ro
branch lengths, but this does not seem to work either.
Any help would be gratefully received.
Jarrod Hadfield
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The University of Edinburgh is a charitable body, registered in
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R-sig-phylo mailing list
2di(host.tree)))
as.dendrogram(h.d)
Maybe it works.
Regards,
Klaus
On 7/25/11, Jarrod Hadfield wrote:
Dear list,
I'm trying to convert a phylo object into a dendrogram object with
little success. host.tree is a rooted ultrametric tree with polytomies
stored as phylo object. The polytomie
Hello Everyone,
I am fitting mixed models in which I have two phylogenies and I would
like to understand them in the context of earlier work. In parafit
(Legndre 2002) principal coordinates of the distance matrices (J-P) are
used, where J is a matrix of ones and P the phylogenetic correlation
matr
Hi Juan,
Thank you for your answer - this makes sense to me. However, could the
same not be said for the unormalised eigenvectors of A as they preserve
the original similarities among the taxa, have the same units etc.?
Also, since solve(A) has the same eigenvectors as A but with reciprocal
eigenv
Hi,
asreml (which is now free for academic users on windows I think) and
MCMCglmm can fit this type of model. For exampl a two trait model with
y1 and y2:
Ainv<-inverseA(tree)$Ainv
m1<-MCMCglmm(cbind(y1, y2)~trait, random=~us(trait):species,
rcov=~us(trait):units, ginverse=list(species=
Hi,
The warning:
1: glm.fit: algorithm did not converge
is from a standard glm that MCMCglmm uses to obtain semi-reasonable
starting values. For a three (I think this is correct for your data?)
category response the starting values are obtained from 2 binomial
glms (the presence of catego
Hi,
I have been helping someone with some analyses and came across some
routines to estimate asymmetric transition rates between discrete
characters. This surprised me because its fairly straightforward to
prove that asymmetric transition rates cannot be identified using data
collected on
(m1, m2) # asymmetric evolutionary transition rates strongly
supported
y<-rbinom(n, 1, 0.5) # random data unconnected to the tree but p=0.5
m1<-ace(y, tree, type = "d", model="SYM")
m2<-ace(y, tree, type = "d", model="ARD")
anova(m1, m2) #
would be happy to
know what others think.
Cheers,
~Dan
On Aug 16, 2012, at 10:09 AM, Jarrod Hadfield wrote:
> Hi,
>
> I have had a few replies off-list which have made me try and clarify
what I mean. I think the distinction needs to be made between two types of
probability: the prob
Hi,
Thanks for the Allman & Rhodes paper, it is very nice. For me at least
it confirms my suspicions, but made me realise that claims of
asymmetric transition rates are only suspicious if you are unprepared
to make some (strong?) assumptions. If anyone disagrees with what I
have written b
gh error rates.
Mark, thanks for pointing out the relationship between the
threshold model and the single-site covarion model.
Cheers,
~Dan
On Aug 17, 2012, at 6:31 AM, Jarrod Hadfield wrote:
Hi,
Thanks for the Allman & Rhodes paper, it is very nice. For me at
least it conf
Hi,
Regarding the blog and the feasibility of MCMCglmm for threshold models:
If y1 is binary and y2 is normal, then the univariate analysis would be:
Ainv<-inverseA(tree)$Ainv
m1<-MCMCglmm(y1~y2, random=~species,ginverse=list(species=Ainv),
data=my.data, prior=my.prior, family="ordinal")
f
Hi,
Quoting Margaret Evans on Mon, 24 Sep 2012
22:56:48 +0100 (BST):
Hello all,
I have a few questions concerning the specification of flat priors
(on the probability scale) for a phylogenetic logistic regression in
MCMCglmm.
1) First, I'd like to verify my understanding of the defau
Hi,
ASReml is another option, which uses REML. It takes 1/10th of a second
on a 1000 tip phylogeny and is considerably more flexible.
fit<-asreml(y~x,random=~giv(species),data=dat,ginverse=list(species=sm2asreml(Ainv)))
# with the data set up as:
ntips<-1000
tree<-rcoal(ntips) # si
Hi Sam,
The terminology G and R structure is used widely, for example in
ASreml & SAS and probably others. The G-structure is the covariance
matrix of the random effects and the R-structure is the covariance
matrix of the residuals. In your model you have one random term
(animal) and one
Hi Sereina,
You should not get that error message when you do not specify a prior
- but if you do can you let me know.
For the prior you specified you get the error message because
us(trait):units is specifying a 3x3 covariance matrix, and yet your
prior, R=list(V=1,nu=0.002), is specifyi
nits
etc.
Cheers,
Jarrod
Quoting "sereina.graber" on Fri, 02 Aug 2013
12:54:00 +0200:
Ursprüngliche Nachricht
Betreff: Re: Aw: Re: [R-sig-phylo] MCMCglmm for categorical data
with more than 2 levels - prior specification?
Von: Jarrod Hadfield
An: Sereina
2:binary2 -9.59263 -16.21345 -3.889063.403 <0.001
***
traitnominal.3:binary2 13.37745 9.26769 19.930644.247 <0.001
***
traitnominal.4:binary2 8.61585 3.82747 15.541713.446 <0.001
***
---
Best & thank you so much for your help!
GESENDET: Freitag, 02. August 20
: "Jarrod Hadfield"
AN: "Sereina Graber"
CC: r-sig-phylo@r-project.org
BETREFF: Re: Aw: Re: [R-sig-phylo] WG: Re: Re: MCMCglmm for
categorical data with more than 2 levels - prior specification?
Hi,
They are the effect of the covariates on the probability of being in
the cat
? I didn`t find it...
GESENDET: Donnerstag, 08. August 2013 um 15:36 Uhr
VON: "Jarrod Hadfield"
AN: "Sereina Graber"
CC: r-sig-phylo@r-project.org
BETREFF: Re: Aw: Re: Re: [R-sig-phylo] WG: Re: Re: MCMCglmm for
categorical data with more than 2 levels - prior specification?
H
Dear Gustaf,
How many levels of `habitat' are there, and are they cross-classified
with respect to species (i.e. are multiple species measured in the
same habitat)?
Assuming for now there are a reasonable number of habitats then the
simplest model (without cross-classification) in asreml/
z package which looks promising.
Cheers
Gustaf
On 2015-01-21 16:26, Jarrod Hadfield wrote:
Dear Gustaf,
How many levels of `habitat' are there, and are they
cross-classified with respect to species (i.e. are multiple species
measured in the same habitat)?
Assuming for now there
ution to the pattern we
observe:
Va / (Va+Vhab+Ve) #phylo
Vhab / (Va+Vhab+Ve) #habitat
Ve / (Va+Vhab+Ve) #measurement/plasticity/local adaption and
other processes
Did I get that right or am I lost?
Gustaf
On 2015-01-22 04:54, Jarrod Hadfield wrote:
Hi Gustaf,
1/ You can ignore n
n. This
should be easier for a continuous predictor, right?
Cheers
Gustaf
On 2015-01-22 12:23, Jarrod Hadfield wrote:
Hi Gustaf,
In the model with just species the residual variation is
measurement error/plasticity error, but could also include
deviations from the assumed BM process. If
Dear Diederik,
The lack of convergence is because the residual variance is
non-identifiable with binary data but you have a very weak prior on
it. You should fix the residual variance at something (I usually use 1):
prior.test<-list(R=list(V=1,fix=1), G=list(G1=list(V=1, nu=0.002),G2 =
li
problem may derive from your specification of the priors. Usually you don’t
specify the prior for B in MCMCglmm. The problem may also be related to the
size of your dataset. Estimation of effects can be difficult with binary data,
when the dataset is small. Below is a small example from Jarrod Ha
Hi,
I am unclear what assumptions are being made about the root values in
mvOU, and was wondering if someone could clarify? For ease, imagine an
OU1 model where there is one optimum per trait and so theta is a vector.
Is the root value assumed to be theta, or a draw from a multivariate
normal
Hi Chris,
I think ngen in threshbayes is not the number of full iterations (i.e. a
full update of all parameters), but the number of full iterations
multiplied by the number of nodes (2n-1). With n=600 species this means
threshbayes has only really done about 8,000 iterations (i.e. about
1000
pl=TRUE,
family="threshold")
an send me the summary and hist(dep2$Liab)
Cheers,
Jarrod
On 16/12/2016 07:02, Jarrod Hadfield wrote:
Hi Chris,
I think ngen in threshbayes is not the number of full iterations (i.e.
a full update of all parameters), but the number of full
data=traits,
prior=prior.dep2,
pr=TRUE,
pl=TRUE,
family="threshold")
an send me the summary and hist(dep2$Liab)
Cheers,
Jarrod
On 16/12/2016 07:02, Jarrod Hadf
Dear Wayne,
This is my fault. With phylogenies the ancestral nodes are treated as
missing data and so I set their measurement error to an arbitrary
value. The code for working out how many "new" measurement errors
there are was incorrect.
L98 of MCMCglmm.R should read
mev<-c(mev, rep(1,
Hi Diogo,
First, your model1 is unlikely to be valid unless the residual variance
happens to be 1. You should not fix it at one, and use a prior like:
prior = list(R = list(V = 1, nu = 0.02), G=list(G1=list(V=1, nu=0.02)))
Note that the residual variance (Ve) is the intra-specific variance,
Jarrod,
Thanks very much for the quick reply.
I'll try to implement the changes in the model.
Have a nice weekend,
Diogo
Em Sex, 14 de jul de 2017 17:48, Jarrod Hadfield
mailto:j.hadfi...@ed.ac.uk>> escreveu:
Hi Diogo,
First, your model1
Hi Jesse,
In order to account for phylogenetic uncertainty you are better just
pulling trees from their posterior rather than choosing trees that are
incongruent. The latter will give estimates biased toward those
associated with extreme trees.
If the analysis is the binomial model you poste
Hi Liam,
In multi-level models DIC can be 'focused' at different levels. In
MCMCglmm, DIC is focussed at the highest possible level because this is
the only level at which it can be analytically computed for non-Gaussian
models. The highest level is not the level at which most scientists want
Hi David,
It looks like phylo_ultra might be a list? Is phylo_ultra[[1]] a tree?
Also, don't use nodes="TIPS"; this is just to demonstrate how poor the
algorithm is when you don't use the expanded inverse. I see people using
nodes="TIPS" a lot - where does this code come from?
Cheers,
Jarrod
ysiology
Muséum National d'Histoire Naturelle, CNRS
7 rue Cuvier
75005 Paris, France
Tel.: +33.(0)1.40.79.53.74
Associate Editor Functional Ecology
http://davidcostantini.wordpress.com/
https://twitter.com/DavidZool
http://scholar.google.com/citations?user=nBSC4-EJ&hl=it
- Original Me
al Ecology
http://davidcostantini.wordpress.com/
https://twitter.com/DavidZool
http://scholar.google.com/citations?user=nBSC4-EJ&hl=it
- Original Message -
From: "Jarrod Hadfield"
To: "David COSTANTINI"
Cc: "r-sig-phylo"
Sent: Monday, 17 May, 2021 21:34:34
Subject: R
/DavidZool
http://scholar.google.com/citations?user=nBSC4-EJ&hl=it
- Original Message -
From: "Jarrod Hadfield"
To: "David COSTANTINI"
Cc: "r-sig-phylo"
Sent: Monday, 17 May, 2021 21:34:34
Subject: Re: [R-sig-phylo] phylogenetic correction and MCMC model
Hi,
chr
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