aic: Akaike Information Criterion

In nadiv: (Non)Additive Genetic Relatedness Matrices

Akaike Information Criterion

Description

Calculates AIC/AICc values, AIC differences, Likelihood of models, and model probabilities.

Usage

aic(logLik, fp, n = NULL)

Arguments

logLik	A vector of model log-Likelihoods
fp	A vector containing the numbers of free parameters of each model included in the logLik vector
n	An optional vector of sample sizes for each model. Used to calculate AICc (small sample un-biased AIC).

Details

Calculations and notation follows chapter 2 of Burnham and Anderson (2002).

Value

a list:

AIC

vector containing AIC/AICc (depending on value of n)

delta_AIC

vector containing AIC differences from the minimum AIC(c)

AIClik

vector containing likelihoods for each model, given the data. Represents the relative strength of evidence for each model.

w

Akaike weights.

Author(s)

[e-mail]

References

Burnham, K.P. and D.R. Anderson. 2002. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach, 2nd edn. Springer, New York.

Examples

   aic(c(-3139.076, -3136.784, -3140.879, -3152.432), c(8, 7, 8, 5)) 

nadiv documentation built on Dec. 8, 2022, 1:11 a.m.