s.cluster.h

This function performs hierarchical clustering on a group of variables, given their distances from each other.

Methods and tools for model selection and multi-model inference (Burnham and Anderson (2002) <doi:10.1007/b97636>, among others).
'SUR' (for parameter estimation), 'logit'/'probit' (for binary classification), and 'VARMA' (for time-series forecasting) are implemented.
Evaluations are both in-sample and out-of-sample.
It is designed to be efficient in terms of CPU usage and memory consumption.

Ramin Mojab

Automated Uncertainty Analysis

Stephen Becker

s.cluster.h function

<dl><dt>distances</dt>
<dd>Lower triangle of a symmetric distance matrix (without the diagonal).
This can be the output of <code>s.distance</code> function.</dd>
<dt>linkage</dt>
<dd>Character string specifying the method for calculating the distance in a left-right node merge.
It can be <code>single</code>, <code>complete</code>, <code>uAverage</code>, <code>wAverage</code>, or <code>ward</code>.</dd></dl>

Arguments

Hierarchical Clustering — s.cluster.h

<dl>

<dt>distances</dt>
<dd>Lower triangle of a symmetric distance matrix (without the diagonal).
This can be the output of <code>s.distance</code> function.</dd>


<dt>linkage</dt>
<dd>Character string specifying the method for calculating the distance in a left-right node merge.
It can be <code>single</code>, <code>complete</code>, <code>uAverage</code>, <code>wAverage</code>, or <code>ward</code>.</dd>

</dl>

s.cluster.h: Hierarchical Clustering

Description

Usage

Value

Arguments

Details

Examples