ace estimates ancestral character states, and the associated
uncertainty, for continuous and discrete characters. If marginal
= TRUE, a marginal estimation procedure is used. With this method,
the likelihood values at a given node are computed using only the
information from the tips (and branches) descending from this node.
The present implementation of marginal reconstruction for discrete characters does not calculate the most likely state for each node, integrating over all the possible states, over all the other nodes in the tree, in proportion to their probability. For more details, see the Note below.
logLik, deviance, and AIC are generic functions
used to extract the log-likelihood, the deviance, or the Akaike
information criterion of a fitted object. If no such values are
available, NULL is returned.
anova is another generic function which is used to compare
nested models: the significance of the additional parameter(s) is
tested with likelihood ratio tests. You must ensure that the models
are effectively nested (if they are not, the results will be
meaningless). It is better to list the models from the smallest to the
largest.
ace(x, phy, type = "continuous", method = if (type == "continuous")
"REML" else "ML", CI = TRUE,
model = if (type == "continuous") "BM" else "ER",
scaled = TRUE, kappa = 1, corStruct = NULL, ip = 0.1,
use.expm = FALSE, use.eigen = TRUE, marginal = FALSE)
# S3 method for ace
print(x, digits = 4, ...)
# S3 method for ace
logLik(object, ...)
# S3 method for ace
deviance(object, ...)
# S3 method for ace
AIC(object, ..., k = 2)
# S3 method for ace
anova(object, ...)a vector or a factor; an object of class "ace" in the
case of print.
an object of class "phylo".
the variable type; either "continuous" or
"discrete" (or an abbreviation of these).
a character specifying the method used for
estimation. Four choices are possible: "ML", "REML",
"pic", or "GLS".
a logical specifying whether to return the 95% confidence intervals of the ancestral state estimates (for continuous characters) or the likelihood of the different states (for discrete ones).
a character specifying the model (ignored if method
= "GLS"), or a numeric matrix if type = "discrete" (see
details).
a logical specifying whether to scale the contrast
estimate (used only if method = "pic").
a positive value giving the exponent transformation of the branch lengths (see details).
if method = "GLS", specifies the correlation
structure to be used (this also gives the assumed model).
the initial value(s) used for the ML estimation procedure
when type == "discrete" (possibly recycled).
a logical specifying whether to use the package
expm to compute the matrix exponential (relevant only if
type = "d"). If FALSE, the function matexpo
from ape is used (see details). This option is ignored if
use.eigen = TRUE (see next).
a logical (relevant if type = "d"); if
TRUE then the probability matrix is computed with an eigen
decomposition instead of a matrix exponential (see details).
a logical (relevant if type = "d"). By default,
the joint reconstruction of the ancestral states are done. Set this
option to TRUE if you want the marginal reconstruction (see
details.)
the number of digits to be printed.
an object of class "ace".
a numeric value giving the penalty per estimated parameter;
the default is k = 2 which is the classical Akaike
information criterion.
further arguments passed to or from other methods.
an object of class "ace" with the following elements:
if type = "continuous", the estimates of the
ancestral character values.
if type = "continuous", the estimated 95%
confidence intervals.
if type = "continuous", model = "BM", and
method = "ML", the maximum likelihood estimate of the
Brownian parameter.
if type = "discrete", the maximum likelihood
estimates of the transition rates.
if type = "discrete", the standard-errors of
estimated rates.
if type = "discrete", gives the indices of
the rates in the rate matrix.
if method = "ML", the maximum log-likelihood.
if type = "discrete", the scaled likelihoods of
each ancestral state.
the function call.
If type = "continuous", the default model is Brownian motion
where characters evolve randomly following a random walk. This model
can be fitted by residual maximum likelihood (the default), maximum
likelihood (Felsenstein 1973, Schluter et al. 1997), least squares
(method = "pic", Felsenstein 1985), or generalized least
squares (method = "GLS", Martins and Hansen 1997, Cunningham et
al. 1998). In the last case, the specification of phy and
model are actually ignored: it is instead given through a
correlation structure with the option corStruct.
In the setting method = "ML" and model = "BM" (this used
to be the default until ape 3.0-7) the maximum likelihood
estimation is done simultaneously on the ancestral values and the
variance of the Brownian motion process; these estimates are then used
to compute the confidence intervals in the standard way. The REML
method first estimates the ancestral value at the root (aka, the
phylogenetic mean), then the variance of the Brownian motion process
is estimated by optimizing the residual log-likelihood. The ancestral
values are finally inferred from the likelihood function giving these
two parameters. If method = "pic" or "GLS", the
confidence intervals are computed using the expected variances under
the model, so they depend only on the tree.
It could be shown that, with a continous character, REML results in unbiased estimates of the variance of the Brownian motion process while ML gives a downward bias. Therefore the former is recommanded.
For discrete characters (type = "discrete"), only maximum
likelihood estimation is available (Pagel 1994) (see MPR
for an alternative method). The model is specified through a numeric
matrix with integer values taken as indices of the parameters. The
numbers of rows and of columns of this matrix must be equal, and are
taken to give the number of states of the character. For instance,
matrix(c(0, 1, 1, 0), 2) will represent a model with two
character states and equal rates of transition, matrix(c(0, 1,
2, 0), 2) a model with unequal rates, matrix(c(0, 1, 1, 1, 0,
1, 1, 1, 0), 3) a model with three states and equal rates of
transition (the diagonal is always ignored). There are short-cuts to
specify these models: "ER" is an equal-rates model (e.g., the
first and third examples above), "ARD" is an
all-rates-different model (the second example), and "SYM" is a
symmetrical model (e.g., matrix(c(0, 1, 2, 1, 0, 3, 2, 3, 0),
3)). If a short-cut is used, the number of states is determined from
the data.
By default, the likelihood of the different ancestral states of
discrete characters are computed with a joint estimation procedure
using a procedure similar to the one described in Pupko et al. (2000).
If marginal = TRUE, a marginal estimation procedure is used
(this was the only choice until ape 3.1-1). With this method, the
likelihood values at a given node are computed using only the
information from the tips (and branches) descending from this node.
With the joint estimation, all information is used for each node. The
difference between these two methods is further explained in
Felsenstein (2004, pp. 259-260) and in Yang (2006, pp. 121-126). The
present implementation of the joint estimation uses a ``two-pass''
algorithm which is much faster than stochastic mapping while the
estimates of both methods are very close.
With discrete characters it is necessary to compute the exponential of
the rate matrix. The only possibility until ape 3.0-7 was the
function matexpo in ape. If use.expm = TRUE
and use.eigen = FALSE, the function expm,
in the package of the same name, is used. matexpo is faster but
quite inaccurate for large and/or asymmetric matrices. In case of
doubt, use the latter. Since ape 3.0-10, it is possible to use
an eigen decomposition avoiding the need to compute the matrix
exponential; see details in Lebl (2013, sect. 3.8.3). This is much
faster and is now the default.
Since version 5.2 of ape, ace can take state uncertainty
for discrete characters into account: this should be coded with R's
NA only. More details:
https://www.mail-archive.com/r-sig-phylo@r-project.org/msg05286.html
Cunningham, C. W., Omland, K. E. and Oakley, T. H. (1998) Reconstructing ancestral character states: a critical reappraisal. Trends in Ecology & Evolution, 13, 361--366.
Felsenstein, J. (1973) Maximum likelihood estimation of evolutionary trees from continuous characters. American Journal of Human Genetics, 25, 471--492.
Felsenstein, J. (1985) Phylogenies and the comparative method. American Naturalist, 125, 1--15.
Felsenstein, J. (2004) Inferring Phylogenies. Sunderland: Sinauer Associates.
Lebl, J. (2013) Notes on Diffy Qs: Differential Equations for Engineers. https://www.jirka.org/diffyqs/.
Martins, E. P. and Hansen, T. F. (1997) Phylogenies and the comparative method: a general approach to incorporating phylogenetic information into the analysis of interspecific data. American Naturalist, 149, 646--667.
Pagel, M. (1994) Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters. Proceedings of the Royal Society of London. Series B. Biological Sciences, 255, 37--45.
Pupko, T., Pe'er, I, Shamir, R., and Graur, D. (2000) A fast algorithm for joint reconstruction of ancestral amino acid sequences. Molecular Biology and Evolution, 17, 890--896.
Schluter, D., Price, T., Mooers, A. O. and Ludwig, D. (1997) Likelihood of ancestor states in adaptive radiation. Evolution, 51, 1699--1711.
Yang, Z. (2006) Computational Molecular Evolution. Oxford: Oxford University Press.
MPR, corBrownian, compar.ou,
anova
Reconstruction of ancestral sequences can be done with the package
phangorn (see function ?ancestral.pml).
# NOT RUN {
### Some random data...
data(bird.orders)
x <- rnorm(23)
### Compare the three methods for continuous characters:
ace(x, bird.orders)
ace(x, bird.orders, method = "pic")
ace(x, bird.orders, method = "GLS",
corStruct = corBrownian(1, bird.orders))
### For discrete characters:
x <- factor(c(rep(0, 5), rep(1, 18)))
ans <- ace(x, bird.orders, type = "d")
#### Showing the likelihoods on each node:
plot(bird.orders, type = "c", FALSE, label.offset = 1)
co <- c("blue", "yellow")
tiplabels(pch = 22, bg = co[as.numeric(x)], cex = 2, adj = 1)
nodelabels(thermo = ans$lik.anc, piecol = co, cex = 0.75)
# }
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