Hi all,
to feed Dave's questions; I have body size data (showing high
phylogenetic signal) and a trend verified independently (Cope's).
Although all species in the tree are in fact fossil species, I derived
from it a number of trees which are time-interval phylogenies,
restricted to species living in a particular temporal interval. As such,
interval phylogenies are ultrametric. I'm trying ape's yule.cov function
to test whether body size did influence diversification, yet yule.cov
requires aces which are calculated by assuming brownian motion, which is
wrong given I know there is a real trend in the data. This is why I
asked if it is possible to calculate aces by assuming BM with a positive
(mu>0) trend.
Any hint?
Pas
----Messaggio originale----
Da: dwba...@uchicago.edu
Data: 17/02/2011 18.04
A: "pasquale.r...@libero.it"<pasquale.r...@libero.it>, "R Sig Phylo
Listserv"<r-sig-phylo@r-project.org>, "Liam J.
Revell"<liam.rev...@umb.edu>, "Gene Hunt"<hu...@si.edu>
Ogg: Re: [R-sig-phylo] R: Re: Simulation of Continuous Trait with
Active Trend
All-
Thanks for all the alternative ways to simulate trends!
I'd say estimating ancestral traits always depends on the question
you are after. If we are dealing with traits that may be evolving
under an active trend, than we know reconstruction could be
inaccurate because ancestral states are simply a weighted average of
the observed values. Whether that invalidates ones approach depends
on the question, how one deals with uncertainty in the ancestral
trait estimates and the data itself (i.e. How many fossil taxa do
you have? Do you have good a priori evidence for a particular root
state? Does the trait have a high or low level of phylogenetic
signal, as this influences the uncertainty?).
I think Finarelli and Flynn have a good point that including a large
sample of fossil taxa can help quite a bit in constraining our
understanding of active trend patterns, but I think we (as
paleontologists) need to give a great deal of consideration to how
we want to treat lineages in the fossil record (as internal nodes or
as terminal taxa) and how we time-scale our trees.
-Dave
On Thu, Feb 17, 2011 at 6:19 AM, pasquale.r...@libero.it
<mailto:pasquale.r...@libero.it> <pasquale.r...@libero.it
<mailto:pasquale.r...@libero.it>> wrote:
Hi all,
I have a simple question. Is it possible, or even feasible, to
run ancestral
state estimation under Brownian Motion with a trend, as modelled
by Emmanuel,
Gene and Liam? I wonder whether this is feasible with real data.
Thanks all
Pas
>----Messaggio originale----
>Da: emmanuel.para...@ird.fr <mailto:emmanuel.para...@ird.fr>
>Data: 17/02/2011 6.13
>A: "Hunt, Gene"<hu...@si.edu <mailto:hu...@si.edu>>
>Cc: "R Sig Phylo Listserv"<r-sig-phylo@r-project.org
<mailto:r-sig-phylo@r-project.org>>
>Ogg: Re: [R-sig-phylo] Simulation of Continuous Trait with
Active Trend
>
>Hi all,
>
>In rTraitCont, the argument model can be a function, eg:
>
>f <- function(x, l) x + rnorm(1, 1, sqrt(l * 0.1))
>
>which is a way to model a trend. Here's an example of what you
can do:
>
>tr <- rbdtree(.1, .05)
>tr <- makeNodeLabel(tr)
>x <- rTraitCont(tr, model = f, ancestor = TRUE)
>bt <- branching.times(tr)
>plot(-bt, x[names(bt)])
>
>You may add the tip values with:
>
>n <- Ntip(tr)
>points(rep(0, n), x[1:n], pch = 2)
>
>You can also draw lines linking the ancestors with their
descendants in
>this "morpho-time-space" since they can be identified in the
edge matrix.
>
>If you don't like this model, just change f. As a side note:
>
>f <- function(x, l) x + rnorm(1, 0, sqrt(l * 0.1))
>x <- rTraitCont(tr, model = f)
>
>simulates the same model (ie, BM) than:
>
>x <- rTraitCont(tr)
>
>HTH,
>
>Emmanuel
>
>Hunt, Gene wrote on 17/02/2011 01:25:
>> Hi,
>>
>> To Liam's extremely helpful and practical reply, I'd add
that one can also
use theory to generate tip data for a trend. For a given tree,
all tip values
can be considered a single draw from a multivariate normal
distribution, with
each dimension representing a tip taxon. For this MVN, the
vector of means is
equal to the directionality term (mu) times the root-to-tip
distance for each
terminal taxon, and the variance-covariance is that for BM. So
the following
two lines also generate tip data for a trend model for phylogeny
(phy):
>>
>> V<- vcv(phy)
>> x<- mvrnorm(n=1, diag(V)*mu, V)
>>
>> Setting n to a higher number will generate more replicates.
Liam in his
blog talks about why the above code can be slow, but I think it
is useful to
point out theory. BM can be generated the same way with the
multivariate mean
equal to the ancestral value repeated as many times as there are
tips in the
tree.
>>
>> Best,
>> Gene
>>
>>
>> On 2/16/11 1:04 PM, "Liam J. Revell" <liam.rev...@umb.edu
<mailto:liam.rev...@umb.edu>> wrote:
>>
>> Hi Dave.
>>
>> I believe BM with a trend should just have a non-zero mean
for the
>> random normal changes along individual branches of the tree.
It was
>> easy to add this to a function that I already have for fast
Brownian
>> motion simulation. I will post this update to my website:
>> http://anolis.oeb.harvard.edu/~liam/R-phylogenetics/
<http://anolis.oeb.harvard.edu/%7Eliam/R-phylogenetics/> but it
is so simple
>> that I have also pasted the code below.
>>
>> - Liam
>>
>> # Simulates BM evolution more quickly.
>> # A trend can be simulated by mu!=0.
>> # mu=0 is standard BM; mu<0 downward trend; mu>0 upward trend.
>> # Written by Liam J. Revell 2011
>> fastBM.trend<-function(tree,a=0,mu=0,sig2=1,internal=FALSE){
>>
>> n<-length(tree$tip)
>>
>> # first simulate changes along each branch
>>
x<-rnorm(n=length(tree$edge.length),mean=mu,sd=sqrt(sig2*tree$edge.
length))
>>
>> # now add them up
>> y<-matrix(0,nrow(tree$edge),ncol(tree$edge))
>> for(i in 1:length(x)){
>> if(tree$edge[i,1]==(n+1))
>> y[i,1]<-a
>> else
>> y[i,1]<-y[match(tree$edge[i,1],tree$edge[,2]),2]
>>
>> y[i,2]<-y[i,1]+x[i]
>> }
>>
>> rm(x); x<-c(y[1,1],y[,2])
>> names(x)<-c(n+1,tree$edge[,2])
>> x<-x[as.character(1:(n+tree$Nnode))]
>> names(x)[1:n]<-tree$tip.label
>>
>> # return simulated data
>> if(internal==TRUE)
>> return(x) # include internal nodes
>> else
>> return(x[1:length(tree$tip.label)]) # tip nodes only
>>
>> }
>>
>>
>> --
>> Liam J. Revell
>> University of Massachusetts Boston
>> web: http://www.bio.umb.edu/facstaff/faculty_Revell.html
>> (new) email: liam.rev...@umb.edu <mailto:liam.rev...@umb.edu>
>> (new) blog: http://phytools.blogspot.com
>>
>> On 2/16/2011 12:28 PM, David Bapst wrote:
>>> All-
>>> For a class presentation I was working on, I wanted other
students to use
>>> model fitting with fitContinuous() on simulated traits,
under different
>>> models of trait evolution. I was surprised to discover
there seemed to be
no
>>> easy way to simulate active directional trend mechanisms,
unlike other
trait
>>> evolution models.
>>>
>>> One idea I had was to kludge it.
>>> tree<-rtree(20)
>>> trait1<-,rTraitCont(tree,sigma=1)+2*diag(vcv.phylo(tree))
>>>
>>> An alternative would be an OU with the root far from the
theta value.
Takes
>>> some playing with the parameters to get something that
looks like a trend
in
>>> a traitgram.
>>>
trait2<-rTraitCont(tree,model="OU",sigma=1,alpha=0.5,root.value=-10)
>>>
>>> Both of these were strongly preferred as 'trend' in
fitContinuous()
compared
>>> to BM and OU1, but I was wondering if there were any
alternatives I was
>>> missing.
>>> -Dave
>>>
>>
>> _______________________________________________
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>>
>>
>>
>> --
>> Gene Hunt
>> Curator, Department of Paleobiology
>> National Museum of Natural History
>> Smithsonian Institution [NHB, MRC 121]
>> P.O. Box 37012
>> Washington DC 20013-7012
>> Phone: 202-633-1331 Fax: 202-786-2832
>> http://paleobiology.si.edu/staff/individuals/hunt.cfm
>>
>> _______________________________________________
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>> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
>>
>
>--
>Emmanuel Paradis
>IRD, Jakarta, Indonesia
>http://ape.mpl.ird.fr/
>
>_______________________________________________
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--
David Bapst
Dept of Geophysical Sciences
University of Chicago
5734 S. Ellis
Chicago, IL 60637
http://home.uchicago.edu/~dwbapst/
<http://home.uchicago.edu/%7Edwbapst/>
