mvMORPH (version 1.1.5)

ancestral: Estimation of traits ancestral states.

Description

Reconstruct the ancestral states at each node of a phylogenetic tree from models fit obtained using the mvgls function. For models of the class mvXX this is a wrapper to the function estim

Usage

ancestral(object, ...)

Arguments

object

A model fit object obtained by the mvgls function.

...

Further options to be passed through. For instance, if a regression model is used, values for the predictor(s) at each node of the tree should be given in a matrix to the newdata argument. If a model of the type mvXX is used, the argument tree and data should be provided like in estim function.

Value

a matrix of reconstructed ancestral states for each node (note that the numerotation of the ancestral states starts at "N+1" [for the root], where N is the number of species in the tree)

Details

ancestral is an S3 method that reconstruct the ancestral states at each nodes of a phylogenetic tree from the models fit obtained by the mvgls function (Clavel et al. 2019). Ancestral states are estimated using generalized least squares (GLS; Martins & Hansen 1997, Cunningham et al. 1998 ). Note that when a regression model (rather than an intercept only model of the form Y~1) is provided, an argument "newdata" with a matrix of regressor values for each node should be provided (similar to what is done in the "predict" function).

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

mvgls, estim, predict.mvgls

Examples

# NOT RUN {
set.seed(1)
n <- 32 # number of species
p <- 5 # number of traits

tree <- pbtree(n=n, scale=1) # phylogenetic tree
R <- crossprod(matrix(runif(p*p), ncol=p)) # a random covariance matrix
# simulate a BM dataset
Y <- mvSIM(tree, model="BM1", nsim=1, param=list(sigma=R, theta=rep(0,p))) 
data=list(Y=Y)

fit <- mvgls(Y~1, data=data, tree, model="BM", method="LL")

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

# retrieve the ancestral states
head(anc)

# }