AICc.flexsurvreg | R Documentation |
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 class 'flexsurvreg'
AICc(object, cens = TRUE, ...)
## S3 method for class 'flexsurvreg'
AICC(object, cens = TRUE, ...)
object |
Fitted model returned by |
cens |
Include censored observations in the sample size term
( |
... |
Other arguments (currently unused). |
This can be spelt either as AICC
or AICc
.
The second-order AIC of the fitted model.
Burnham, K. P., Anderson, D. R. (2002) Model Selection and Multimodel Inference: a practical information-theoretic approach. Second edition. Springer: New York.
BIC
, AIC
, BIC.flexsurvreg
, nobs.flexsurvreg
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