If you are trying to mimic real data, then perhaps you have some fossil data to 
go on?  If not, then you can try to pick a "reasonable" value based on other 
biological knowledge.  Check the Garland et al. (1993) for how we did it.

Cheers,
Ted

From: Bryan McLean [bryansmcl...@gmail.com]
Sent: Tuesday, May 10, 2016 3:32 PM
To: Theodore Garland Jr
Cc: r-sig-phylo@r-project.org
Subject: Re: [R-sig-phylo] simulating continuous data

Thanks Joe and Ted,

By similar scaling, I just meant (as Ted guessed) that the root value depends 
on the empirical trait data, and does not start at 0 or 1, e.g., and thus 
produces simulated values that can be directly compared to the true data. Under 
a Brownian model, the mean trait value is suitable as the root value, but how 
does one specify a root value under a different and potentially better fitting 
model (OU, EB)? Im working mostly in R.

-Bryan


On 10 May2016, at 4:38 PM, Theodore Garland Jr 
<theodore.garl...@ucr.edu<redir.aspx?REF=AN_ibnXiaD0O4vZyU0gRkuM10zwe_J79otA2qAjqOGNrZWjaMHnTCAFtYWlsdG86dGhlb2RvcmUuZ2FybGFuZEB1Y3IuZWR1>>
 wrote:

This is a good point and one that is often glossed over.
We talked about it quite a bit here:

Garland, T., Jr., A. W. Dickerman, C. M. Janis, and J. A. Jones. 1993. 
Phylogenetic analysis of covariance by computer simulation. Systematic Biology 
42:265�292.
http://www.biology.ucr.edu/people/faculty/Garland/GarlEA93.pdf<redir.aspx?REF=nrsLOfdTXQ0xP6XNqhfwOU7IJZYuetx0hvh-igbGQTJrZWjaMHnTCAFodHRwOi8vd3d3LmJpb2xvZ3kudWNyLmVkdS9wZW9wbGUvZmFjdWx0eS9HYXJsYW5kL0dhcmxFQTkzLnBkZg..>

Surely you want to do various descriptive statistics on your simulated data 
sets to see how they compare with the real one, and presumably you want some of 
those to include the phylogenetic versions (e.g., conventional and phylogenetic 
estimates of the correlation coefficient if you are simulating two traits).

I think it is also really important to consider models that have limits to 
trait evolution (again, see the paper listed above).  Those limits can interact 
strongly with starting (root) values, especially if you include evolutionary 
trends.

Check Figure 1 in this paper:

Diaz-Uriarte, R., and T. Garland. 1996. Testing hypotheses of correlated 
evolution using phylogenetically independent contrasts: sensitivity to 
deviations from Brownian motion. Systematic Biology 45:27�47.
http://www.biology.ucr.edu/people/faculty/Garland/DiazGa96.pdf

Cheers,
Ted

Theodore Garland, Jr., Professor
Department of Biology
University of California, Riverside
Riverside, CA 92521
Office Phone:  (951) 827-3524
Facsimile:  (951) 827-4286 (not confidential)
Email:  tgarl...@ucr.edu
http://www.biology.ucr.edu/people/faculty/Garland.html
http://scholar.google.com/citations?hl=en&user=iSSbrhwAAAAJ

Director, UCR Institute for the Development of Educational Applications

Editor in Chief, Physiological and Biochemical Zoology

Fail Lab: Episode One
http://testtube.com/faillab/zoochosis-episode-one-evolution
http://www.youtube.com/watch?v=c0msBWyTzU0

________________________________________
From: R-sig-phylo [r-sig-phylo-boun...@r-project.org] on behalf of Bryan McLean 
[bryansmcl...@gmail.com]
Sent: Tuesday, May 10, 2016 1:24 PM
To: r-sig-phylo@r-project.org
Subject: [R-sig-phylo] simulating continuous data

Hi list,

I�m working to simulate multiple continuous characters on a known phylogeny 
(using several of the standard models), and I want to compare properties of the 
simulated datasets to an empirical dataset. My question is: what is the 
standard method for ensuring that those datasets (simulated, empirical) are 
actually directly comparable, i.e. scaled similarly? Does this involve 
specifying a sensible root state (e.g. ancestral reconstruction) OR just 
rescaling one or the other datasets before or after the analysis? Forgive me if 
this is a bit of a naive question, just trying to get a sense of standard 
practices.

-Bryan McLean
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