is about
is_specified_input_of
measurement datum
statistical model
Akaike information criterion
AIC
AIC(object, ..., k = 2)
from:
https://stat.ethz.ch/R-manual/R-devel/library/stats/html/AIC.html
Alejandra Gonzalez-Beltran
Orlaith Burke
Philippe Rocca-Serra
The Akaike information criterion (AIC) is a measure of the relative quality of a statistical model for a given set of data. As such, AIC provides a means for model selection. AIC is defined as:
AIC = 2K - 2log(L)
where K is the number of predictors and L is the maximized likelihood value.
AIC deals with the trade-off between the goodness of fit of the model and the complexity of the model. It is founded on information theory: it offers a relative estimate of the information lost when a given model is used to represent the process that generates the data. AIC does not provide a test of a model in the sense of testing a null hypothesis; i.e. AIC can tell nothing about the quality of the model in an absolute sense. If all the candidate models fit poorly, AIC will not give any warning of that.
http://en.wikipedia.org/wiki/Akaike_information_criterion
and
http://users.ecs.soton.ac.uk/jn2/teaching/aic.pdf
http://www.ncbi.nlm.nih.gov/pubmed/7791040
statistical model selection
ready for release