Fits a multi-rate Brownian motion evolution model using maximum likelihood.
brownie.lite(tree, x, maxit=2000, test="chisq", nsim=100, se=NULL, ...)An object of class "brownie.lite" containing the following elements:
is the rate, \(\sigma^2\), for a single-rate model. This is usually the "null" model.
is the estimated state at the root node for the single rate model.
variance on the single rate estimator - obtained from the Hessian.
log-likelihood of the single-rate model.
number of parameters in the single rate model (always 2).
is a length p (for p rates) vector of BM rates (\(\sigma_1^2\), \(\sigma_2^2\), and so on) from the multi-rate model.
is the estimated state at the root node for the multi-rate model.
p x p variance-covariance matrix for the p rates - the square-roots of the diagonals should give the standard error for each rate.
log-likelihood of the multi-rate model.
number of parameters in the multi-rate model (p+1).
P-value for a likelihood ratio test against the \(\chi^2\) distribution; or
P-value for a likelihood ratio test against a simulated null distribution.
logical value indicating if the likelihood optimization converged.
a phylogenetic tree either as an object of class "phylo" or "simmap". (See read.simmap, make.simmap, or paintSubTree for more details about the latter object class.)
a vector of tip values for species. names(x) should be the species names.
an optional integer value indicating the maximum number of iterations for optimization - may need to be increased for large trees.
an optional string indicating the method for hypothesis testing - options are "chisq" or "simulation".
number of simulations (only used if test="simulation").
a vector containing the standard errors for each estimated mean in x.
optional arguments.
Liam Revell liam.revell@umb.edu
This function takes an object of class "phylo" or class "simmap" with a mapped binary or multi-state trait (see read.simmap) and data for a single continuously valued character. It then fits the Brownian rate variation ("noncensored") model of O'Meara et al. (2006; Evolution). This is also the basic model implemented in Brian O'Meara's Brownie software.
Sampling error in the estimation of species means can also be accounted for by assigning the vector se with the species specific sampling errors for x.
O'Meara, B. C., C. Ane, M. J. Sanderson, and P. C. Wainwright (2006) Testing for different rates of continuous trait evolution using likelihood. Evolution, 60, 922-933.
Revell, L. J. (2024) phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things). PeerJ, 12, e16505.
brownieREML, evol.vcv, ratebytree
## load data from Revell & Collar (2009)
data(sunfish.tree)
data(sunfish.data)
## extract character of interest
buccal.length<-setNames(sunfish.data$buccal.length,
rownames(sunfish.data))
## fit model
multiBM.fit<-brownie.lite(sunfish.tree,
buccal.length)
print(multiBM.fit)
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