BMhyb (version 1.5.2)

AICc: AICc

Description

Calculates the second order Akaike's information criterion score for models of interest.

Usage

AICc(n, k, LogLik)

Arguments

n

number of taxa for the given phylogenetic tree. It represents the sample size(the number of species on the tip of phylogeny).

k

number of free parameters in the model.

LogLik

the minimum of the negative log-likelihood value obtained by optimizing the likelihood function.

Value

The AICc values.

Details

'AICc' is a function to compute the AICc values and is valid to select among different models. \(AICc = 2*n*k/(n-k-1) -2 log L\) where \(L\) is the maximum likelihood for the model.

References

Burnham, K.P., and D.R. Anderson. 2004. Model selection and inference: a practical information-theoretic approach. Sec. Ed. Springer, New York.

Examples

# NOT RUN {
  #assign the size 
	n<-5
	#assign the number of parameter
	k<-3
	#assign the negative log likelihood value.
	LogLik<- -2
	#compute the AICc score
	AICc(n,k,LogLik)
	# result AICc value of 26.
	
# }