init_pam

Initialisation of the co-clusters by partitioning around medoids method.

internal

Implementation of a probabilistic method for biclustering
adapted to overdispersed count data. It is a Gamma-Poisson Latent
Block Model.  It also implements two selection criteria in order to
select the number of biclusters.

Julie Aubert

cobiclust

Biclustering via Latent Block Model Adapted to Overdispersed
Count Data

INRAE 

init_pam function

<dl><dt>x</dt>
<dd>The output of the cobiclust function.</dd>
<dt>nu_j</dt>
<dd>a vector of numeric, corresponding of a column effect, may be interpreted as a sampling effort. The length is equal to the number of columns.</dd>
<dt>a</dt>
<dd>an numeric.</dd>
<dt>K</dt>
<dd>an integer specifying the number of groups in rows.</dd>
<dt>G</dt>
<dd>an integer specifying the number of groups in columns.</dd>
<dt>akg</dt>
<dd>a logical variable indicating whether to use a common dispersion parameter (<code>akg = FALSE</code>) or a dispersion parameter per cocluster (<code>akg = TRUE</code>).</dd></dl>

Arguments

Initialisation of the co-clusters by partitioning around medoids method. — init_pam

<dl>

<dt>x</dt>
<dd>The output of the cobiclust function.</dd>


<dt>nu_j</dt>
<dd>a vector of numeric, corresponding of a column effect, may be interpreted as a sampling effort. The length is equal to the number of columns.</dd>


<dt>a</dt>
<dd>an numeric.</dd>


<dt>K</dt>
<dd>an integer specifying the number of groups in rows.</dd>


<dt>G</dt>
<dd>an integer specifying the number of groups in columns.</dd>


<dt>akg</dt>
<dd>a logical variable indicating whether to use a common dispersion parameter (<code>akg = FALSE</code>) or a dispersion parameter per cocluster (<code>akg = TRUE</code>).</dd>

</dl>

init_pam: Initialisation of the co-clusters by partitioning around medoids method.

Description

Usage

Value

Arguments