RPANDA (version 2.3)

GIC.fit_pl.rpanda: Generalized Information Criterion (GIC) to compare models fit by Maximum Likelihood (ML) or Penalized Likelihood (PL).

Description

The GIC allows comparing models fit by Maximum Likelihood (ML) or Penalized Likelihood (PL).

Usage

# S3 method for fit_pl.rpanda
GIC(object, ...)

Value

a list with the following components

LogLikelihood

the log-likelihood estimated for the model with estimated parameters

GIC

the GIC criterion

bias

the value of the bias term estimated to compute the GIC

Arguments

object

An object of class "fit_pl.rpanda". See ?fit_t_pl

...

Options to be passed through.

Author

J. Clavel

Details

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.

References

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.

See Also

gic_criterion, fit_t_pl mvgls

Examples

# \donttest{
if(require(mvMORPH)){
test = FALSE
if(test){
      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)
      }
}
# }