MVKE

A toolbox for estimating vector fields from intensive
longitudinal data, and construct potential landscapes thereafter. The
vector fields can be estimated with two nonparametric methods: the
Multivariate Vector Field Kernel Estimator (MVKE) by Bandi & Moloche
(2018) <doi:10.1017/S0266466617000305> and the Sparse Vector Field
Consensus (SparseVFC) algorithm by Ma et al. (2013)
<doi:10.1016/j.patcog.2013.05.017>. The potential landscapes can be
constructed with a simulation-based approach with the 'simlandr'
package (Cui et al., 2021) <doi:10.31234/osf.io/pzva3>, or the
Bhattacharya et al. (2011) method for path integration
<doi:10.1186/1752-0509-5-85>.

Jingmeng Cui

fitlandr

Fit Vector Fields and Potential Landscapes from Intensive
Longitudinal Data

MVKE function

<dl><dt>d</dt>
<dd>The dataset. Should be a matrix or a data frame, with each row representing a random vector.</dd>
<dt>v</dt>
<dd>The vectors corresponding to the dataset. Should be a matrix or a data frame with the same shape as <code>d</code>. If missing, then the vectors will be calculated from the dataset.</dd>
<dt>h</dt>
<dd>The bandwidth for the kernel estimator.</dd>
<dt>kernel</dt>
<dd>The type of kernel estimator used. "Gaussian" by default.</dd></dl>

Arguments

Multivariate vector field kernel estimator — MVKE

<dl>

<dt>d</dt>
<dd>The dataset. Should be a matrix or a data frame, with each row representing a random vector.</dd>


<dt>v</dt>
<dd>The vectors corresponding to the dataset. Should be a matrix or a data frame with the same shape as <code>d</code>. If missing, then the vectors will be calculated from the dataset.</dd>


<dt>h</dt>
<dd>The bandwidth for the kernel estimator.</dd>


<dt>kernel</dt>
<dd>The type of kernel estimator used. "Gaussian" by default.</dd>

</dl>

MVKE: Multivariate vector field kernel estimator

Description

Usage

Value

Arguments

References