Yes, we have always used REML for this!
Cheers,
Ted
From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org] on
behalf of Emmanuel Paradis [emmanuel.para...@ird.fr]
Sent: Friday, March 15, 2013 11:21 PM
To: Liam J. Revell; r-sig-phylo-boun...@r-project.org; Alejandro Gonzalez
Cc: R-phylo Mailing-list
Subject: Re: [R-sig-phylo] Ancestral state estimates of continuous traits
Alejandro and Liam,
You can also try method="REML" which gives much better estimates of sigma^2
than ML. I think I'll make it the default for continuous characters.
Best,
Emmanuel
-Original Message-
From: "Liam J. Revell"
Sender: r-sig-phylo-boun...@r-project.org
Date: Fri, 15 Mar 2013 14:23:20
To: Alejandro Gonzalez
Cc: R-phylo Mailing-list
Subject: Re: [R-sig-phylo] Ancestral state estimates of continuous traits
Hi Alejandro.
That must be a bug in ace(...,method="GLS").
I would also suggest you check out fastAnc in the phytools package. It
uses ace(...,method="pic") internally to take advantage of the fact that
the contrasts estimate at the root and the MLE assuming Brownian
evolution are the same. It re-roots the tree at all internal nodes which
sounds computationally intensive but is actually much faster than
getting the MLEs via numerical optimization.
fastAnc also uses equation (6) from Rohlf (2001) to get the correct 95%
interval on the estimates. My analysis
(http://blog.phytools.org/2013/02/new-version-of-fastanc-new-build-of.html)
suggests that the 95% CIs from ace(...,method="ML") are too small. (This
is probably because they rely on asymptotic properties of likelihood
that are not satisfied for the relatively small size of most
phylogenetic datasets.)
All the best, Liam
Liam J. Revell, Assistant Professor of Biology
University of Massachusetts Boston
web: http://faculty.umb.edu/liam.revell/
email: liam.rev...@umb.edu
blog: http://blog.phytools.org
On 3/15/2013 8:18 AM, Alejandro Gonzalez wrote:
> Hello,
>
> I am using ape to obtain ancestral state estimates for continuous
> traits. Two options are available, either maximum likelihood or a GLS
> method. I am comparing the results of both methods and one difference
> between the two puzzles me, I hope someone can enlighten me. Under GLS
> the ancestral state estimate at the root has virtually identical values
> for the point estimate and 95% confidence intervals, here is one
> example: 100 1.7848301 1.78483005 1.7848301 (100 is the root node
> number, then the ancestral state estimate and 95% CIs, respectively).
> However, when I use maximum likelihood for the same ancestral sate
> estimate, with the same data and tree I get the following result for the
> root : 100 1.6570119 0.86612615 2.4478977 (numbers in the same order
> as above).
> Any ideas as to why GLS gives such narrow confidence intervals for the
> ancestral state estimate at the basal node?
>
> Cheers
>
> Alejandro
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