GIC function

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

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

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

Arguments

• object: An object of class "fit_pl.rpanda". See ?fit_t_pl
• ...: Options to be passed through.

Returns

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

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.

Author(s)

J. Clavel

See Also

gic_criterion, fit_t_pl

mvgls

Examples

if(require(mvMORPH)){

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)
      }
}