Actually, loess is much more than an "interpolant". I wouldn't even call it that. It is a local regression technique that comes with all the equipment you get in classical regression. But it is meant for normal-like errors, which is not what you have.
-- This is misleading. The local smoother part makes a big difference. For example df (and standard tests, consequently)are not defined as in conventional multiple regression (though the enp argument gives you something like df). Also, it was specifically designed for _non_-normal errors -- specifically, long-tailed distributions -- via use of the "symmetric" family argument which fits via a re-descending M-estimator not Gaussian likelihood = least squares. But you are certainly free to characterize it as you think appropriate if you do not think "interpolant" is reasonable... -- Bert I would recommend that you take a look at the locfit package. It fits local likelihood models. I've never tried it with binary data, but if y is your 0/1 response and x is a covariate, you might try something like: locfit(y ~ x, ..., family="binomial") If you have a good library at your disposal, try picking up Loader's book "Local Regression and Likelihood". ______________________________________________ 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. ______________________________________________ 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.