Hi Milton,

The drift model gives a time-dependent change in the expected, or mean value of 
the trait. If we denote the drift parameter as M, then the expected value of 
the trait, E(x), at time t is a+Mt where a is the root state of the trait. So 
if M is positive, your trait tends to get larger over time and if it is 
negative, the trait tends to get smaller. Note that this is a tendency because 
the trait is actually evolving under a biased random walk, so variance still 
increases with time, as in Brownian motion. For this reason, you wouldn’t do a 
branch length reconstruction to infer ancestral states under drift.  Because we 
are modeling change in the expected value of the trait through time, the model 
is unidentifiable without non-comtemporaneous (i.e. fossil, time series) tips 
or a very strong prior / bound on the root state. This is a good reference for 
the model in a non-phylogenetic context 
http://www.psjournals.org/doi/abs/10.1666/05070.1

You can do ancestral state estimation under the drift model using 
fitContinuousMCMC in geiger (this also allows you to place informative priors 
on node values) and I think Liam has a function in phytools to give you the ML 
estimates.

Cheers,

Graham


On May 28, 2015, at 4:07 PM, Milton Tan 
<[email protected]<mailto:[email protected]>> wrote:

Hello all,

I have some questions about the drift model implemented in geiger to test for a 
trend in increasing or decreasing trait values over time. I'm curious how to 
interpret the drift parameter, as well as whether BM is nested within BM. Is 
there a citation I can read for more information? I haven't seen the parameters 
of the drift model described anywhere explicitly, though perhaps I have missed 
it. It seems similar to the test for a directional pressure implemented in 
Pagel 1997, but I don't see any mention of the drift parameter.

Additionally, I'm curious if there's a good way to incorporate a drift model 
into ancestral state reconstruction? I imagine I can transform the branch 
lengths based on the drift parameter somehow and simply reconstruct ancestral 
trait states under BM, but I'm unsure how to do the branch length 
transformation for a drift model given that I'm not entirely sure what the 
drift parameter represents.

Thanks all,

Milton Tan
Auburn University
Department of Biological Sciences
PhD Candidate


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Graham Slater
Peter Buck Post-Doctoral Fellow
Department of Paleobiology
National Museum of Natural History
The Smithsonian Institution [NHB, MRC 121]
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