GIC: Generalized Information Criterion (GIC) to compare models fit...

In RPANDA: Phylogenetic ANalyses of DiversificAtion

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 class 'fit_pl.rpanda'
GIC(object, ...)
  

Arguments

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

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.

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

Author(s)

J. Clavel

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

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)
      }
}

RPANDA documentation built on Oct. 24, 2022, 5:06 p.m.