RPANDA (version 2.3)

ancestral: Estimation of traits ancestral states.

Description

Reconstruct the ancestral states at the root (and possibly for each nodes) of a phylogenetic tree from models fit obtained using the fit_t_XX functions.

Usage

ancestral(object, ...)

Value

a list with the following components

root

the reconstructed ancestral states at the root

nodes

the reconstructed ancestral states at each nodes (not yet implemented for all the methods)

Arguments

object

A model fit object obtained by the fit_t_XX class of functions.

...

Further arguments to be passed through (not used yet).

Author

J. Clavel

Details

ancestral reconstructs the ancestral states at the root and possibly for each nodes of a phylogenetic tree from the models fit obtained by the fit_t_XX class of functions (e.g., fit_t_pl, fit_t_comp and fit_t_env). Ancestral states are estimated using generalized least squares (GLS; Martins & Hansen 1997, Cunningham et al. 1998 ).

References

Clavel, J., Aristide, L., Morlon, H., 2019. A Penalized Likelihood framework for high-dimensional phylogenetic comparative methods and an application to new-world monkeys brain evolution. Syst. Biol. 68: 93-116.

Cunningham C.W., Omland K.E., Oakley T.H. 1998. Reconstructing ancestral character states: a critical reappraisal. Trends Ecol. Evol. 13:361-366.

Martins E.P., Hansen T.F. 1997. Phylogenies and the comparative method: a general approach to incorporating phylogenetic information into the analysis of interspecific data. Am. Nat. 149:646-667.

See Also

fit_t_pl, fit_t_env, phyl.pca_pl, GIC, gic_criterion

Examples

# \donttest{
if(require(mvMORPH)){
set.seed(1)
n <- 32 # number of species
p <- 31 # number of traits

tree <- pbtree(n=n) # phylogenetic tree
R <- Posdef(p)      # a random symmetric matrix (covariance)

# simulate a dataset
Y <- mvSIM(tree, model="BM1", nsim=1, param=list(sigma=R))

# fit a multivariate BM with Penalized likelihood
fit <- fit_t_pl(Y, tree, model="BM", method="RidgeAlt")

# Perform the ancestral states reconstruction
anc <- ancestral(fit)

# retrieve the scores
head(anc$nodes)
}
# }