IC {paleoTS}R Documentation

Compute information criterion scores and Akaike weights for evoltuionary models

Description

These functions compute information criteria (IC) or Akaike weights based on information scores (akaike.wts).
Function IC is used internally by the optimization functions and generally will not need to be called directly by the user.

Usage

IC(w, logL = NULL, K = NULL, n = NULL, meth = c("AICc", "AIC", "BIC"))
akaike.wts(aa)

Arguments

w output from an optimization such as opt.GRW
logL log-likelihood
K the number of free parameters
n sample size for AICc and BIC calculations
meth which information criterion to compute; one of AIC, AICc, or BIC
aa vector of AIC or AICc values used to compute Akaike weights

Details

Function IC can take two kinds of arguments. The easiest is to send it the output of any of the paleoTS optimizations, in which case the function will automaically extract the necessary information. Alternatively, the log-likelihoods, number of parameters and sample size can be passed explicitly.
Function akaike.wts takes a vector of AIC or AICc values and computes corresponding Akaike weights.

Value

the computed information criterion, or a vector of Akaike weights

Author(s)

Gene Hunt

References

Hunt, G. 2006. Fitting and comparing models of phyletic evolution: random walks and beyond. Paleobiology32:578–601.

See Also

opt.GRW

Examples

 x <- sim.GRW(ns=40, ms=0.1, vs=0.1)
 m1<- opt.GRW(x)	
 m2<- opt.URW(x)
 m3<- opt.Stasis(x)
 akaike.wts(c(m1$AICc, m2$AICc, m3$AICc))  # it is easier to use fit3models()
	

[Package paleoTS version 0.3-1 Index]