bifun_clu_plot

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

bifun_clu_plot function

<dl><dt>result</dt>
<dd>list directly from bifun_clu_parallel function</dd>
<dt>best.k</dt>
<dd>scale of BIC-determined cluster number</dd>
<dt>label</dt>
<dd>relabel x and y label due to log-transform, set 10 as default</dd>
<dt>degree</dt>
<dd>scalar control transparency degree</dd>
<dt>show.legend</dt>
<dd>show legend or not</dd>
<dt>color1</dt>
<dd>Hex Color Codes for first data</dd>
<dt>color2</dt>
<dd>Hex Color Codes for second data</dd></dl>

Arguments

bifunctional clustering plot — bifun_clu_plot

<dl>

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


<dt>best.k</dt>
<dd>scale of BIC-determined cluster number</dd>


<dt>label</dt>
<dd>relabel x and y label due to log-transform, set 10 as default</dd>


<dt>degree</dt>
<dd>scalar control transparency degree</dd>


<dt>show.legend</dt>
<dd>show legend or not</dd>


<dt>color1</dt>
<dd>Hex Color Codes for first data</dd>


<dt>color2</dt>
<dd>Hex Color Codes for second data</dd>

</dl>

bifun_clu_plot: bifunctional clustering plot

Description

Usage

Value

Arguments