Second-order or "corrected" Akaike information criterion, often
known as AICc (see, e.g. Burnham and Anderson 2002). This is
defined as -2 log-likelihood + (2*p*n)/(n - p -1), whereas
the standard AIC is defined as -2 log-likelihood + 2*p,
where p is the number of parameters and n is the
sample size. The correction is intended to adjust AIC for
small-sample bias, hence it only makes a difference to the result
for small n.
# S3 method for flexsurvreg
AICc(object, cens = TRUE, ...)# S3 method for flexsurvreg
AICC(object, cens = TRUE, ...)
The second-order AIC of the fitted model.
Fitted model returned by flexsurvreg
(or flexsurvspline).
Include censored observations in the sample size term
(n) used in this calculation. See
BIC.flexsurvreg for a discussion of the issues
with defining the sample size.
Other arguments (currently unused).
This can be spelt either as AICC or AICc.
Burnham, K. P., Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical information-theoretic approach. Second edition. Springer: New York.