qdODE_fit

legendre polynomials fit to qdODE model

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_fit function

<dl><dt>pars</dt>
<dd>vector of qdODE parameters</dd>
<dt>data</dt>
<dd>dataframe of observed data</dd>
<dt>Time</dt>
<dd>vector of time point</dd>
<dt>power_par</dt>
<dd>matrix of power equation parameters for dependent effect</dd>
<dt>LOP_order</dt>
<dd>scalar of LOP order</dd>
<dt>new_time</dt>
<dd>vector produce new defined time point</dd>
<dt>n_expand</dt>
<dd>scalar for how many interpolation needed</dd></dl>

Arguments

legendre polynomials fit to qdODE model — qdODE_fit

<dl>

<dt>pars</dt>
<dd>vector of qdODE parameters</dd>


<dt>data</dt>
<dd>dataframe of observed data</dd>


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


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


<dt>LOP_order</dt>
<dd>scalar of LOP order</dd>


<dt>new_time</dt>
<dd>vector produce new defined time point</dd>


<dt>n_expand</dt>
<dd>scalar for how many interpolation needed</dd>

</dl>

qdODE_fit: legendre polynomials fit to qdODE model

Description

Usage

Value

Arguments