François Gindraud

Julie Aubert

Bastien Batardière

Giovanni Poggiato

Cole Trapnell

Maddy Duran

PLN function

<dl><dt>formula</dt>
<dd>an object of class "formula": a symbolic description of the model to be fitted.</dd>
<dt>data</dt>
<dd>an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called.</dd>
<dt>subset</dt>
<dd>an optional vector specifying a subset of observations to be used in the fitting process.</dd>
<dt>weights</dt>
<dd>an optional vector of observation weights to be used in the fitting process.</dd>
<dt>control</dt>
<dd>a list-like structure for controlling the optimization, with default generated by <code>PLN_param()</code>. See the associated documentation
for details.</dd></dl>

Arguments

Poisson lognormal model — PLN

<dl>

<dt>formula</dt>
<dd>an object of class "formula": a symbolic description of the model to be fitted.</dd>


<dt>data</dt>
<dd>an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which PLN is called.</dd>


<dt>subset</dt>
<dd>an optional vector specifying a subset of observations to be used in the fitting process.</dd>


<dt>weights</dt>
<dd>an optional vector of observation weights to be used in the fitting process.</dd>


<dt>control</dt>
<dd>a list-like structure for controlling the optimization, with default generated by <code>PLN_param()</code>. See the associated documentation
for details.</dd>

</dl>

PLN: Poisson lognormal model

Description

Usage

Value

Arguments

See Also

Examples