NNS.distance

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

Nonlinear nonparametric statistics using partial moments.  Partial moments are the elements of variance and asymptotically approximate the area of f(x).  These robust statistics provide the basis for nonlinear analysis while retaining linear equivalences.  NNS offers: Numerical integration, Numerical differentiation, Clustering, Correlation, Dependence, Causal analysis, ANOVA, Regression, Classification, Seasonality, Autoregressive modeling, Normalization and Stochastic dominance.  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>rpm_class</dt>
<dd>integer <code>rpm</code>.</dd>
<dt>dist.estimate</dt>
<dd>Vector to generate distances from.</dd>
<dt>type</dt>
<dd>"L1", "L2" or "FACTOR"</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>rpm_class</dt>
<dd>integer <code>rpm</code>.</dd>


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


<dt>type</dt>
<dd>"L1", "L2" or "FACTOR"</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