posterior_mean

Compute the posterior distribution of the means between multiple groups.
All peptides are considered independent from one another.

Bayesian toolbox for quantitative proteomics. In particular, this
package provides functions to generate synthetic datasets, execute Bayesian
differential analysis methods, and display results as, described in the
associated article Marie Chion and Arthur Leroy (2023) <arXiv:2307.08975>.

Arthur Leroy

ProteoBayes

Bayesian Statistical Tools for Quantitative Proteomics

Marie Chion

posterior_mean function

<dl><dt>data</dt>
<dd>A tibble or data frame containing imputed data sets for all
groups. Required columns: <code>Peptide</code>, <code>Output</code>, <code>Group</code>,
<code>Sample</code>.</dd>
<dt>mu_0</dt>
<dd>A vector, corresponding to the prior mean.</dd>
<dt>lambda_0</dt>
<dd>A number, corresponding to the prior covariance scaling
parameter.</dd>
<dt>beta_0</dt>
<dd>A matrix, corresponding to the prior covariance parameter.</dd>
<dt>alpha_0</dt>
<dd>A number, corresponding to the prior degrees of freedom.</dd></dl>

Arguments

Posterior distribution of the means — posterior_mean

<dl>

<dt>data</dt>
<dd>A tibble or data frame containing imputed data sets for all
groups. Required columns: <code>Peptide</code>, <code>Output</code>, <code>Group</code>,
<code>Sample</code>.</dd>


<dt>mu_0</dt>
<dd>A vector, corresponding to the prior mean.</dd>


<dt>lambda_0</dt>
<dd>A number, corresponding to the prior covariance scaling
parameter.</dd>


<dt>beta_0</dt>
<dd>A matrix, corresponding to the prior covariance parameter.</dd>


<dt>alpha_0</dt>
<dd>A number, corresponding to the prior degrees of freedom.</dd>

</dl>

posterior_mean: Posterior distribution of the means

Description

Usage

Value

Arguments

Examples