similarityMat

Computes the co-clustering (or similarity) matrix

Dirichlet process mixture of multivariate normal, skew normal or skew t-distributions
modeling oriented towards flow-cytometry data preprocessing applications. Method is
detailed in: Hejblum, Alkhassimn, Gottardo, Caron & Thiebaut (2019) <doi: 10.1214/18-AOAS1209>.

Boris P. Hejblum

NPflow

Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry
Data

Chariff Alkhassim

Francois Caron

similarityMat function

<dl><dt>c</dt>
<dd>a list of vector of length <code>n</code>. <code>c[[j]][i]</code> is 
the cluster allocation of observation <code>i=1...n</code> at iteration 
<code>j=1...N</code>.</dd>
<dt>step</dt>
<dd>provide co-clustering every <code>step</code> iterations.
Default is 1.</dd></dl>

Arguments

Author

Computes the co-clustering (or similarity) matrix — similarityMat

<dl>

<dt>c</dt>
<dd>a list of vector of length <code>n</code>. <code>c[[j]][i]</code> is 
the cluster allocation of observation <code>i=1...n</code> at iteration 
<code>j=1...N</code>.</dd>


<dt>step</dt>
<dd>provide co-clustering every <code>step</code> iterations.
Default is 1.</dd>

</dl>

similarityMat: Computes the co-clustering (or similarity) matrix

Description

Usage

Value

Arguments

Author