The GIC allows comparing models fit by Maximum Likelihood (ML) or Penalized Likelihood (PL).
# S3 method for fit_pl.rpanda
GIC(object, ...)An object of class "fit_pl.rpanda". See ?fit_t_pl
Options to be passed through.
a list with the following components
the log-likelihood estimated for the model with estimated parameters
the GIC criterion
the value of the bias term estimated to compute the GIC
GIC allows comparing the fit of various models estimated by Penalized Likelihood (see ?fit_t_pl). It's a wrapper to the gic_criterion function.
Konishi S., Kitagawa G. 1996. Generalised information criteria in model selection. Biometrika. 83:875-890.
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.
# NOT RUN {
require(mvMORPH)
set.seed(1)
n <- 32 # number of species
p <- 40 # 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))
fit1 <- fit_t_pl(Y, tree, model="BM", method="RidgeAlt")
fit2 <- fit_t_pl(Y, tree, model="OU", method="RidgeAlt")
GIC(fit1); GIC(fit2)
# }
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