Hi Rafael.
> Also, I realized that this has the effect of making the mserr of
> species with sample size of 1 equal to the pooled standard deviation.
Yes. This makes sense because our uncertainty about that observation of
the mean should be exactly equal to the population dispersion around the
true mean.
> Do you mind having a quick look at the piece of code below to see if
> I'm interpreting your suggestion correctly?
I just posted a function for the pooled variance on my blog:
http://blog.phytools.org/2016/08/simple-function-to-compute-pooled.html.
Maybe you can use this to double-check your code. You should be able to
just take the value from this function, and divide it's square-root by a
vector consisting of the square-roots of the sample sizes of each
species to get your (pooled) species-specific standard errors.
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 8/18/2016 4:34 PM, Rafael S. Marcondes wrote:
Hi again Liam,
Do you mind having a quick look at the piece of code below to see if I'm
interpreting your suggestion correctly?
Also, I realized that this has the effect of making the mserr of species
with sample size of 1 equal to the pooled standard deviation. Is this
what you had in mind?
Thank you so much again! That was a really helpful suggestion.
I have attached my dat.R file.
load('dat.R')
head(dat)
name mean sd n
4 Aklet_goeld_xxx_f_crow 5.350051 1.8719113 4
21 Aklet_melan_xxx_f_crow 2.599925 0.7038916 4
38 Ampel_grise_xxx_f_crow 8.818584 2.2707127 4
54 Apros_disju_xxx_f_crow 5.400079 0.7584150 2
70 Batar_ciner_arg_f_crow 6.937473 NA 1
86 Batar_ciner_cin_f_crow 4.393245 NA 1
psd=weighted.mean(x=dat[,'sd'], w=dat[,'n'], na.rm=TRUE)
#pooled standard deviation (wheighted avg of SDs for each tip)
pv=psd^2
#pooled variance (square of psd)
dat[,5]=sqrt(pv)/sqrt(dat[,'n'])
colnames(dat)=c('name', 'mean', 'sd', 'n', 'mserr')
head(dat)
name mean sd n mserr
4 Aklet_goeld_xxx_f_crow 5.350051 1.8719113 4 0.5746504
21 Aklet_melan_xxx_f_crow 2.599925 0.7038916 4 0.5746504
38 Ampel_grise_xxx_f_crow 8.818584 2.2707127 4 0.5746504
54 Apros_disju_xxx_f_crow 5.400079 0.7584150 2 0.8126784
70 Batar_ciner_arg_f_crow 6.937473 NA 1 1.1493008
86 Batar_ciner_cin_f_crow 4.393245 NA 1 1.1493008
*--
*
*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>
On Wed, Aug 17, 2016 at 8:59 AM, Rafael S. Marcondes
<raf.marcon...@gmail.com <mailto:raf.marcon...@gmail.com>> wrote:
Hi Liam,
Great, thank you! I'll definitely try that. Do you know of any
references describing that approach in detail, or using it in practice?
Thank you,
*--
*
*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>
On Tue, Aug 16, 2016 at 8:26 PM, Liam J. Revell <liam.rev...@umb.edu
<mailto:liam.rev...@umb.edu>> wrote:
Hi Rafael.
What I would recommend in the case where you have relatively
small samples per species is to compute a pooled within-species
variance (https://en.wikipedia.org/wiki/Pooled_variance
<https://en.wikipedia.org/wiki/Pooled_variance>). This is
effectively equivalent to assuming that the within-species
variances are relatively homogeneous across species - which is
probably pretty reasonable, particularly if your data are on a
log-scale. You would then compute the standard error of each
species mean as the square-root of this value divided by the
square-root of each species-specific sample size. These values
would then be your input for OUwie or other model-fitting
methods that take species mean standard errors.
All the best, Liam
Liam J. Revell, Associate Professor of Biology
University of Massachusetts Boston
web: http://faculty.umb.edu/liam.revell/
<http://faculty.umb.edu/liam.revell/>
email: liam.rev...@umb.edu <mailto:liam.rev...@umb.edu>
blog: http://blog.phytools.org
On 8/16/2016 4:34 PM, Rafael S. Marcondes 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/
<http://sites01.lsu.edu/wp/mns/>>
Louisiana State University
119 Foster Hall
Baton Rouge, LA 70803, USA
Twitter: @rafmarcondes <https://twitter.com/rafmarcondes
<https://twitter.com/rafmarcondes>>
[[alternative HTML version deleted]]
_______________________________________________
R-sig-phylo mailing list - R-sig-phylo@r-project.org
<mailto:R-sig-phylo@r-project.org>
https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
<https://stat.ethz.ch/mailman/listinfo/r-sig-phylo>
Searchable archive at
http://www.mail-archive.com/r-sig-phylo@r-project.org/
<http://www.mail-archive.com/r-sig-phylo@r-project.org/>
_______________________________________________
R-sig-phylo mailing list - R-sig-phylo@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/
