Re: [R-sig-phylo] Will phyloXML in R be useful?

[Daniel Fulop]

Sounds like a good idea. Not sure if you’re reinventing the wheel or not.
Sounds like something ROpenSci might support; see:
https://github.com/ropensci/onboarding




From: George Vega Yon  
Reply: George Vega Yon  
Date: December 12, 2017 at 4:13:59 PM
To: Group R-sig-phylo 

Subject:  [R-sig-phylo] Will phyloXML in R be useful?

Hey,

Right now, I'm working on a wrapper for jsPhyloSVG (a javascript library
for visualizing phylogenetic trees on the web browser) in R, and just got
to learn about the phyloXML format. Googling around and checking out this
email list archives it seems that there's no support for this format in R.

My question is: how useful do you think having this in R will be? I'm
willing to write an R package to read/write trees in this format (done
before with GEXF, which is for networks in general). But I just want to
make sure that (1) this will be useful for the community, and (2) I'm not
reinventing the wheel (is anybody working on this now?). What are your
thoughts?

Best,

George G. Vega Yon
+1 (626) 381 8171
http://ggvy.cl

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Re: [R-sig-phylo] Non Parametric PGLS

[Daniel Fulop]

Sergio, 

You can fit a non-Gaussian phylo regression with MCMCglmm. 

HTH, 
Dan. 



 On Jun 17, 2015, at 9:40 AM, Sergio Ferreira Cardoso 
 sff.card...@campus.fct.unl.pt wrote:
 
 Hello all,
 
 I'm having a problem with a Multiple Regression PGLS analysis that I'm
 performing. The residuals are not normal and it's difficult to bring them
 to normality. In these cases, are there any alternatives to the linear
 model? I know that for non-phylogenetic analyses other models exist, but is
 there any alternative method for phylogenetic analysis?
 Thanks in advance.
 
 Best regards,
 Sérgio.
 
 
 -- 
 Com os melhores cumprimentos,
 Sérgio Ferreira Cardoso.
 
 
 
 Best regards,
 Sérgio Ferreira Cardoso
 
 
 
 
 MSc. Paleontology candidate
 Departamento de Ciências da Terra - FCT /Universidade Nova de Lisboa
 Geociências - Universidade de Évora
 
 Lisboa, Portugal
 
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Re: [R-sig-phylo] Standalone version of AWTY

[Daniel Fulop]

What about using Tracer instead? It's well-maintained with quite recent 
versions.

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Re: [R-sig-phylo] Phylogenetic regression with a trait homologous within lower taxonomic groups, but analogous between higher taxonomic levels

[Daniel Fulop]

Fwd'ing to the list because I neglected to do so  ...and in case others 
have something to add


 Original Message 
Subject: 	Re: [R-sig-phylo] Phylogenetic regression with a trait 
homologous within lower taxonomic groups, but analogous between higher 
taxonomic levels

Date:   Sun, 07 Jun 2015 19:13:42 -0700
From:   Daniel Fulop dfulop@gmail.com
To: David Labonte dl...@cam.ac.uk



Hi David,

You're correct that if you build phylogenies for each your 4 groups and
then connect all 4 in a polytomy (i.e. with zero branch lengths) then
you would get an appropriate tree to adjust for the effect of
phylogenetic relatedness (shared evol. history) in your trait.

In terms of the resulting matrix, as you suggest, you would have a block
matrix (such as the one attached) with zeroes in the cells that specify
trait covariance between species in different groups.

If the trait evolved independently, i.e. is analogous, in flies and
beetles then yes treat them as lacking trait phylogenetic covariance,
just as between beetles and geckos,

HTH,
Dan.



David Labonte wrote:

 Dear all,

 I have a data set on the size of a morphological trait, including
 representatives from very different groups (insects, frogs, lizards,
 spiders), and I am interested in its allometry, i.e. how its size
 changes with the weight of the animals. I started off with normal
 regression techniques, i.e. I treated the data points as independent,
 which is of course incorrect. I have good evidence that the allometry of
 the trait depends on the taxonomic level: the slope of the predicted
 relationship changes systematically from class to genus level.
 Now, I would like to correct for the dependence of the data introduced
 by relatedness. And here begins my problem: While the trait fulfils the
 same function across all taxa, it evolved independently several times.
 My, perhaps naive, question is: do I have to account for the relatedness
 with//respect/to the taxa/, or//with//respect/to the trait/ in question?
 If the trait was homologous across all taxa, this should be identical.
 However, it is not. For example, the trait is homologous within flies,
 beetles and geckos, but analogous between them. Now, should I treat
 flies more similar to beetles than to geckos (which seems intuitive), or
 should they be as unrelated to beetles as to geckos, given that the
 trait is analogous?

 As far as I understand phylogenetic generalised least squares, I
 construct a matrix to re-scale the residual error covariance structure,
 and the elements of this matrix are proportional to the branch length
 /shared /between two taxa. Thus, if the shared branch length is zero,
 the two taxa are effectively independent (is this interpretation
 actually correct?). If I attempted to build a tree based on the
 relatedness with respect to the trait, then my guess would be that
 first, I should build groups within which the trait is homologous, and
 second, all these groups should be connected directly to the root of my
 tree, so that their shared branch length is zero. Within these groups, I
 should be able to use the relatedness with respect to the taxa to create
 a reasonable tree topology. Would this procedure be horrifically wrong?
 If so, why, and are there reasonable alternatives?

 Thanks very much and best wishes
 David



--
Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2220
Life Sciences Addition, One Shields Ave.
Davis, CA 95616

510-253-7462
dfu...@ucdavis.edu



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Re: [R-sig-phylo] Constraining node values in an OUCH analysis

[Daniel Fulop]

Thanks, Graham ...but I'm not the OP. I was just shooting off a quick 
lead without actually checking the specifics in case it was useful.

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Re: [R-sig-phylo] Constraining node values in an OUCH analysis

[Daniel Fulop]

Isn't at least some of this functionality in mvSLOUCH and/or geiger? 
...it's definitely the case that mvSLOUCH can handle missing data at the 
tips, and I think fossil data can be incorporated in it and geiger as 
well. At least Slater 2013 has code for incorporating fossils in geiger 
or modified geiger functions.



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Re: [R-sig-phylo] A perfect storm: phylogenetic trees, random effects and zero-inflated binomial data

[Daniel Fulop]

MCMCglmm can definitely handle all of that.  Post back here and/or at 
the R-sig-mixed-models list for help with priors and others stuff when 
you've got some code developed.


Diederik Strubbe wrote:

Dear all,



I am struggling with analysing a dataset aimed at explaining invasion
success of non-native species. At a country level, I need to relate
invasion success (binomial: 0 for failed invasions, 1 for success) to
socio-economic variables, taking into account

-  Phylogenetic relatedness among introduced species: including
a phylogenetic tree

-  Country as a random effect

-  The fact that data are zero-inflated (most introductions fail).



Any suggestions for R packages that can handle a binomial response
variable, phylogenetic trees, random effects and zero-inflation?



Thanks in advance,



Diederik



--
Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2220
Life Sciences Addition, One Shields Ave.
Davis, CA 95616

510-253-7462
dfu...@ucdavis.edu

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Re: [R-sig-phylo] clade-specific release and radiate model?

[Daniel Fulop]

 to BM, but I would ideally still like compare
 standard models' fits (including OUwie models) to the fits of
 clade-specific release models.

 Any leads or suggestions would be much appreciated,
 especially about how to implement these clade-specific models
 with current tools or about how to roll my own.

 Thanks!
 Dan.

 --
 Daniel Fulop, Ph.D.
 Postdoctoral Scholar
 Dept. Plant Biology, UC Davis
 Maloof Lab, Rm. 2220
 Life Sciences Addition, One Shields Ave.
 Davis, CA 95616

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 http://www.mail-archive.com/r-sig-phylo%40r-project.org/



 --
 Daniel Fulop, Ph.D.
 Postdoctoral Scholar
 Dept. Plant Biology, UC Davis
 Maloof Lab, Rm. 2220
 Life Sciences Addition, One Shields Ave.
 Davis, CA 95616

 510-253-7462
 dfu...@ucdavis.edu mailto:dfu...@ucdavis.edu


-- 
Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2220
Life Sciences Addition, One Shields Ave.
Davis, CA 95616

510-253-7462
dfu...@ucdavis.edu


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[R-sig-phylo] clade-specific release and radiate model?

[Daniel Fulop]

Hi All,

Do you know if there are any methods out there for fitting ecological 
release and release and radiate models that are clade-specific?  That 
is, in which the change in mode (OU to BM) happens at the root of a 
clade instead of at specific time for the whole phylogeny (as in Slater 
2013).


As far as I know the closest out there are models in OUwie, say with a 
clade-specific second OU process with a very low alpha.  However, I 
don't think that biologically OU is a good model for the trait and clade 
in question (though it is for the rest of the tree).  I know that at the 
limit as alpha - 0 OU converts to BM, but I would ideally still like 
compare standard models' fits (including OUwie models) to the fits of 
clade-specific release models.


Any leads or suggestions would be much appreciated, especially about how 
to implement these clade-specific models with current tools or about how 
to roll my own.


Thanks!
Dan.

--
Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2220
Life Sciences Addition, One Shields Ave.
Davis, CA 95616

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Re: [R-sig-phylo] clade-specific release and radiate model?

[Daniel Fulop]

Hi Brian,

Thanks for your thoughtful response!  Those are very good points about 
identifiability and penalization of the OU mean for the released clade.

The censored model is an appealing approach, especially since the timing 
of the release along the (stem) branch leading to the released clade is 
unknown, and somewhat beside the point. In the case in question several 
novel morphological-mechanical structures had to evolve to enable the 
release, and whether those evolved gradually or not along the stem 
branch is an open question -- perhaps unknowable from analyzing extant taxa.

In thinking about this a bit more, though, I think methods for an epoch 
release may be able to accommodate a clade-specific scenario, at least 
if the timing of the mode shift is specified using SIMMAP trees and not 
a shift-time parameter.  Slater 2013 has R code associated with it that 
implements release models, and there's a multivariate implementation of 
release (and other mode shift) models in the new mvMORPH package (which 
is on CRAN, but not yet published as a manuscript as far as I can tell).

In skimming Slater's code it doesn't seem to use SIMMAP trees.  However, 
mvMORPH's shift specification can be done with SIMMAP trees, and I see 
no reason why its mvSHIFT function would care whether the shift is 
clade-specific or not.  I'm cc'ing Graham and Julien to see if they have 
something to add.  Regarding the use of mvSHIFT, I don't have 
multivariate data; hopefully that won't be a problem.

Cheers,
Dan.

Brian O'Meara wrote:
 Hi, Daniel. It's a bit arguable whether as alpha - 0, OU - BM: I 
 think it should, but IIRC in OUCH this doesn't happen, and that's a 
 deliberate choice. That said, I think that an OU with alpha near zero 
 would be ok for your question, though you might want to think about 
 how to penalize parameters (that is, for that clade there'd be an OU 
 mean parameter that is unidentifiable (alpha of zero, so no pull, so 
 no evidence for it): should you count this as a parameter when doing 
 model comparison? I'd argue no: you're doing OU with alpha of zero as 
 a kludgy hack to treat it as BM).

 Another approach would be to resuscitate the censored model of 
 O'Meara et al. 2006. Slice your tree on the edge leading to the 
 released clade (I guess this truly releases the clade to roam free of 
 its relatives) so you have the paraphyletic non-released set and the 
 released clade as separate trees. Then you can try fitting the same or 
 different models to the two trees. The downside of this is that you 
 must use an additional ancestral state (at the MRCA of the released 
 clade); the upside is that any weird changes happening on the edge 
 leading to the released clade aren't in the model and so don't affect 
 the fit (you could imagine that whatever led the clade to be 
 ecologically released happened somewhere on the stem edge, but you 
 don't know where, and it could be associated with a major single shift 
 in your continuous trait, too). You could try an OU on the 
 non-released tree (let's call this A) and an OU on the released clade 
 (B), OU on A and BM on B, etc. The only practical difficulty with this 
 is constraining the cases where A and B are supposed to have the same 
 model: by default, optimization will happen separately in different 
 trees, but you can create a wrapper function that calls OUwie.fixed() 
 separately on A and B but with the same parameters and adds the 
 likelihood and then optimize the parameters in this wrapper function.

 Hope this helps,
 Brian


 On Fri, May 29, 2015 at 2:02 AM, Daniel Fulop dfulop@gmail.com 
 mailto:dfulop@gmail.com wrote:

 Hi All,

 Do you know if there are any methods out there for fitting
 ecological release and release and radiate models that are
 clade-specific?  That is, in which the change in mode (OU to BM)
 happens at the root of a clade instead of at specific time for the
 whole phylogeny (as in Slater 2013).

 As far as I know the closest out there are models in OUwie, say
 with a clade-specific second OU process with a very low alpha. 
 However, I don't think that biologically OU is a good model for
 the trait and clade in question (though it is for the rest of the
 tree).  I know that at the limit as alpha - 0 OU converts to BM,
 but I would ideally still like compare standard models' fits
 (including OUwie models) to the fits of clade-specific release models.

 Any leads or suggestions would be much appreciated, especially
 about how to implement these clade-specific models with current
 tools or about how to roll my own.

 Thanks!
 Dan.

 -- 
 Daniel Fulop, Ph.D.
 Postdoctoral Scholar
 Dept. Plant Biology, UC Davis
 Maloof Lab, Rm. 2220
 Life Sciences Addition, One Shields Ave.
 Davis, CA 95616

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Re: [R-sig-phylo] clade-specific release and radiate model?

[Daniel Fulop]

Great!  Thanks, Julien!  Yeah, I was aware of make.simmap()...

Cheers,
Dan.

Julien Clavel wrote:
 Hi Dan,

 Fitting models with two distinct modes of trait evolution is possible 
 with the mvSHIFT function in mvMORPH. You just need to map on the tree 
 the two clades (or selective regimes, groups... otherwise) of interest 
 rather than two times periods.

 The mapping can easily be done using the make.simmap or the 
 paintSupTree functions in phytools. The package handle both univariate 
 and multivariate data.

 Best,

 Julien

 
 Date: Fri, 29 May 2015 08:27:31 -0700
 From: dfulop@gmail.com
 To: omeara.br...@gmail.com
 CC: r-sig-phylo@r-project.org; julien.cla...@hotmail.fr; slat...@si.edu
 Subject: Re: [R-sig-phylo] clade-specific release and radiate model?

 Hi Brian,

 Thanks for your thoughtful response!  Those are very good points about 
 identifiability and penalization of the OU mean for the released clade.

 The censored model is an appealing approach, especially since the 
 timing of the release along the (stem) branch leading to the released 
 clade is unknown, and somewhat beside the point. In the case in 
 question several novel morphological-mechanical structures had to 
 evolve to enable the release, and whether those evolved gradually or 
 not along the stem branch is an open question -- perhaps unknowable 
 from analyzing extant taxa.

 In thinking about this a bit more, though, I think methods for an 
 epoch release may be able to accommodate a clade-specific scenario, at 
 least if the timing of the mode shift is specified using SIMMAP trees 
 and not a shift-time parameter.  Slater 2013 has R code associated 
 with it that implements release models, and there's a multivariate 
 implementation of release (and other mode shift) models in the new 
 mvMORPH package (which is on CRAN, but not yet published as a 
 manuscript as far as I can tell).

 In skimming Slater's code it doesn't seem to use SIMMAP trees.  
 However, mvMORPH's shift specification can be done with SIMMAP trees, 
 and I see no reason why its mvSHIFT function would care whether the 
 shift is clade-specific or not.  I'm cc'ing Graham and Julien to see 
 if they have something to add.  Regarding the use of mvSHIFT, I don't 
 have multivariate data; hopefully that won't be a problem.

 Cheers,
 Dan.

 Brian O'Meara wrote:

 Hi, Daniel. It's a bit arguable whether as alpha - 0, OU - BM: I
 think it should, but IIRC in OUCH this doesn't happen, and that's
 a deliberate choice. That said, I think that an OU with alpha near
 zero would be ok for your question, though you might want to think
 about how to penalize parameters (that is, for that clade there'd
 be an OU mean parameter that is unidentifiable (alpha of zero, so
 no pull, so no evidence for it): should you count this as a
 parameter when doing model comparison? I'd argue no: you're doing
 OU with alpha of zero as a kludgy hack to treat it as BM).

 Another approach would be to resuscitate the censored model of
 O'Meara et al. 2006. Slice your tree on the edge leading to the
 released clade (I guess this truly releases the clade to roam free
 of its relatives) so you have the paraphyletic non-released set
 and the released clade as separate trees. Then you can try fitting
 the same or different models to the two trees. The downside of
 this is that you must use an additional ancestral state (at the
 MRCA of the released clade); the upside is that any weird changes
 happening on the edge leading to the released clade aren't in the
 model and so don't affect the fit (you could imagine that whatever
 led the clade to be ecologically released happened somewhere on
 the stem edge, but you don't know where, and it could be
 associated with a major single shift in your continuous trait,
 too). You could try an OU on the non-released tree (let's call
 this A) and an OU on the released clade (B), OU on A and BM on B,
 etc. The only practical difficulty with this is constraining the
 cases where A and B are supposed to have the same model: by
 default, optimization will happen separately in different trees,
 but you can create a wrapper function that calls OUwie.fixed()
 separately on A and B but with the same parameters and adds the
 likelihood and then optimize the parameters in this wrapper function.

 Hope this helps,
 Brian


 On Fri, May 29, 2015 at 2:02 AM, Daniel Fulop
 dfulop@gmail.com mailto:dfulop@gmail.com wrote:

 Hi All,

 Do you know if there are any methods out there for fitting
 ecological release and release and radiate models that are
 clade-specific?  That is, in which the change in mode (OU to
 BM) happens at the root of a clade instead of at specific time
 for the whole phylogeny

[R-sig-phylo] Is it okay to model species as a fixed effect in an LMM while also accounting for phylogenetic covariance?

[Daniel Fulop]

Hi All,

I have a question about the appropriateness of modeling species as a 
fixed effect in a mixed effect model while also accounting for phylogeny 
with a phylogenetic covariance random effect.

My aim is to compare the growth rates of 10 species in 2 temperatures.  
I am using MCMCglmm and have fit the following model:

stemLen ~ poly(day,2,raw=TRUE)*trt*spp , random=~ 
us(1+poly(day,2,raw=TRUE)):ID + phylo, rcov=~units

...where day is a numeric var. of days 1 - 12, trt is the temperature 
treatment, and spp is the species factor.

I am comparing average slopes in days 1 - 10.  I've got good inferences 
from the above model and used both the gelman.prior() function for fixed 
effects and parameter expanded priors for the random effects to improve 
mixing.

My question is whether it's acceptable to model species as a fixed 
effect, as above, in a phylogenetic mixed effect regression.  I think 
species is usually modeled as a random effect in such models.  However, 
since my aim is to compare species growth rates it made more sense to me 
to model species as a fixed effect.  Moreover, I found that models with 
species as a fixed effect are better fit by a long shot according to 
both DIC (from MCMCglmm) and AIC (from an lmer model w/o phylogeny).

Thanks in advance for your feedback!!
Dan.

-- 
Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2220
Life Sciences Addition, One Shields Ave.
Davis, CA 95616

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Re: [R-sig-phylo] fit Discrecte function of Geiger package

[Daniel Fulop]

Hi Beatriz,

These are messages related to optimization, as you likely surmised. It 
might be okay.  What's the convergence flag in the output of 
fitDiscrete?  If it's zero (i.e. it successfully converged) then it's okay.

Others with more expertise will probably chime in...

HTH,
Dan.

Beatriz Guzm�n wrote:
 Hi all:

 I am using the geiger package to estimate the phylogenetic signal of
 one discrete trait. I would like to compare the negative log
 likelihood when there is no signal
 (using a tree transformed lambda=0)to that when lambda was estimated
 using the original tree topology using a likelihood ratio test.
 However, I have a problem when
 estimating lambda using the original tree topology.

 *What I am doing is this:*
 mytree-read.nexus(MCC.nex)
 mydata-read.csv(DataPhySig.csv)
 col-mydata$Colour
 names(col)-mydata$SP
 col_lambda- fitDiscrete(mytree, col, transform=lambda)

 *And this is the error I get:*

 DLSODA-  Warning..Internal T (=R1) and H (=R2) are
such that in the machine, T + H = T on the next step
   (H = step size). Solver will continue anyway.
 In above message, R1 = 0, R2 = 0

 DINTDY-  T (=R1) illegal
 In above message, R1 = 7.42108e-219

T not in interval TCUR - HU (= R1) to TCUR (=R2)
 In above message, R1 = 0, R2 = 0

 DINTDY-  T (=R1) illegal
 In above message, R1 = 8.94663e-219 .

 The analysis estimates a negative logLikelihood but I'm not sure
 whether I could trust the value.

 Has anyone experienced this problem before? I would be very pleased
 whether someone could help me to solve it.

 Thanks

 Teresa

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-- 
Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2220
Life Sciences Addition, One Shields Ave.
Davis, CA 95616

510-253-7462
dfu...@ucdavis.edu


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Re: [R-sig-phylo] Replacement of Phybase - species tree from gene trees

[Daniel Fulop]

Hi,

Just a quick suggestion. You could install the archived version of 
phybase in order to look at the R code for your function(s) of interest 
and then reuse that code directly in your own R scripts (or put them in 
separate scripts and source them). You can also then further taylor 
those functions to suit your needs. This gets around the issue of using 
an old package version, but the minor downside that maintaining the 
functions' code is then up to you.

HTH,
Dan.

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Re: [R-sig-phylo] Phylogenetic signal in MCMCglmm

[Daniel Fulop]

Dear R-sig-phylo list,

I'm following up on a, seemingly unaddressed question from August 2013 
about whether or not MCMCglmm co-estimates Pagel's lambda within a 
phylogenetic regression.  Seraina's original message is copied at bottom.


From what I can tell MCMCglmm doesn't co-estimate lambda, but perhaps 
I'm missing something.  If it does, then I would like to know how to 
specify that lambda be co-estimated.


I have a complicated model with random effects apart from accounting for 
phylogeny that is unable to be fit by lme(); I get an optimization error 
when trying to include corPagel(fixed=FALSE) within the lme() call, 
whether I specify the non-phylogenetic random effects by formula or with 
pdMat constructors.


I'm analyzing plant growth of 10 species in 2 temperatures (control and 
cold) over a timeline, where I have several plants per species and daily 
measurements over 12 days; my model is (following lme4 formula syntax):


plant_height ~ day * temp * species + (day | ID)

My goal is to estimate/predict a growth rate for each species (while 
accounting for daily variation/noise in growth rate at the individual 
plant level = hence the day | ID random effect term) to then compare 
the growth rates in cold versus control temperatures for each species  
...to then assess which species seems most cold tolerant as measured by 
growth rate difference (cold - control) and relative growth rate (cold / 
control).


As an aside, I've fit the model without random effects but with 
corPagel(fixed=FALSE)in gls() and lambda is estimated as equal to zero 
or effectively so, depending on whether the starting value is 0 or not.  
Likewise, I've fit the full mixed model without the phylogeny with lme() 
and lmer() and then analyzed the phylogenetic signal of the residuals 
with phylosig() in phytools and again lambda is estimated as equal to 
0.  So, perhaps I shouldn't worry about fitting a phylogenetic 
regression in this particular case.


However, I have similar data from this and other experiments and so it 
would be ideal to find a robust way of running a phylogenetic mixed 
model regression with co-estimation of lambda, i.e. a way that doesn't 
lead to an optimization error.  Perhaps MCMCglmm offers that?


Thanks in advance for any input you could provide as to MCMCglmm and 
phylogenetic signal!

Cheers,
Dan.

--
Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2220
Life Sciences Addition, One Shields Ave.
Davis, CA 95616

Original message from Seraina Graber:

Dear MCMCglmm users,
I am running a simple model corrrecting for phylogenetic 
relationships using MCMCglmm. Now I am interested in the phylogenetic 
signal, the analogue to Pagels lambda.

Now I have two questions:
1.) According to Hadfield and Nakagawa (2010) the analogue to lambda 
(Pagel) in the mixed model approach is 
var(phylo)/var(phylo)+var(residuals), however, in another conversation 
about pyhlogenetic signal in MCMCglmm I found that actually 
var(phylo)/var(phylo)+var(residuals)+var(random effects) is the right 
measurement for the phylogenetic signal. But isnt the var(phylo) and 
var(random effects) basically the same, cos actually the pyhlogeny is 
the random effect in such a model? so for me rather 
var(phylo)/var(phylo) + var(residuals) makes more sense.

My model:
MCMCglmm(Y ~ X random=~animal, data= pedigree=phylotree, pr=F, 
saveX=F, pl=T), X and Y are two continuous variables.
2.) Comparing to the PGLS function in caper, there the 
variance-covariance matrix is adjusted for the strength of the 
phylogenetic signal (estimated lambda scales the off-diagonals of the 
phylogenetic vcv matrix). Is that somehow done in the MCMCglmm approach? 
if yes, how?

For any help I am very grateful.
Cheers,
Sereina

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Re: [R-sig-phylo] Phylogenetic signal in MCMCglmm

[Daniel Fulop]

Hi Shinichi,

Thanks so much for the prompt and detailed response.  I should've reread 
some of that literature before writing to the list-serv.

So, what I am gathering from your answer is that it is unnecessary to 
co-estimate lambda and transform the tree's branch lengths by it, since 
lambda is implicitly always co-estimated.  Hence if var(phylo) is 
inferred to be very small in absolute value and/or very small in 
proportion to the total random effect + residual variance, then lambda 
would be very close to zero.

In any case, I'll have to do some reading to understand why the branch 
length transformation using lambda (or kappa or delta for that matter) 
that is done in the ML context isn't necessary in the Bayesian context 
in MCMCglmm.  Perhaps it's something related to a different 
parameterization that makes it more computationally tractable in ML, 
i.e. that optimizing lambda (to infer how much variance is accounted for 
by the phylogeny) is easier than optimizing var(phylo) while also 
optimizing var(residuals) and the other random effects.  Maybe 
optimizing lambda isn't computationally easier, but simply a different 
way to achieve the same goal?

Thanks again,
Dan.

Shinichi Nakagawa wrote:
 Hi, Daniel

 lamda and H^2 are equivalent as we say in Hadfield and Nakagawa (2010) 
 or said also in Housworth et al (2004)

 Housworth E, Martins E, Lynch M (2004) The phylogenetic mixed model. 
 Am Nat 163(1):84–96.

 lamda = var(phylo)/(var(phylo)+var(residuals))

 A mathematical proof of this is in Hansen and Orzack 2005

 Hansen TF, Orzack SH (2005) Assessing current adaptation and 
 phylogenetic inertia as explanations of trait evolution: the need for 
 controlled comparisons. Evolution 59(10):2063–2072

 More general verison of H^2 or lamda is

 lamda = var(phylo)/(var(phylo)+var(all other random effects + 
 residuals)) (probably not including measurment errors)

 Best wishes,

 Shinichi

 On 24/07/2014, at 10:51 am, Daniel Fulop dfulop@gmail.com 
 mailto:dfulop@gmail.com wrote:

 Dear R-sig-phylo list,

 I'm following up on a, seemingly unaddressed question from August 
 2013 about whether or not MCMCglmm co-estimates Pagel's lambda within 
 a phylogenetic regression.  Seraina's original message is copied at 
 bottom.

 From what I can tell MCMCglmm doesn't co-estimate lambda, but perhaps 
 I'm missing something.  If it does, then I would like to know how to 
 specify that lambda be co-estimated.

 I have a complicated model with random effects apart from accounting 
 for phylogeny that is unable to be fit by lme(); I get an 
 optimization error when trying to include corPagel(fixed=FALSE) 
 within the lme() call, whether I specify the non-phylogenetic random 
 effects by formula or with pdMat constructors.

 I'm analyzing plant growth of 10 species in 2 temperatures (control 
 and cold) over a timeline, where I have several plants per species 
 and daily measurements over 12 days; my model is (following lme4 
 formula syntax):

 plant_height ~ day * temp * species + (day | ID)

 My goal is to estimate/predict a growth rate for each species (while 
 accounting for daily variation/noise in growth rate at the individual 
 plant level = hence the day | ID random effect term) to then compare 
 the growth rates in cold versus control temperatures for each species 
  ...to then assess which species seems most cold tolerant as measured 
 by growth rate difference (cold - control) and relative growth rate 
 (cold / control).

 As an aside, I've fit the model without random effects but with 
 corPagel(fixed=FALSE)in gls() and lambda is estimated as equal to 
 zero or effectively so, depending on whether the starting value is 0 
 or not.  Likewise, I've fit the full mixed model without the 
 phylogeny with lme() and lmer() and then analyzed the phylogenetic 
 signal of the residuals with phylosig() in phytools and again lambda 
 is estimated as equal to 0.  So, perhaps I shouldn't worry about 
 fitting a phylogenetic regression in this particular case.

 However, I have similar data from this and other experiments and so 
 it would be ideal to find a robust way of running a phylogenetic 
 mixed model regression with co-estimation of lambda, i.e. a way that 
 doesn't lead to an optimization error.  Perhaps MCMCglmm offers that?

 Thanks in advance for any input you could provide as to MCMCglmm and 
 phylogenetic signal!
 Cheers,
 Dan.

 -- 
 Daniel Fulop, Ph.D.
 Postdoctoral Scholar
 Dept. Plant Biology, UC Davis
 Maloof Lab, Rm. 2220
 Life Sciences Addition, One Shields Ave.
 Davis, CA 95616

 Original message from Seraina Graber:

 Dear MCMCglmm users,
 I am running a simple model corrrecting for phylogenetic 
 relationships using MCMCglmm. Now I am interested in the phylogenetic 
 signal, the analogue to Pagels lambda.
 Now I have two questions:
 1.) According to Hadfield and Nakagawa (2010) the analogue to lambda 
 (Pagel) in the mixed model approach is 
 var(phylo)/var(phylo)+var(residuals

[R-sig-phylo] phylogenetic multiple regression of plant growth

[Daniel Fulop]

Dear All,

I am considering how best to account for phylogeny in plant growth 
regressions with one numeric and two categorical predictors.  I am 
initially trying to use nlme functions lme() or gls(), but I am open to 
other software solutions; I do have one non-phylogenetic random effect 
to deal with possible spatial variance within the plant growth chamber, 
but it accounts for very little variance.


I have daily growth measurements (stem, root, and number of leaves) for 
a set of species under control and cold conditions.  I will be modeling 
the three response variables (i.e. stem and root lengths, and number of 
leaves) separately.


The fixed effect part of my linear model is, e.g.: stemLength ~ day * 
treatment * species.  I am then using the regression-predicted means to 
compare the absolute and relative growth rates of each species under 
control and cold temperatures. In case it's not clear, day is the 
numeric predictor and treatment and species the categorical (i.e. 
factor) ones.


In order to incorporate within species variation, I was going to use 
zero branch length polytomies as in this post from Simon Blomberg: 
https://stat.ethz.ch/pipermail/r-sig-phylo/2008-November/000206.html.  
However, since I not only have multiple individuals per species but also 
multiple (i.e. daily) measurements per individual I am not sure if it is 
appropriate to deal with the multiplicity of measurements this way.


Say I have a species for which 8 individuals in each of 2 conditions 
were measured for 10 days, that makes for 160 measurements.  Thus, I 
would have to graft onto this species' tree branch a 160 branch polytomy 
with zero branch lengths.  However, I am not sure that this would be an 
appropriate way of implicitly modelingresidual variance in growth rate 
*between days* (within and among individuals) or between control and 
cold treatments.


Any suggestions are most welcome, whether it's reassurance that the 
polytomy method is okay to use in this case or advice about how to 
better deal with between day and between treatment residual variance.  
...or an entirely different way of running these regressions.


Thanks!
Dan.

--
Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2220
Life Sciences Addition, One Shields Ave.
Davis, CA 95616

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Re: [R-sig-phylo] Pairwise tree distance matrix

[Daniel Fulop]

Hi Ross,

What about Dendropy?  It has several tree distances measures implemented, and 
can be easily scripted in Python (easy to use even without much knowledge of 
the language).

See http://pythonhosted.org/DendroPy/tutorial/treestats.html, section 3.3.10 at 
the bottom.

HTH,
Dan.


Daniel Fulop, Ph.D.
Postdoctoral Scholar
Dept. Plant Biology, UC Davis
Maloof Lab, Rm. 2119
Life Sciences Addition, One Shields Ave.
Davis, CA 95616

530-752-8086
dfu...@ucdavis.edu



On Sep 21, 2013, at 3:00 AM, r-sig-phylo-requ...@r-project.org wrote:

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 Today's Topics:
 
   1. Re: Pairwise tree distance matrix (Ross Mounce)
 
 
 --
 
 Message: 1
 Date: Fri, 20 Sep 2013 11:25:47 +0100
 From: Ross Mounce rcp...@bath.ac.uk
 To: r-sig-phylo@r-project.org
 Subject: Re: [R-sig-phylo] Pairwise tree distance matrix
 Message-ID: 20130920112547.688b9dda2fb3288faaa34...@bath.ac.uk
 Content-Type: text/plain; charset=UTF-8
 
 Hi Rob,
 
 Dave Bapst kindly brought this to my attention.
 
 I have a few suggestions for calculating pairwise RF distances between trees.
 (Apologies if I don't exactly answer the original question)
 
 1.) Firstly, I have to say, the very fastest solutions lie in implementations 
 currently outside of R e.g.
 MrsRF. So if speed is vital I'd build a workflow around this, it's extremely 
 quick at all the all-to-all tree-to-tree calculations I've tried it with:
 
 * Matthews, S. and Williams, T. 2010. MrsRF: an efficient MapReduce algorithm 
 for analyzing large collections of evolutionary trees. BMC Bioinformatics 
 11:S15-9.
 
 2.) If you're looking for broad solutions (measuring tree distance with a 
 variety of measures, not just Robinson-Foulds) I'd recommend TreeCmp as that 
 does a wide variety of them:
 
 * Bogdanowicz, D., Giaro, K., and Wrobel, B. 2012. TreeCmp: Comparison of 
 trees in polynomial time. Evolutionary Bioinformatics pp. 475+.
 
 Sidenote: whilst being the computationally quickest and most commonly-used, 
 I'm sure both 'biologically' and in terms of discriminatory power 
 Robinson-Foulds is NOT the most optimal measure of tree-tree distance (but 
 hey, that won't stop people using it exclusively...).
 
 
 3.) I built a kludgy workflow myself from TNT - R to better automate the 
 taxon-jackknifing tests first performed in: Cobbett, A., Wilkinson, M., and 
 Wills, M. 2007. Fossils impact as hard as living taxa in parsimony analyses 
 of morphology. Systematic biology 56:753-766.
 
 So if by chance you're doing these all-to-all tree distance calculations to 
 get at the mean-minimum tree distances between 2 different sets of trees you 
 might want to peruse my code which is here on github:
 https://github.com/rossmounce/extinct_extant_chapter (see the diagram for a 
 visual explanation)
 It's not the fastest or most elegant implementation possible... but it works. 
 (Apologies if this last option is not relevant to your use-case)
 
 
 
 Best,
 
 Ross
 
 
 -- Forwarded message --
 From: Rob Lanfear rob.lanf...@gmail.com
 Date: Thu, Sep 19, 2013 at 4:41 PM
 Subject: [R-sig-phylo] Pairwise tree distance matrix
 To: r-sig-phylo r-sig-phylo@r-project.org
 
 
 Hi All,
 
 I'm looking for a method to calculate a pairwise distance matrix of RF
 distances between a set of trees.
 
 Specifically, I know it's possible to do this in linear time (relative to
 the number of taxa), using an algorithm proposed in 1985[1]. This algorithm
 is implemented in various places (e.g. TreeSetVis in Mesquite), but I
 couldn't find an implementation in R.
 
 If anyone knows of an implementation, or has ideas on where best to start
 building one, please let me know.
 
 Cheers,
 
 Rob
 
 
 [1] Day,W. H. E. 1985. Optimal algorithms for comparing trees with labeled
 leaves. J. Classi?cation 2:7?28.
 
 --
 Rob Lanfear
 Research Fellow,
 Ecology, Evolution, and Genetics,
 Research School of Biology,
 Australian National University
 
 
 
 -- 
 -/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-/-
 Ross Mounce
 PhD Student (writing-up now!)
 Fossils, Phylogeny and Macroevolution Research Group
 University of Bath, 4 South Building, Lab 1.07
 Systematics Association Council Member
 http://about.me/rossmounce
 
 
 
 --
 
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