madgrad

stochastic gradient descent optimizer

A scalable and fast method for estimating joint Species Distribution Models (jSDMs) for big community data, including eDNA data. The package estimates a full (i.e. non-latent) jSDM with different response distributions (including the traditional multivariate probit model). The package allows to perform variation partitioning (VP) / ANOVA on the fitted models to separate the contribution of environmental, spatial, and biotic associations. In addition, the total R-squared can be further partitioned per species and site to reveal the internal metacommunity structure, see Leibold et al., <doi:10.1111/oik.08618>. The internal structure can then be regressed against environmental and spatial distinctiveness, richness, and traits to analyze metacommunity assembly processes. The package includes support for accounting for spatial autocorrelation and the option to fit responses using deep neural networks instead of a standard linear predictor. As described in Pichler & Hartig (2021) <doi:10.1111/2041-210X.13687>, scalability is achieved by using a Monte Carlo approximation of the joint likelihood implemented via 'PyTorch' and 'reticulate', which can be run on CPUs or GPUs.

Maximilian Pichler

sjSDM

Scalable Joint Species Distribution Modeling

Florian Hartig

Wang Cai

madgrad function

<dl><dt>momentum</dt>
<dd>strength of momentum</dd>
<dt>weight_decay</dt>
<dd>l2 penalty on weights</dd>
<dt>eps</dt>
<dd>epsilon</dd></dl>

Arguments

madgrad — madgrad

<dl>

<dt>momentum</dt>
<dd>strength of momentum</dd>


<dt>weight_decay</dt>
<dd>l2 penalty on weights</dd>


<dt>eps</dt>
<dd>epsilon</dd>

</dl>

madgrad: madgrad

Description

Usage

Value

Arguments

References