informationCriterion {maticce}R Documentation

Information criterion and weights for a set of models

Description

Returns AIC, AICc, and BIC values and weights for a set of models.

Usage

  informationCriterion(u = NULL, lnL = NULL, K, n = 1, names = NULL)
  ## S3 method for class 'hansenBatch':
  informationCriterion(hansenBatch)
  ## S3 method for class 'informationCriterion':
  print(x, ...)

Arguments

u A vector of deviances, indexed by model.
lnL A vector of log-likelihoods, indexed by model.
K A vector of degrees-of-freedom / number of free parameters, indexed by model.
n Sample size; for a phylogenetic comparative analysis, n = the number of tips.
names Optional vector of model names, indexed by model.
hansenBatch Output from runBatchHansen.
x Output from informationCriterion.
... Additional arguments to be passed along to print

Details

At the minimum, a vector of either the model log-likelihoods (lnL) or deviances (u = -2 * lnL) and a vector of number of free parameters for each model (K) must be provided for the function to work. If the sample size (n) is not provided, the function calculates AICc and BIC assuming n = 1. Information criterion statistics are calculated following Burnham and Anderson (2002).

Value

A list with the following vectors, all indexed by model number:

names Model names; if not provided, a vector of 1:length(u).
u Deviance.
K Degrees of freedom.
AIC Akaike information criterion.
AICc Small-sample AIC.
BIC Bayes information criterion.
AICwi AIC weight.
AICcwi AICc weight.
BICwi BIC weight.

Author(s)

Andrew Hipp ahipp@mortonarb.org

References

Burnham, K. P., and D. R. Anderson (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York.


[Package maticce version 0.9-2 Index]