informationCriterion {maticce} | R Documentation |
Returns AIC, AICc, and BIC values and weights for a set of models.
informationCriterion(u = NULL, lnL = NULL, K, n = 1, names = NULL) ## S3 method for class 'hansenBatch': informationCriterion(hansenBatch) ## S3 method for class 'informationCriterion': print(x, ...)
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
|
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).
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. |
Andrew Hipp ahipp@mortonarb.org
Burnham, K. P., and D. R. Anderson (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York.