fun_clu_BIC

plot BIC results 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_BIC function

<dl><dt>result</dt>
<dd>list directly from fun_clu_parallel function</dd>
<dt>crit</dt>
<dd>either BIC or AIC for module selection</dd>
<dt>title</dt>
<dd>title for the plot</dd></dl>

Arguments

plot BIC results for functional clustering — fun_clu_BIC

<dl>

<dt>result</dt>
<dd>list directly from fun_clu_parallel function</dd>


<dt>crit</dt>
<dd>either BIC or AIC for module selection</dd>


<dt>title</dt>
<dd>title for the plot</dd>

</dl>

fun_clu_BIC: plot BIC results for functional clustering

Description

Usage

Value

Arguments