qdODE_ls

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

qdODE_ls function

<dl><dt>pars</dt>
<dd>vector for unknown ODE parameters</dd>
<dt>data</dt>
<dd>data contain independent effect as first row and dependent effect</dd>
<dt>Time</dt>
<dd>vector of time point</dd>
<dt>power_par</dt>
<dd>matrix of power equation parameters for dependent effect</dd></dl>

Arguments

least-square fit for qdODE model — qdODE_ls

<dl>

<dt>pars</dt>
<dd>vector for unknown ODE parameters</dd>


<dt>data</dt>
<dd>data contain independent effect as first row and dependent effect</dd>


<dt>Time</dt>
<dd>vector of time point</dd>


<dt>power_par</dt>
<dd>matrix of power equation parameters for dependent effect</dd>

</dl>

qdODE_ls: least-square fit for qdODE model

Description

Usage

Value

Arguments