R OO is documented for S3 classes under section 5 (Object-oriented programming) in the R language definition.
I guess the issue is somewhat philosophial as to how you use it. R philosophy _mostly_ separates data from operations on data, so the OO model provides classes for data and essentially separate methods that apply to those classes. This is the kind of model sometimes called a 'visitor pattern'. An alternative is to include operations on the data within the data object, which sometimes has advantages if you want to simplify the look of code for things like display (instead of a display method for each class, one effectively sends a mesage to any object of the form "display yourself here"). In practice, of course, one ends up writing class-specific operations code; the difference is pretty much where it's stored. On balance there seems to me a rationale for a statistician to separate data from the operations formed on it; one collects and curates data carefuly, so it as a kind of lifecycle of its own that is unrelated to mathematical operations performed on it. But I have allowed _data_ objects to include functions or at least function names when it is a necessary part of the description of the data. For example, in some of our interlaboratory studies labs give uncertainty information in the form of a variance or interval, but may additionally tell us what the assumed distribution is (eg Normal, t, lognormal etc). It then makes sense to have the distribution as part of the data. For these functions, the root name (norm, t, etc)_ suffices in conjunction with do.call, but to generalise completely, one can consider allowing a user to specify the distribution as (say) some arbitrary density function or density/probability family. (It's pretty rare that we'd need that, but hey - thinking ahead and all that). That would generate data which in part consisted of a function describing the (assumed) associated distribution. Steve Ellison >>> Ted Harding <ted.hard...@manchester.ac.uk> 12/05/2010 22:48:17 >>> Greetings All, Out of curiosity, I've just done a very primitive experiment: Obj <- list(Fun=sum, Dat=c(1,2,3,4)) Obj$Fun(Obj$Dat) # [1] 10 That sort of thing (much more sophisticated) must be documented mind-blowingly somewhere. Where? Where I stand right now: The above (and its immediately obvious generalisations, like Obj$Fun<-cos) is all I know about it so far. Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <ted.hard...@manchester.ac.uk> Fax-to-email: +44 (0)870 094 0861 Date: 12-May-10 Time: 22:48:14 ------------------------------ XFMail ------------------------------ ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ******************************************************************* This email and any attachments are confidential. Any use...{{dropped:8}} ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
