Hi Santiago.
This is identical to my suggestion, except that the pooled variance is a
weighted mean in which weights (for better or worse) are proportional to
the sample size of each species. If the variances are indeed
homogeneous, this should be preferred because it gives greater weight to
species whose variance we should know well. If not, then it risks giving
high weight to a species with a well-estimated, but peculiarly high or
low variance. Computing the straight mean, as you suggest, comes with
exactly the opposite set of shortcomings since species with relatively
small sample sizes may have very poorly estimated variances.
All the best, Liam
Liam J. Revell, Associate 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 8/16/2016 10:46 PM, Santiago Claramunt wrote:
Hi Rafael,
Your method would underestimate the error associated with values derived from
single specimens because those values would have the highest errors, not
average errors.
What I have done in such cases is to estimate an average standard deviation
across species and use that average standard deviation as the standard error of
the species with single specimens.
I don't know of a formal description of this solution but it is mentioned in
one of my papers: http://rspb.royalsocietypublishing.org/content/279/1733/1567
Best,
Santiago
Research Associate
Department of Ornithology
American Museum of Natural History
https://sites.google.com/site/sclaramuntuy/
On Aug 16, 2016, at 4:34 PM, Rafael S. Marcondes <raf.marcon...@gmail.com>
wrote:
Hi all,
I’m using OUwie to fit multi-optima OU models and I have a question about
incorporating measurement error into my analyses.
I’m running my models with known measurement error (mserr=‘known’) and
using the standard error (std.error()) as an estimate of it, as recommended
by Ives et al (2007). However, for some (a minority) of my tips, I was only
able to measure 1 specimen, so I have no standard error for them. So I’m
not sure about how to deal with those. At first I thought about just
setting their measurement error as 0, but then I figured that would
introduce false confidence. So what I’m doing now is I’m setting
measurement error for those tips as the mean of the errors of all the tips
for which I did measure more than one specimen. I got that idea also from
Ives et al when they mention averaging the error across species (jn the
third-to-last paragraph), but that was in a different context. I can’t find
any references that report dealing with the same problem, even though I
assume it must not be an uncommon one. So I’m wondering if mine is really
the best way to do it and, or if anyone has alternative suggestions?
i hope I’ve made my problem clear, and thanks in advance for any
suggestions.
*--*
*Rafael Sobral Marcondes*
PhD Candidate (Systematics, Ecology and Evolution/Ornithology)
Museum of Natural Science <http://sites01.lsu.edu/wp/mns/>
Louisiana State University
119 Foster Hall
Baton Rouge, LA 70803, USA
Twitter: @rafmarcondes <https://twitter.com/rafmarcondes>
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