Data from: A penalized likelihood framework for high- dimensional phylogenetic comparative methods and an application to new-world monkeys brain evolution

Working with high-dimensional phylogenetic comparative datasets is
challenging because likelihood-based multivariate methods suffer from low
statistical performances as the number of traits p approaches the number
of species n and because some computational complications occur when p
exceeds n. Alternative phylogenetic comparative methods have recently been
proposed to deal with the large p small n scenario but their use and
performances are limited. Here we develop a penalized likelihood framework
to deal with high-dimensional comparative datasets. We propose various
penalizations and methods for selecting the intensity of the penalties. We
apply this general framework to the estimation of parameters (the
evolutionary trait covariance matrix and parameters of the evolutionary
model) and model comparison for the high-dimensional multivariate Brownian
(BM), Early-burst (EB), Ornstein-Uhlenbeck (OU) and Pagel’s lambda models.
We show using simulations that our penalized likelihood approach
dramatically improves the estimation of evolutionary trait covariance
matrices and model parameters when p approaches n, and allows for their
accurate estimation when p equals or exceeds n. In addition, we show that
penalized likelihood models can be efficiently compared using Generalized
Information Criterion (GIC). We implement these methods, as well as the
related estimation of ancestral states and the computation of phylogenetic
PCA in the R package RPANDA and mvMORPH. Finally, we illustrate the
utility of the new proposed framework by evaluating evolutionary models
fit, analyzing integration patterns, and reconstructing evolutionary
trajectories for a high-dimensional 3-D dataset of brain shape in the New
World monkeys. We find a clear support for an Early-burst model suggesting
an early diversification of brain morphology during the ecological
radiation of the clade. Penalized likelihood offers an efficient way to
deal with high-dimensional multivariate comparative data.