Note that I just updated sim.history on GitHub. I recommend using the
latest version of phytools on GitHub if you intend to try this.
(This version can easily be installed using the devtools package. See my
blog for details.)
The latest version of sim.history permits you to set the ancestral state
for all simulations, to pick it randomly from a vector of possible
states, or to pick it randomly with probabilities specified by the user.
Let me know if you have any questions. 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 5/11/2016 1:45 PM, Liam J. Revell wrote:
Hi Michael.
It looks like you want to simulate a process A->A'->B (for example)
where the difference between A & A' cannot be observed, and the
transitions are permitted to have different rates (and, perhaps, be
reversible)?
If so, you might want to try sim.history in phytools followed by
mergeMappedStates to merge the states (e.g., A & A' in this case) that
are not distinguishable. Finally, you can use getStates to pull out the
tip values. (Alternatively, if you are not interested in the full
history you can just simulate using sim.char & then merge the states
manually afterwards....)
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 5/11/2016 1:37 PM, Michael Foisy wrote:
P.S. -- I should add that I do have estimated rates for my precursor
model(s)... I'm not sure why I presented the matrix like that.
, Michael
On Wed, May 11, 2016 at 1:34 PM, Michael Foisy <
michael.fo...@mail.utoronto.ca> wrote:
Hi,
I'm running a statistical analysis which involves simulating discrete
data
at the tips (0,1) under a precursor-2 model (Marazzi et al., 2012).
It seems like Beaulieu et al. (Syst. Biol 2013) simulated under a few
different precursor models in his corHMM package; however, the
citation may
just be for fitting corHMM models to the simulated data, I'm not sure...
I've read through the package notes a few times and can't find
anything to
do this in corHMM.
Any ideas? Am I missing something in corHMM, or is there another
package
which I should use?(familiar with sim.char{geiger} and rTraitDisc {ape}
# my precursor matrix goes something like this:
precursor2 <- matrix(data = c(NA, NA, 2, NA,
NA, NA, NA, NA,
1, NA, NA, 4,
NA, NA, 3, NA),
4,
byrow = TRUE)
# I would add in zeros and balance the gain and loss rates, etc.,
depending
on what package requires..
Thanks a lot!
, Michael Foisy
MSc. Student,
Rodd and Mahler Labs
University of Toronto
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