The GIC allows comparing models fit by Maximum Likelihood (ML) or Penalized Likelihood (PL).
# S3 method for fit_pl.rpanda
GIC(object, ...)
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
An object of class "fit_pl.rpanda". See ?fit_t_pl
Options to be passed through.
J. Clavel
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.
gic_criterion
,
fit_t_pl
,
mvgls
# \donttest{
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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