This function implements the SDM method of Criscuolo et al. (2006) for
 a set of n distance matrices.

models

Functions for reading, writing, plotting, and manipulating phylogenetic trees, analyses of comparative data in a phylogenetic framework, ancestral character analyses, analyses of diversification and macroevolution, computing distances from DNA sequences, reading and writing nucleotide sequences as well as importing from BioConductor, and several tools such as Mantel's test, generalized skyline plots, graphical exploration of phylogenetic data (alex, trex, kronoviz), estimation of absolute evolutionary rates and clock-like trees using mean path lengths and penalized likelihood, dating trees with non-contemporaneous sequences, translating DNA into AA sequences, and assessing sequence alignments. Phylogeny estimation can be done with the NJ, BIONJ, ME, MVR, SDM, and triangle methods, and several methods handling incomplete distance matrices (NJ*, BIONJ*, MVR*, and the corresponding triangle method). Some functions call external applications (PhyML, Clustal, T-Coffee, Muscle) whose results are returned into R.

Emmanuel Paradis

Analyses of Phylogenetics and Evolution

Simon Blomberg

Ben Bolker

Joseph Brown

Santiago Claramunt

Julien Claude

Hoa Sien Cuong

Richard Desper

Gilles Didier

Benoit Durand

Julien Dutheil

RJ Ewing

Olivier Gascuel

Thomas Guillerme

Christoph Heibl

Anthony Ives

Bradley Jones

Franz Krah

Daniel Lawson

Vincent Lefort

Pierre Legendre

Jim Lemon

Guillaume Louvel

Eric Marcon

Rosemary McCloskey

Johan Nylander

Rainer Opgen-Rhein

Andrei-Alin Popescu

Manuela Royer-Carenzi

Klaus Schliep

Korbinian Strimmer

Damien de Vienne

SDM function

<dl> <dt>...</dt>
<dd>2n elements (with n &gt; 1), the first n elements are the
 distance matrices: these can be (symmetric) matrices, objects of
 class <code>"dist"</code>, or a mix of both. The next n elements are the
 sequence length from which the matrices have been estimated (can be
 seen as a degree of confidence in matrices).</dd></dl>

Arguments

Author

Construction of Consensus Distance Matrix With SDM — SDM

<dl>

 <dt>...</dt>
<dd>2n elements (with n &gt; 1), the first n elements are the
 distance matrices: these can be (symmetric) matrices, objects of
 class <code>"dist"</code>, or a mix of both. The next n elements are the
 sequence length from which the matrices have been estimated (can be
 seen as a degree of confidence in matrices).</dd>

</dl>

Construction of Consensus Distance Matrix With SDM

SDM: Construction of Consensus Distance Matrix With SDM

Description

Usage

Value

Arguments

Author

Details

References

See Also