Skip to contents

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, ...)



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).


The second-order AIC of the fitted model.


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.