WH.SSQ

WH.SSQ,MatH-method

Compute the sum-of-squares-deviations (from the mean) matrix of a <code>MatH</code> object, i.e.
a matrix of numbers, consistent with
a set of distributions equipped with a L2 wasserstein metric.

In the framework of Symbolic Data Analysis, a relatively new
approach to the statistical analysis of multi-valued data, we consider
histogram-valued data, i.e., data described by univariate histograms. The
methods and the basic statistics for histogram-valued data are mainly based
on the L2 Wasserstein metric between distributions, i.e., the Euclidean metric
between quantile functions. The package contains unsupervised classification
techniques, least square regression and tools for histogram-valued data and for
histogram time series. An introducing paper is Irpino A. Verde R. (2015) <doi: 10.1007/s11634-014-0176-4>.

Antonio Irpino

HistDAWass

Histogram-Valued Data Analysis

WH.SSQ function

<dl><dt>object</dt>
<dd>a <code>MatH</code> object</dd>
<dt>...</dt>
<dd>some optional parameters</dd>
<dt>w</dt>
<dd>it is possible to add a vector of weights (positive numbers)
having the same size of the rows of the <code>MatH object</code>,
default = equal weight for each row</dd></dl>

Arguments

Method WH.SSQ — WH.SSQ

<dl>

<dt>object</dt>
<dd>a <code>MatH</code> object</dd>


<dt>...</dt>
<dd>some optional parameters</dd>


<dt>w</dt>
<dd>it is possible to add a vector of weights (positive numbers)
having the same size of the rows of the <code>MatH object</code>,
default = equal weight for each row</dd>

</dl>

WH.SSQ: Method WH.SSQ

Description

Usage

Value

Arguments

Examples