biget_par_int

acquire initial parameters 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

biget_par_int function

<dl><dt>X</dt>
<dd>matrix for cluster</dd>
<dt>k</dt>
<dd>vector for the cluster number</dd>
<dt>times1</dt>
<dd>vector for the x values or time points</dd>
<dt>times2</dt>
<dd>vector for the x values or time points</dd>
<dt>n1</dt>
<dd>scalar for number of column contain first trait/location etc</dd>
<dt>n2</dt>
<dd>scalar for number of column contain second trait/location etc</dd></dl>

Arguments

acquire initial parameters for functional clustering — biget_par_int

<dl>

<dt>X</dt>
<dd>matrix for cluster</dd>


<dt>k</dt>
<dd>vector for the cluster number</dd>


<dt>times1</dt>
<dd>vector for the x values or time points</dd>


<dt>times2</dt>
<dd>vector for the x values or time points</dd>


<dt>n1</dt>
<dd>scalar for number of column contain first trait/location etc</dd>


<dt>n2</dt>
<dd>scalar for number of column contain second trait/location etc</dd>

</dl>

biget_par_int: acquire initial parameters for functional clustering

Description

Usage

Value

Arguments