Continuous Ancestral Character Estimation
This function estimates ancestral character states, and the associated
uncertainty, for continuous characters. It mainly works as the
ace function, from which it differs in the fact that
optimisations are not performed by numerical algorithms but through
reconstruct(x, phyInit, method = "ML", CI = TRUE)
- a vector or a factor.
- an object of class
- a character specifying the method used for
estimation. Three choices are possible:
- a logical specifying whether to return the 95% confidence intervals of the ancestral state estimates.
The default model is Brownian motion where characters evolve randomly
following a random walk. This model can be fitted by maximum
likelihood (Felsenstein 1973, Schluter et al. 1997 - the default),
residual maximum likelihood, or generalized least squares
method = "GLS", Martins and Hansen 1997, Cunningham et
- a list with the following elements:
ace the estimates of the ancestral character values. CI95 the estimated 95% confidence intervals. sigma2 if
method = "ML", the maximum likelihood estimate of the Brownian parameter.
method = "ML", the maximum log-likelihood.
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.
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.
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.
Royer-Carenzi, M. and Didier, G. (2014) Comparison of ancestral state reconstruction methods for continuous characters under directional evolution. Submitted.
Reconstruction of ancestral sequences can be done with the package
### Some random data... data(bird.orders) x <- rnorm(23) ### Compare the three methods for continuous characters: reconstruct(x, bird.orders) reconstruct(x, bird.orders, method = "REML")