choose_cluster

Choose an ultrametric phylo object from <code>cluster_patristicmatrix()</code> obtained
with a particular clustering method, or the next best tree.
If there are no ultrametric trees, it does not force them to be ultrametric.

Methods and workflows to get chronograms (i.e., phylogenetic trees with branch lengths
proportional to time), using open, peer-reviewed, state-of-the-art scientific data on time of lineage divergence.
This package constitutes the main underlying code of the DateLife web service
at <https://www.datelife.org>. To obtain a single summary chronogram from a group of
relevant chronograms, we implement the Super Distance Matrix (SDM) method
described in Criscuolo et al. (2006) <doi:10.1080/10635150600969872>.
To find the grove of chronograms with a sufficiently overlapping set of taxa
for summarizing, we implement theorem 1.1. from Ané et al. (2009)
<doi:10.1007/s00026-009-0017-x>.
A given phylogenetic tree can be dated using time of lineage divergence data
as secondary calibrations (with caution, see Schenk (2016) <doi:10.1371/journal.pone.0148228>).
To obtain and apply secondary calibrations, the package implements the congruification method described
in Eastman et al. (2013) <doi:10.1111/2041-210X.12051>. Tree dating can be performed with different methods
including BLADJ (Webb et al. (2008) <doi:10.1093/bioinformatics/btn358>), PATHd8
(Britton et al. (2007) <doi:10.1080/10635150701613783>), mrBayes (Huelsenbeck
and Ronquist (2001) <doi:10.1093/bioinformatics/17.8.754>), and treePL (Smith
and O'Meara (2012) <doi:10.1093/bioinformatics/bts492>).

Luna L. Sanchez Reyes

datelife

Scientific Data on Time of Lineage Divergence for Your Taxa

Brian O'Meara

Jonathan Eastman

Tracy Heath

April Wright

Klaus Schliep

Scott Chamberlain

Peter Midford

Luke Harmon

Joseph Brown

Matt Pennell

Mike Alfaro

Emily Jane McTavish

choose_cluster function

<dl><dt>phycluster</dt>
<dd>An output from <code>cluster_patristicmatrix()</code></dd>
<dt>clustering_method</dt>
<dd>A character vector indicating the method to construct
the tree. Options are:<dl>
<dt>nj</dt>
<dd>Neighbor-Joining method applied with <code>ape::nj()</code>.</dd></dl></dd><dt>upgma</dt>
<dd>Unweighted Pair Group Method with Arithmetic Mean method applied
with <code>phangorn::upgma()</code>.</dd><dt>bionj</dt>
<dd>An improved version of the Neighbor-Joining method applied with
<code>ape::bionj()</code>.</dd><dt>triangle</dt>
<dd>Triangles method applied with <code>ape::triangMtd()</code></dd><dt>mvr</dt>
<dd>Minimum Variance Reduction method applied with <code>ape::mvr()</code>.</dd>
</dl>

Arguments

Choose an ultrametric phylo object from cluster_patristicmatrix() obtained
with a particular clustering method, or the next best tree.
If there are no ultrametric trees, it does not force them to be ultrametric. — choose_cluster

<dl>

<dt>phycluster</dt>
<dd>An output from <code>cluster_patristicmatrix()</code></dd>


<dt>clustering_method</dt>
<dd>A character vector indicating the method to construct
the tree. Options are:<dl>
<dt>nj</dt>
<dd>Neighbor-Joining method applied with <code>ape::nj()</code>.</dd>

<dt>upgma</dt>
<dd>Unweighted Pair Group Method with Arithmetic Mean method applied
with <code>phangorn::upgma()</code>.</dd>

<dt>bionj</dt>
<dd>An improved version of the Neighbor-Joining method applied with
<code>ape::bionj()</code>.</dd>

<dt>triangle</dt>
<dd>Triangles method applied with <code>ape::triangMtd()</code></dd>

<dt>mvr</dt>
<dd>Minimum Variance Reduction method applied with <code>ape::mvr()</code>.</dd>


</dl></dd>

</dl>

Choose an ultrametric phylo object from <code>cluster_patristicmatrix()</code> obtained
with a particular clustering method, or the next best tree.
If there are no ultrametric trees, it does not force them to be ultrametric.

choose_cluster: Choose an ultrametric phylo object from `cluster_patristicmatrix()` obtained with a particular clustering method, or the next best tree. If there are no ultrametric trees, it does not force them to be ultrametric.

Description

Usage

Value

Arguments