post_processing_rgm

This function performs post-processing on simulated data and results from a Random Graph Model (RGM).
It calculates mean posterior estimates, compares true and estimated edge probabilities, generates
various diagnostic plots, and returns a list of these plots.

Implements state-of-the-art Random Graphical Models (RGMs) for multivariate data analysis across multiple environments, offering tools for exploring network interactions and structural relationships. Capabilities include joint inference across environments, integration of external covariates, and a Bayesian framework for uncertainty quantification. Applicable in various fields, including microbiome analysis. Methods based on Vinciotti, V., Wit, E., & Richter, F. (2023). "Random Graphical Model of Microbiome Interactions in Related Environments." <arXiv:2304.01956>.

Francisco Richter

Advanced Inference with Random Graphical Models

Veronica Vinciotti

Ernst Wit

post_processing_rgm function

<dl><dt>simulated_data</dt>
<dd>A list containing simulated data from an RGM.</dd>
<dt>results</dt>
<dd>A list containing results from fitting an RGM to `simulated_data`.</dd></dl>

Arguments

Post-Processing for RGM (Random Graph Model) — post_processing_rgm

<dl>

<dt>simulated_data</dt>
<dd>A list containing simulated data from an RGM.</dd>


<dt>results</dt>
<dd>A list containing results from fitting an RGM to `simulated_data`.</dd>

</dl>

post_processing_rgm: Post-Processing for RGM (Random Graph Model)

Description

Usage

Value

Arguments