Performs Bayesian Probit Regression given the predictors and response.

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

bpr function

<dl><dt>y</dt>
<dd>Vector of binary responses.</dd>
<dt>X</dt>
<dd>Matrix of predictors.</dd>
<dt>offset</dt>
<dd>Optional offset for the linear predictor.</dd>
<dt>theta</dt>
<dd>Initial values for the regression coefficients.</dd>
<dt>theta_0</dt>
<dd>Prior mean for the regression coefficients.</dd>
<dt>N_sim</dt>
<dd>Number of simulations to perform.</dd></dl>

Arguments

Bayesian Probit Regression (BPR) — bpr

<dl>

<dt>y</dt>
<dd>Vector of binary responses.</dd>


<dt>X</dt>
<dd>Matrix of predictors.</dd>


<dt>offset</dt>
<dd>Optional offset for the linear predictor.</dd>


<dt>theta</dt>
<dd>Initial values for the regression coefficients.</dd>


<dt>theta_0</dt>
<dd>Prior mean for the regression coefficients.</dd>


<dt>N_sim</dt>
<dd>Number of simulations to perform.</dd>

</dl>

bpr: Bayesian Probit Regression (BPR)

Description

Usage

Value

Arguments