fun_clu_parallel

parallel version for functional clustering

Most existing approaches for network reconstruction can only infer an overall network
and, also, fail to capture a complete set of network properties. To address these issues,
a new model has been developed, which converts static data into their 'dynamic' form.
'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic,
omnidirectional and personalized networks. For more information on functional
clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>,
Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model,
see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022)
<doi:10.1080/19490976.2022.2106103>.

Ang Dong

idopNetwork

A Network Tool to Dissect Spatial Community Ecology

fun_clu_parallel function

<dl><dt>data</dt>
<dd>data for cluster</dd>
<dt>Time</dt>
<dd>vector for the time point</dd>
<dt>trans</dt>
<dd>indicate log/log2/log10 transform dataset</dd>
<dt>start</dt>
<dd>vector for the minimum cluster number</dd>
<dt>end</dt>
<dd>vector for the maximum cluster number</dd>
<dt>iter.max</dt>
<dd>scales control iteration for EM algorithm</dd>
<dt>thread</dt>
<dd>scales for how many threads used</dd></dl>

Arguments

parallel version for functional clustering — fun_clu_parallel

<dl>

<dt>data</dt>
<dd>data for cluster</dd>


<dt>Time</dt>
<dd>vector for the time point</dd>


<dt>trans</dt>
<dd>indicate log/log2/log10 transform dataset</dd>


<dt>start</dt>
<dd>vector for the minimum cluster number</dd>


<dt>end</dt>
<dd>vector for the maximum cluster number</dd>


<dt>iter.max</dt>
<dd>scales control iteration for EM algorithm</dd>


<dt>thread</dt>
<dd>scales for how many threads used</dd>

</dl>

fun_clu_parallel: parallel version for functional clustering

Description

Usage

Value

Arguments