Dear Karla, Concerning the n that should be passed to AICc I agree with what
was previously written that this is a complex, case by case issue. One can have
a number of ways to define what could be used in place of n. However, at
least in the one dimensional, BM and OU for me it seems that if your tree is
not too small, then taking n or n-1, as written by Liam, should be OK for model
selection purposes. At least I could not find issues with this:
"Phylogenetic effective sample size" J. Theor. Biol. 407: 371-386,
2016. Best wishes Krzysztof Bartoszek Dnia 5 wrzelśnia 2019 18:41
r-sig-phylo-request@r-project. napisał(a): Send R-sig-phylo mailing list
submissions to r-sig-phylo@r-project.org To subscribe or unsubscribe via the
World Wide Web, visit stat.ethz.ch stat.ethz.ch or, via email, send a message
with subject or body 'help' to r-sig-phylo-request@r-project. You
can reach the person managing the list at r-sig-phylo-ow...@r-project.or
When replying, please edit your Subject line so it is more specific than
"Re: Contents of R-sig-phylo digest..." Today's Topics: 1.
model averaging using brownie.lite (Karla Shikev) 2. Re: model averaging
using brownie.lite (Liam Revell) 3. Re: model averaging using brownie.lite
(Karla Shikev) 4. Re: model averaging using brownie.lite (Liam Revell) 5.
Re: model averaging using brownie.lite (Brian O'Meara) 6. Re: model
averaging using brownie.lite (Cecile Ane) 7. Rate of trait evolution through
time (Gregory Mutumi) ------------------------------ Message: 1 Date: Thu,
5 Sep 2019 10:13:34 -0300 From: Karla Shikev <karlashi...@gmail.com>
To: r-sig-phylo@r-project.org Subject: [R-sig-phylo] model averaging using
brownie.lite Message-ID: <CAPU4_4pkQzH8wnzV7EgiWTawAzNP Content-Type:
text/plain; charset="utf-8" Dear all, I've been trying to use
brownie.lite to implement the tutorial available here ( treethinkers.org
treethinkers.org to calculate model-averaged rates of evolution and for model
selection (1 versus 2 rates). However, the current version of phytools 0.6-99
won't produce AICc estimates. Does anyone know a way around this? Any help
would be greatly appreciated. thanks a bunch, Karla [[alternative HTML
version deleted]] ------------------------------ Message: 2 Date: Thu,
5 Sep 2019 13:27:12 +0000 From: Liam Revell <liam.rev...@umb.edu> To:
Karla Shikev <karlashi...@gmail.com>, "r-sig-phylo@r-project.org"
<r-sig-phylo@r-project.org> Subject: Re: [R-sig-phylo] model averaging
using brownie.lite Message-ID: <a9dfbf84-f333-ca99-074c-604c2
Content-Type: text/plain; charset="utf-8" Dear Karla. You could try
& create your own logLik method for the object class
"brownie.lite" as follows: ## method
logLik.brownie.lite<-function( lik<-setNames(
c(object$logL1,object$logL.mul c("single-rate","multi-rate"))
attr(lik,"df")<-c(object$k1,ob lik } ## fit model
fit<-brownie.lite(tree,x) ## use it logLik(fit) AIC(fit) All the best,
Liam Liam J. Revell Associate Professor, University of Massachusetts Boston
Profesor Asistente, Universidad Católica de la Ssma Concepción web:
faculty.umb.edu faculty.umb.edu www.phytools.org www.phytools.org Academic
Director UMass Boston Chile Abroad (starting 2019): www.umb.edu www.umb.edu
On 9/5/2019 9:13 AM, Karla Shikev wrote: [EXTERNAL SENDER] Dear all,
I've been trying to use brownie.lite to implement the tutorial available
here ( nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com to calculate model-averaged rates of
evolution and for model selection (1 versus 2 rates). However, the current
version of phytools 0.6-99 won't produce AICc estimates. Does anyone know
a way around this? Any help would be greatly appreciated. thanks a bunch,
Karla [[alternative HTML version deleted]]
______________________________ R-sig-phylo mailing list -
R-sig-phylo@r-project.org nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com Searchable archive at
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
------------------------------ Message: 3 Date: Thu, 5 Sep 2019 10:49:24
-0300 From: Karla Shikev <karlashi...@gmail.com> Cc:
"r-sig-phylo@r-project.org" <r-sig-phylo@r-project.org> Subject:
Re: [R-sig-phylo] model averaging using brownie.lite Message-ID:
<CAPU4_4oJ6NOz3aRHre5T1PfJ0DOG Content-Type: text/plain;
charset="utf-8" Thanks so much, Liam! Just one quick follow-up
question: what do you suggest should be the sample size for transforming AIC
into AICc? the number of tips on the tree? Karla On Thu, Sep 5, 2019 at
10:27 AM Liam Revell <liam.rev...@umb.edu> wrote: Dear Karla. You
could try & create your own logLik method for the object class
"brownie.lite" as follows: ## method
logLik.brownie.lite<-function( lik<-setNames(
attr(lik,"df")<- lik } ## fit model
fit<-brownie.lite(tree,x) ## use it logLik(fit) AIC(fit) All the best,
Liam Liam J. Revell Associate Professor, University of Massachusetts Boston
Profesor Asistente, Universidad Católica de la Ssma Concepción web:
faculty.umb.edu faculty.umb.edu www.phytools.org www.phytools.org Academic
Director UMass Boston Chile Abroad (starting 2019): www.umb.edu www.umb.edu
On 9/5/2019 9:13 AM, Karla Shikev wrote: > [EXTERNAL SENDER] > >
Dear all, > > I've been trying to use brownie.lite to implement the
tutorial available > here ( nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com to > calculate model-averaged rates
of evolution and for model selection (1 > versus 2 rates). However, the
current version of phytools 0.6-99 won't > produce AICc estimates. Does
anyone know a way around this? Any help would > be greatly appreciated.
> > thanks a bunch, > > Karla > > [[alternativ
HTML version deleted]] > > ______________________________ >
R-sig-phylo mailing list - R-sig-phylo@r-project.org >
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
> Searchable archive at nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com > [[alternative HTML version
deleted]] ------------------------------ Message: 4 Date: Thu, 5 Sep
2019 13:55:53 +0000 From: Liam Revell <liam.rev...@umb.edu> To: Karla
Shikev <karlashi...@gmail.com> Cc: "r-sig-phylo@r-project.org"
<r-sig-phylo@r-project.org> Subject: Re: [R-sig-phylo] model averaging
using brownie.lite Message-ID: <d9de7e79-a712-7214-2af4-ed53c
Content-Type: text/plain; charset="utf-8" Dear Karla. In my
opinion, it is probably correct to use the number of tips on the tree as the
sample size for AICc when estimating the Brownian rate: as the number of
independent pieces of information is n-1, just like with an ordinary variance.
For other parameters in phylogenetic comparative analyses, the effective
sample size may be different, however. All the best, Liam Liam J. Revell
Associate Professor, University of Massachusetts Boston Profesor Asistente,
Universidad Católica de la Ssma Concepción web: faculty.umb.edu
faculty.umb.edu www.phytools.org www.phytools.org Academic Director UMass
Boston Chile Abroad (starting 2019): www.umb.edu www.umb.edu On 9/5/2019
9:49 AM, Karla Shikev wrote: [EXTERNAL SENDER] Thanks so much, Liam! Just
one quick follow-up question: what do you suggest should be the sample size
for transforming AIC into AICc? the number of tips on the tree? Karla On
Thu, Sep 5, 2019 at 10:27 AM Liam Revell <liam.rev...@umb.edu> wrote:
Dear Karla. You could try & create your own logLik method for the object
class "brownie.lite" as follows: ## method
logLik.brownie.lite<-function( lik<-setNames(
attr(lik,"df") lik } ## fit model
fit<-brownie.lite(tree,x) ## use it logLik(fit) AIC(fit) All the best,
Liam Liam J. Revell Associate Professor, University of Massachusetts Boston
Profesor Asistente, Universidad Católica de la Ssma Concepción web:
faculty.umb.edu faculty.umb.edu nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com Academic Director UMass Boston Chile
Abroad (starting 2019): www.umb.edu www.umb.edu On 9/5/2019 9:13 AM, Karla
Shikev wrote: [EXTERNAL SENDER] Dear all, I've been trying to use
brownie.lite to implement the tutorial available here (
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
to calculate model-averaged rates of evolution and for model selection (1
versus 2 rates). However, the current version of phytools 0.6-99 won't
produce AICc estimates. Does anyone know a way around this? Any help would be
greatly appreciated. thanks a bunch, Karla [[alternativ HTML
version deleted]] ______________________________ R-sig-phylo mailing list -
R-sig-phylo@r-project.org nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com Searchable archive at
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
[[alternative HTML version deleted]] ______________________________
R-sig-phylo mailing list - R-sig-phylo@r-project.org
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
Searchable archive at nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com ------------------------------
Message: 5 Date: Thu, 5 Sep 2019 10:55:11 -0400 From: "Brian
O'Meara" <omeara.br...@gmail.com> To: Liam Revell
<liam.rev...@umb.edu> Cc: Karla Shikev <karlashi...@gmail.com>,
"r-sig-phylo@r-project.org" <r-sig-phylo@r-project.org>
Subject: Re: [R-sig-phylo] model averaging using brownie.lite Message-ID:
<CAKywhkq-CgX=khQnAbztr5s58dSb Content-Type: text/plain;
charset="utf-8" Sample size is a weird thing in this area for AICc.
For comparing DNA models in something like ModelTest, number of sites is used,
but for OU/BM models, we typically use number of taxa. It's not resolved
what's best. Posada and Buckley (2004, doi.org doi.org have a
discussion on this: Both in the AICc and the BIC descriptions above, the
total number of characters was used as an estimate of sample size. However,
effective sample sizes in phylogenetic studies are poorly understood, and
depend on the quantity of interest (Churchill et al., 1992; Goldman, 1998;
Morozov et al., 2000). Characters in an alignment will often not be
independent, so using the total number of characters as a surrogate for sample
size (Minin et al., 2003; Posada and Crandall, 2001b) could be an
overestimate. Using only the number of variable sites as an estimate of sample
size is a more conservative approach, but could be an underestimate (note that
all sites are used when estimating base frequencies or the proportion of
invariable sites). Indeed, sample size also depends on the number of taxa.
Importantly, sample size can have an effect on the outcome of model selection
with the AICc. In our example above, if we were to use the number of variable
characters (301 sites) as the sample size, instead of the total number of
characters (1927 sites), the best AICc model would not change, but the second
and third AICc models would exchange their rankings. Furthermore, because the
LRT, the AIC, and the BIC strategies rely on large sample asymptotics, it is
also important to decide when a sample should be considered small. Although
the AICc was derived under Gaussian assumptions, Burnham et al. (1994) found
that this second order expression performed well in product multinomial models
for open population capture-recapture. Burnham and Anderson (2003, p. 66)
suggest using this correction when the sample size is small compared to the
number of adjustable parameters, n/K < 40. Alternatively, and because AICc
converges to the AIC with increasing n/K ratios, one could always use the AICc
(D. Anderson, personal communications). Phylogenetic characters are mostly
discrete, and the unconstrained model in phylogenetics is multinomial
(Goldman, 1993). One may think of an alignment of nucleotide characters as a
large and sparse contingency table with 4^T bins, where T is the number of
taxa. For large sample asymptotics to hold in a contingency table every cell
should contain, in general, more than 5 observations (see Agresti, 1990, p.
49, 244–250), which gives a rule of thumb of n/4^T > 5. Clearly, more
research is needed on sample size in phylogenetics. Beaulieu et al. (2018,
doi.org doi.org note my COI as I'm an author on this) did some
simulations on a codon model testing different ways of counting sample size
(number of sites, number of taxa, number of sites * number of taxa, etc.) and
found that number of cells in the matrix (number of sites * number of taxa)
seemed to work best to approximate Kullback-Liebler distance. For univariate
models like that used in brownie.lite, number of cells is equal to number of
taxa (since there's only one column): We note our use of AICc, as
calculated in Burnham and Anderson (2002, p. 66) and as opposed to the
standard AIC, in the above model comparisons. At the outset of our study it
was unclear what the appropriate sample size n is when comparing models of
sequence evolution. Building upon the work of Jhwueng et al. (2014), our
simulations suggest that using the number of taxa times the number of sites as
the sample size correction performed best as a small sample size correction
for estimating Kullback–Liebler (KL) distance in phylogenetic models
(Supporting Materials). This also has an intuitive appeal. In models that have
at least some parameters shared across sites and some parameters shared across
taxa, increasing the number of sites and/or taxa should be adding more samples
for the parameters to estimate. This is consistent considering how likelihood
is calculated for phylogenetic models: the likelihood for a given site is the
sum of the probabilities of each observed state at each tip, which is then
multiplied across sites. It is arguable that the conventional approach in
comparative methods is calculating AICc in the same way. That is, if only one
column of data (or “site”) is examined, as remains remarkably common in
comparative methods, when we refer to sample size, it is technically the
number of taxa multiplied by number of sites, even though it is referred to
simply as the number of taxa. I suspect this is still not a great
approximation. Compare a balanced tree (every internal node having two
descendants) with every internal branch length the same versus a pectinate
(caterpillar) tree where the two edges connecting to the root node are very
long and the other edges are all near zero. For the same number of taxa and
same number of sites, I bet the first tree has more meaningful data: the
pectinate tree with those branch lengths will likely have all but one of the
taxa having nearly identical states. So I think tree shape and branch lengths
should matter for this. I've done some preliminary analyses on this,
building on Beaulieu et al. (2018) and Jhwueng et al. (2014, doi.org doi.org
also note COI), but nothing definitive yet. It's also worth looking at
Ho and Ané (2014, doi.org doi.org who talk about AIC in the context of OU
shifts, but who get into sample size with shifts in a modified BIC that uses
taxa in different regimes as sample size (but again, univariate, so maybe
it's actually matrix size). I also probably am missing important work by
others -- my apologies if so. If you know of any, please let me know (and
probably Karla, too!). So, in summary.... yeah, what Liam said: number of
taxa, but it might be more complex. Best, Brian
______________________________ Brian O'Meara, brianomeara.info,
brianomeara.info, especially Calendar < brianomeara.info brianomeara.info
CV < brianomeara.info brianomeara.info and Feedback < brianomeara.info
brianomeara.info Professor, Dept. of Ecology & Evolutionary Biology, UT
Knoxville Associate Head, Dept. of Ecology & Evolutionary Biology, UT
Knoxville He/Him/His On Thu, Sep 5, 2019 at 10:00 AM Liam Revell
<liam.rev...@umb.edu> wrote: Dear Karla. In my opinion, it is
probably correct to use the number of tips on the tree as the sample size for
AICc when estimating the Brownian rate: as the number of independent pieces of
information is n-1, just like with an ordinary variance. For other parameters
in phylogenetic comparative analyses, the effective sample size may be
different, however. All the best, Liam Liam J. Revell Associate Professor,
University of Massachusetts Boston Profesor Asistente, Universidad Católica de
la Ssma Concepción web: faculty.umb.edu faculty.umb.edu www.phytools.org
www.phytools.org Academic Director UMass Boston Chile Abroad (starting 2019):
www.umb.edu www.umb.edu On 9/5/2019 9:49 AM, Karla Shikev wrote: >
[EXTERNAL SENDER] > > Thanks so much, Liam! Just one quick follow-up
question: what do you > suggest should be the sample size for transforming
AIC into AICc? the > number of tips on the tree? > > Karla >
> On Thu, Sep 5, 2019 at 10:27 AM Liam Revell <liam.rev...@umb.edu>
wrote: > >> Dear Karla. >> >> You could try &
create your own logLik method for the object class >>
"brownie.lite" as follows: >> >> ## method >>
logLik.brownie.lite<-function( >> lik<-setName >>
>> >> attr(lik,"df
>> lik >> } >> ## fit model >>
fit<-brownie.lite(tree,x) >> ## use it >> logLik(fit)
>> AIC(fit) >> >> All the best, Liam >> >>
Liam J. Revell >> Associate Professor, University of Massachusetts
Boston >> Profesor Asistente, Universidad Católica de la Ssma Concepción
>> web: faculty.umb.edu faculty.umb.edu
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
>> >> Academic Director UMass Boston Chile Abroad (starting 2019):
>> www.umb.edu www.umb.edu >> >> On 9/5/2019 9:13 AM,
Karla Shikev wrote: >>> [EXTERNAL SENDER] >>> >>>
Dear all, >>> >>> I've been trying to use brownie.lite
to implement the tutorial available >>> here ( >>
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
) >> to >>> calculate model-averaged rates of evolution and
for model selection (1 >>> versus 2 rates). However, the current
version of phytools 0.6-99 won't >>> produce AICc estimates. Does
anyone know a way around this? Any help >> would >>> be
greatly appreciated. >>> >>> thanks a bunch, >>>
>>> Karla >>> >>> [[alternat HTML
version deleted]] >>> >>> ______________________________
>>> R-sig-phylo mailing list - R-sig-phylo@r-project.org
>>> >> nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com >>> Searchable archive at
>> nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com >>> >> > >
[[alternativ HTML version deleted]] > >
______________________________ > R-sig-phylo mailing list -
R-sig-phylo@r-project.org > nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com > Searchable archive at
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
> ______________________________ R-sig-phylo mailing list -
R-sig-phylo@r-project.org stat.ethz.ch stat.ethz.ch Searchable archive at
www.mail-archive.com www.mail-archive.com [[alternative HTML version
deleted]] ------------------------------ Message: 6 Date: Thu, 5 Sep
2019 16:07:56 +0000 From: Cecile Ane <cecile....@wisc.edu> To: R Sig
Phylo Listserv <r-sig-phylo@r-project.org> Subject: Re: [R-sig-phylo]
model averaging using brownie.lite Message-ID:
<080ACBDA-BAE1-48C3-B451-E02CB Content-Type: text/plain;
charset="utf-8" Thanks Brian, great review, as always! To add one
bit: this paper looks at the effective sample size that should be used for BIC,
in the standard BM model (univariate). projecteuclid.org projecteuclid.org
It gives a formula that depends on the tree shape and branch lengths. Like what
Brian said: a pectinate tree would generally have a smaller effective sample
size than a symmetric tree, for the same number of taxa. The general formula
uses matrix, but the result should be something less than the number of taxa,
and greater than: # branches stemming from the root * ratio (total tree height
/ length of shortest branch stemming from the root). The effective sample size
should also be at least (total tree length / total tree height) for an
ultrametric tree. See end of section 2 for an example of BIC penalties using
effectives sample sizes. The bottom line is the same as what Brian said: -
it’s generally unknown what “sample size” should be used - in cases when we
know, the answer is complicated (it depends on the tree and on the model).
With multivariate data (multiple sites), the effective sample size for
univariate data (like number of taxa or something smaller) should be multiplied
by the number of sites, if the model assumes that sites are independent and
share the same evolutionary parameters. (consistent with what Brian said).
Cécile On Sep 5, 2019, at 9:55 AM, Brian O'Meara
<omeara.br...@gmail.com<mailto wrote: Sample size is a weird thing in
this area for AICc. For comparing DNA models in something like ModelTest,
number of sites is used, but for OU/BM models, we typically use number of
taxa. It's not resolved what's best. Posada and Buckley (2004,
doi.org doi.org have a discussion on this: Both in the AICc and the BIC
descriptions above, the total number of characters was used as an estimate of
sample size. However, effective sample sizes in phylogenetic studies are
poorly understood, and depend on the quantity of interest (Churchill et al.,
1992; Goldman, 1998; Morozov et al., 2000). Characters in an alignment will
often not be independent, so using the total number of characters as a
surrogate for sample size (Minin et al., 2003; Posada and Crandall, 2001b)
could be an overestimate. Using only the number of variable sites as an
estimate of sample size is a more conservative approach, but could be an
underestimate (note that all sites are used when estimating base frequencies
or the proportion of invariable sites). Indeed, sample size also depends on
the number of taxa. Importantly, sample size can have an effect on the outcome
of model selection with the AICc. In our example above, if we were to use the
number of variable characters (301 sites) as the sample size, instead of the
total number of characters (1927 sites), the best AICc model would not change,
but the second and third AICc models would exchange their rankings.
Furthermore, because the LRT, the AIC, and the BIC strategies rely on large
sample asymptotics, it is also important to decide when a sample should be
considered small. Although the AICc was derived under Gaussian assumptions,
Burnham et al. (1994) found that this second order expression performed well
in product multinomial models for open population capture-recapture. Burnham
and Anderson (2003, p. 66) suggest using this correction when the sample size
is small compared to the number of adjustable parameters, n/K < 40.
Alternatively, and because AICc converges to the AIC with increasing n/K
ratios, one could always use the AICc (D. Anderson, personal communications).
Phylogenetic characters are mostly discrete, and the unconstrained model in
phylogenetics is multinomial (Goldman, 1993). One may think of an alignment of
nucleotide characters as a large and sparse contingency table with 4^T bins,
where T is the number of taxa. For large sample asymptotics to hold in a
contingency table every cell should contain, in general, more than 5
observations (see Agresti, 1990, p. 49, 244–250), which gives a rule of thumb
of n/4^T > 5. Clearly, more research is needed on sample size in
phylogenetics. Beaulieu et al. (2018, doi.org doi.org note my COI as
I'm an author on this) did some simulations on a codon model testing
different ways of counting sample size (number of sites, number of taxa,
number of sites * number of taxa, etc.) and found that number of cells in the
matrix (number of sites * number of taxa) seemed to work best to approximate
Kullback-Liebler distance. For univariate models like that used in
brownie.lite, number of cells is equal to number of taxa (since there's
only one column): We note our use of AICc, as calculated in Burnham and
Anderson (2002, p. 66) and as opposed to the standard AIC, in the above model
comparisons. At the outset of our study it was unclear what the appropriate
sample size n is when comparing models of sequence evolution. Building upon
the work of Jhwueng et al. (2014), our simulations suggest that using the
number of taxa times the number of sites as the sample size correction
performed best as a small sample size correction for estimating
Kullback–Liebler (KL) distance in phylogenetic models (Supporting Materials).
This also has an intuitive appeal. In models that have at least some
parameters shared across sites and some parameters shared across taxa,
increasing the number of sites and/or taxa should be adding more samples for
the parameters to estimate. This is consistent considering how likelihood is
calculated for phylogenetic models: the likelihood for a given site is the sum
of the probabilities of each observed state at each tip, which is then
multiplied across sites. It is arguable that the conventional approach in
comparative methods is calculating AICc in the same way. That is, if only one
column of data (or “site”) is examined, as remains remarkably common in
comparative methods, when we refer to sample size, it is technically the
number of taxa multiplied by number of sites, even though it is referred to
simply as the number of taxa. I suspect this is still not a great
approximation. Compare a balanced tree (every internal node having two
descendants) with every internal branch length the same versus a pectinate
(caterpillar) tree where the two edges connecting to the root node are very
long and the other edges are all near zero. For the same number of taxa and
same number of sites, I bet the first tree has more meaningful data: the
pectinate tree with those branch lengths will likely have all but one of the
taxa having nearly identical states. So I think tree shape and branch lengths
should matter for this. I've done some preliminary analyses on this,
building on Beaulieu et al. (2018) and Jhwueng et al. (2014, doi.org doi.org
also note COI), but nothing definitive yet. It's also worth looking at
Ho and Ané (2014, doi.org doi.org who talk about AIC in the context of OU
shifts, but who get into sample size with shifts in a modified BIC that uses
taxa in different regimes as sample size (but again, univariate, so maybe
it's actually matrix size). I also probably am missing important work by
others -- my apologies if so. If you know of any, please let me know (and
probably Karla, too!). So, in summary.... yeah, what Liam said: number of
taxa, but it might be more complex. Best, Brian
______________________________ Brian O'Meara, brianomeara.info,
brianomeara.info, especially Calendar < brianomeara.info brianomeara.info
CV < brianomeara.info brianomeara.info and Feedback < brianomeara.info
brianomeara.info Professor, Dept. of Ecology & Evolutionary Biology, UT
Knoxville Associate Head, Dept. of Ecology & Evolutionary Biology, UT
Knoxville He/Him/His On Thu, Sep 5, 2019 at 10:00 AM Liam Revell
<liam.rev...@umb.edu<mailto:Li wrote: Dear Karla. In my opinion, it
is probably correct to use the number of tips on the tree as the sample size
for AICc when estimating the Brownian rate: as the number of independent
pieces of information is n-1, just like with an ordinary variance. For other
parameters in phylogenetic comparative analyses, the effective sample size may
be different, however. All the best, Liam Liam J. Revell Associate
Professor, University of Massachusetts Boston Profesor Asistente, Universidad
Católica de la Ssma Concepción web: faculty.umb.edu faculty.umb.edu
www.phytools.org www.phytools.org Academic Director UMass Boston Chile Abroad
(starting 2019): www.umb.edu www.umb.edu On 9/5/2019 9:49 AM, Karla Shikev
wrote: [EXTERNAL SENDER] Thanks so much, Liam! Just one quick follow-up
question: what do you suggest should be the sample size for transforming AIC
into AICc? the number of tips on the tree? Karla On Thu, Sep 5, 2019 at
10:27 AM Liam Revell <liam.rev...@umb.edu> wrote: Dear Karla. You
could try & create your own logLik method for the object class
"brownie.lite" as follows: ## method
logLik.brownie.lite<-function( lik<-setNames( c(
c( attr(lik,"df")<-c( lik } ## fit model
fit<-brownie.lite(tree,x) ## use it logLik(fit) AIC(fit) All the best,
Liam Liam J. Revell Associate Professor, University of Massachusetts Boston
Profesor Asistente, Universidad Católica de la Ssma Concepción web:
faculty.umb.edu faculty.umb.edu nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com Academic Director UMass Boston Chile
Abroad (starting 2019): www.umb.edu www.umb.edu On 9/5/2019 9:13 AM, Karla
Shikev wrote: [EXTERNAL SENDER] Dear all, I've been trying to use
brownie.lite to implement the tutorial available here (
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
) to calculate model-averaged rates of evolution and for model selection (1
versus 2 rates). However, the current version of phytools 0.6-99 won't
produce AICc estimates. Does anyone know a way around this? Any help would be
greatly appreciated. thanks a bunch, Karla [[alternative HTML
version deleted]] ______________________________ R-sig-phylo mailing list -
R-sig-phylo@r-project.org nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com Searchable archive at
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
[[alternative HTML version deleted]] ______________________________
R-sig-phylo mailing list - R-sig-phylo@r-project.org
nam01.safelinks.protection.outlook.com nam01.safelinks.protection.outlook.com
Searchable archive at nam01.safelinks.protection.outlook.com
nam01.safelinks.protection.outlook.com ______________________________
R-sig-phylo mailing list - R-sig-phylo@r-project.org stat.ethz.ch
stat.ethz.ch Searchable archive at www.mail-archive.com www.mail-archive.com
[[alternative HTML version deleted]] ______________________________
R-sig-phylo mailing list - R-sig-phylo@r-project.org<mail stat.ethz.ch
stat.ethz.ch Searchable archive at www.mail-archive.com www.mail-archive.com
[[alternative HTML version deleted]] ------------------------------
Message: 7 Date: Thu, 5 Sep 2019 09:40:39 -0700 From: Gregory Mutumi
<gmut...@gmail.com> To: "r-sig-phylo@r-project.org"
<r-sig-phylo@r-project.org> Subject: [R-sig-phylo] Rate of trait
evolution through time Message-ID: <CAA5DsKHi8f529xxfBox0u7wa18g6
Content-Type: text/plain; charset="utf-8" Dear All I want to
extract rates for about 100 bins across a phylogeny and plot the rate through
time. Can this be done in BTRtools or is there other packages to handle the
next steps?. I want something like the figure 1 b of the paper
'Ecomorphological diversification in squamates from conserved pattern of
cranial integration' - attached. SuppMat_Watanabe et al 2019
Ecomorphological di... < drive.google.com drive.google.com Watanabe et al
2019 Ecomorphological diversific... < drive.google.com drive.google.com I
managed to do this with BAMM and BAMMtools, but the problem is I can only use
1PC (please also find attached the plot I got). BayesPCM1 <
drive.google.com drive.google.com BayesPCM1.Log.txt < drive.google.com
drive.google.com BayesPCM1.Output.trees < drive.google.com
drive.google.com BayesPCM1.Schedule.txt < drive.google.com
drive.google.com BayesPCM1.VarRates.txt < drive.google.com
drive.google.com Please find attached output *'BayesTrait'* files for
one of the structures and also the txt data of the first 9 PCs. If I can get
an idea of what steps to take and which packages to use. I am very new to
Bayesian analyses. Thank you. Yours sincerely Gregory --------------
next part -------------- An HTML attachment was scrubbed... URL: <
stat.ethz.ch stat.ethz.ch -------------- next part -------------- A non-text
attachment was scrubbed... Name: Figure 7 - updated.tif Type: image/tiff
Size: 304104 bytes Desc: not available URL: < stat.ethz.ch stat.ethz.ch
------------------------------ Subject: Digest Footer
______________________________ R-sig-phylo mailing list
R-sig-phylo@r-project.org stat.ethz.ch stat.ethz.ch
------------------------------ End of R-sig-phylo Digest, Vol 140, Issue 3
******************************
[[alternative HTML version deleted]]
_______________________________________________
R-sig-phylo mailing list - R-sig-phylo@r-project.org
https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
Searchable archive at http://www.mail-archive.com/r-sig-phylo@r-project.org/
