AICc(object)logLik method to extract the
corresponding log-likelihood or an object inheriting from class logLikWhile this is an exact result, it only applies in the very specific circumstances in which it was derived. However, as Burnham and Anderson (2002) point out, whenever the sample size is small some form of correction to the standard AIC is necessary, to the extent that they argue the AICc of Hurvich and Tsai (1989) should be used regardless of context unless a specific correction can be derived. In fact Burnham and Anderson (2004) go so far as to argue that it should be used irrespective of sample size as it tends to the standard AIC when n is large.
Hurvich, C. M. & Tsai, C.-L. (1989). Regression and Time Series Model Selection in Small Samples. Biometrika, 76, 297-307
Burnham, K. P. & Anderson, D. R. (2002). Model Selection and Multimodel Inference: a Practical Information-theoretic Approach. Springer
Burnham, K. P. & Anderson, D. R. (2004). Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods Research, 33, 261-304
data(MTB)
fit <- sme(MTB[MTB$variable==6031,c("y","tme","ind")])
AICc(fit)
Run the code above in your browser using DataLab