size.search

The size search algorithm finds the optimal number of HMM states for a set of
sequences and returns both the optimal <code>hmm</code> object and the
corresponding number of hidden states. Used in <code>select.seeds</code>.

Provides an implementation of a mixture of hidden Markov models
(HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering
(DBHC) algorithm. The DBHC algorithm is an HMM Clustering
algorithm that finds a mixture of discrete-output HMMs while using
heuristics based on Bayesian Information Criterion (BIC) to search for the
optimal number of HMM states and the optimal number of clusters.

Gabriel Budel

DBHC

Sequence Clustering with Discrete-Output HMMs

size.search function

<dl><dt>sequences</dt>
<dd>An <code>stslist</code> object (see
<code>seqdef</code>) of sequences with discrete observations.</dd>
<dt>log_space</dt>
<dd>Logical, parameter provided to
<code>fit_model</code> for whether to use optimization in log
space or not.</dd>
<dt>print</dt>
<dd>Logical, whether to print intermediate steps or not.</dd></dl>

Arguments

Size Search Algorithm — size.search

<dl>

<dt>sequences</dt>
<dd>An <code>stslist</code> object (see
<code>seqdef</code>) of sequences with discrete observations.</dd>


<dt>log_space</dt>
<dd>Logical, parameter provided to
<code>fit_model</code> for whether to use optimization in log
space or not.</dd>


<dt>print</dt>
<dd>Logical, whether to print intermediate steps or not.</dd>

</dl>

size.search: Size Search Algorithm

Description

Usage

Value

Arguments

See Also