NNS.distance

Internal kernel function for NNS multivariate regression NNS.reg parallel instances.

NNS (Nonlinear Nonparametric Statistics) leverages partial moments – the fundamental elements of variance that asymptotically approximate the area under f(x) – to provide a robust foundation for nonlinear analysis while maintaining linear equivalences.  NNS delivers a comprehensive suite of advanced statistical techniques, including: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization, Stochastic dominance and Advanced Monte Carlo sampling.  All routines based on: Viole, F. and Nawrocki, D. (2013), Nonlinear Nonparametric Statistics: Using Partial Moments (ISBN: 1490523995).

Fred Viole

Nonlinear Nonparametric Statistics

Roberto Spadim

NNS.distance function

<dl><dt>rpm</dt>
<dd>REGRESSION.POINT.MATRIX from NNS.reg</dd>
<dt>dist.estimate</dt>
<dd>Vector to generate distances from.</dd>
<dt>k</dt>
<dd><code>n.best</code> from NNS.reg</dd>
<dt>class</dt>
<dd>if classification problem.</dd></dl>

Arguments

NNS Distance — NNS.distance

<dl>

<dt>rpm</dt>
<dd>REGRESSION.POINT.MATRIX from NNS.reg</dd>


<dt>dist.estimate</dt>
<dd>Vector to generate distances from.</dd>


<dt>k</dt>
<dd><code>n.best</code> from NNS.reg</dd>


<dt>class</dt>
<dd>if classification problem.</dd>

</dl>

NNS.distance: NNS Distance

Description

Usage

Value

Arguments