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