On 29-Apr-04 John Maindonald wrote: > This is, of course, not strictly about R. But if there should be > a decision to pursue such matters on this list, then we'd need > another list to which such discussion might be diverted.
A few of us have taken further discussion on this topic off-list, between ourselves. > [...] > R provides a lot of help for those who want a frequentist > interpretation, even to including by default the *, **, *** > labeling that some of us deplore. There is no similar help > for those who want at least the opportunity to place the > output from a modeling exercise in a Bayesian context of > some description. There is surely a strong argument for > the use of a more neutral form of default output, even to > the excluding of p-values, on the argument that they also > push too strongly in the direction of a frequentist > interpretative framework. I can do without the stars, but the p-values are handy (saves separately computing them if one wants to know what they are). > There seems, unfortunately, to be a dearth of good ideas > on how the assist the placing of output from modeling > functions such as R provides in an explicitly Bayesian > framework. Or is it, at least in part, that I am unaware of > what is out there? That, I guess, is the point of my > question to Frank. Is it just too technically demanding > to go much beyond trying to get users to understand > that a Bayesian credible interval can, if there is an > informative prior, be very different from a frequentist CI, > that they really do need to pause if there is an > informative prior lurking somewhere in the undergrowth? I think a good starting point would be the ability to extract the likelihood function from a model, perhaps by providing an "interrogation" method whereby the user can generate values of it for paramter-values submitted as arguments. This already exists in a few packages that I know about, e.g. Schafer's multiple imputation packages 'cat', 'norm, and 'mix' where, not only tucked away in FORTRAN code are there MCMC engines which sample the likelihood function, but there are also functions like 'loglik.mix' which return log-likelihood values directly. Best wishes, Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <[EMAIL PROTECTED]> Fax-to-email: +44 (0)870 167 1972 Date: 29-Apr-04 Time: 09:08:42 ------------------------------ XFMail ------------------------------ ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
