vectorised_multi

Alternative vectorised version, highly efficient when nb_peptide &lt; 30.

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

vectorised_multi function

<dl><dt>data</dt>
<dd>A tibble or data frame containing imputed data sets for all
groups. Required columns: <code>Peptide</code>, <code>Group</code>, <code>Sample</code>,
<code>Output</code>. If missing data have been estimated from multiple
imputations, each imputation should be identified in an optional
<code>Draw</code> column.</dd>
<dt>mu_0</dt>
<dd>A vector, corresponding to the prior mean. If NULL, all groups
are initialised with the same empirical mean for each peptide.</dd>
<dt>lambda_0</dt>
<dd>A number, corresponding to the prior covariance scaling
parameter.</dd>
<dt>Sigma_0</dt>
<dd>A matrix, corresponding to the prior covariance parameter.
If NULL, the identity matrix will be used by default.</dd>
<dt>nu_0</dt>
<dd>A number, corresponding to the prior degrees of freedom.</dd></dl>

Arguments

Vectorised version of multi_posterior_mean() — vectorised_multi

<dl>

<dt>data</dt>
<dd>A tibble or data frame containing imputed data sets for all
groups. Required columns: <code>Peptide</code>, <code>Group</code>, <code>Sample</code>,
<code>Output</code>. If missing data have been estimated from multiple
imputations, each imputation should be identified in an optional
<code>Draw</code> column.</dd>


<dt>mu_0</dt>
<dd>A vector, corresponding to the prior mean. If NULL, all groups
are initialised with the same empirical mean for each peptide.</dd>


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


<dt>Sigma_0</dt>
<dd>A matrix, corresponding to the prior covariance parameter.
If NULL, the identity matrix will be used by default.</dd>


<dt>nu_0</dt>
<dd>A number, corresponding to the prior degrees of freedom.</dd>

</dl>

vectorised_multi: Vectorised version of multi_posterior_mean()

Description

Usage

Value

Arguments

Examples