getSe

Post hoc calculation of standard errors

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

getSe function

<dl><dt>object</dt>
<dd>a model fitted by <code>sjSDM</code></dd>
<dt>step_size</dt>
<dd>batch size for stochastic gradient descent</dd>
<dt>parallel</dt>
<dd>number of cpu cores for the data loader, only necessary for large datasets</dd></dl>

Arguments

Post hoc calculation of standard errors — getSe

<dl>

<dt>object</dt>
<dd>a model fitted by <code>sjSDM</code></dd>


<dt>step_size</dt>
<dd>batch size for stochastic gradient descent</dd>


<dt>parallel</dt>
<dd>number of cpu cores for the data loader, only necessary for large datasets</dd>

</dl>

getSe: Post hoc calculation of standard errors

Description

Usage

Value

Arguments