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 class 'ace':
print(x, digits = 4, ...)
## S3 method for class 'ace':
logLik(object, ...)
## S3 method for class 'ace':
deviance(object, ...)
## S3 method for class 'ace':
AIC(object, ..., k = 2)
## S3 method for class 'ace':
anova(object, ...)"ace" with the following elements:type = "continuous", the estimates of the
ancestral character values.type = "continuous", the estimated 95%
confidence intervals.type = "continuous", model = "BM", and
method = "ML", the maximum likelihood estimate of the
Brownian parameter.type = "discrete", the maximum likelihood
estimates of the transition rates.type = "discrete", the standard-errors of
estimated rates.type = "discrete", gives the indices of
the rates in the rate matrix.method = "ML", the maximum log-likelihood.type = "discrete", the scaled likelihoods of
each ancestral state.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 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 second 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 matexpo in 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
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.
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
?ancestral.pml).
### 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)Run the code above in your browser using DataCamp Workspace