[R] local regression using loess

[Bert Gunter]

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".

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