embc

<code>embc</code> implements the core function of the Expectation-Maximization multivariate binary clustering.

Unsupervised, multivariate, binary clustering for meaningful annotation of data, taking into account the uncertainty in the data. A specific constructor for trajectory analysis in movement ecology yields behavioural annotation of trajectories based on estimated local measures of velocity and turning angle, eventually with solar position covariate as a daytime indicator, ("Expectation-Maximization Binary Clustering for Behavioural Annotation").

Joan Garriga

EMbC

Expectation-Maximization Binary Clustering

embc function

<dl><dt>X</dt>
<dd>The input data set. A multivariate matrix where each row is a data point and each column is an input feature (a variable).</dd>
<dt>U</dt>
<dd>A multivariate matrix with same dimension as X with the values of certainty associated to each corresponding value in X. Certainties assign reliability to the data points so that the less reliable is a data point the less its leverage in the clustering. By default certainties are set to one (no uncertainty in any value in X).</dd>
<dt>stdv</dt>
<dd>a vector with bounds for the maximum precision of clusters, given as minimum standard deviation for each variable, (by default is set to rep(sqrt(.Machine$double.eps),ncol(X))</dd>
<dt>maxItr</dt>
<dd>A limit to the number of iterations in case of slow convergence (defaults to 200).</dd>
<dt>info</dt>
<dd>Level of information shown at each step:
info=0 (default) shows step likelihood, number of clusters, and number of changing labels;
info=1, include clustering statistics;
info=2, include delimiters information;
info&lt;0, suppress any step information.</dd></dl>

Arguments

General pourpose multivariate binary Clustering (EMbC) — embc

<dl>

<dt>X</dt>
<dd>The input data set. A multivariate matrix where each row is a data point and each column is an input feature (a variable).</dd>


<dt>U</dt>
<dd>A multivariate matrix with same dimension as X with the values of certainty associated to each corresponding value in X. Certainties assign reliability to the data points so that the less reliable is a data point the less its leverage in the clustering. By default certainties are set to one (no uncertainty in any value in X).</dd>


<dt>stdv</dt>
<dd>a vector with bounds for the maximum precision of clusters, given as minimum standard deviation for each variable, (by default is set to rep(sqrt(.Machine$double.eps),ncol(X))</dd>


<dt>maxItr</dt>
<dd>A limit to the number of iterations in case of slow convergence (defaults to 200).</dd>


<dt>info</dt>
<dd>Level of information shown at each step:
info=0 (default) shows step likelihood, number of clusters, and number of changing labels;
info=1, include clustering statistics;
info=2, include delimiters information;
info&lt;0, suppress any step information.</dd>

</dl>

embc: General pourpose multivariate binary Clustering (EMbC)

Description

Usage

Value

Arguments

Examples