[R] Loess with glm ?

[Luke Whitaker]

Hello,

I am wondering if there is an easy way to combine loess() with glm()
to produce a locally fitted generalised regression.

I have a data set of about 5,000 observations and 5 explanatory variables,
with a binary outcome. One of the explanatory variables (lets call it X)
is much more predictive than the others. A single glm() regression over
the entire data set produces rather poor results, so I have split the
data based on sub ranges of X, and performed a separate glm() regression
on each subset.

This produces much more satisfactory results, but the problem is that
at the boundaries, the result hyper-surfaces don't coincide.

I am using this model in a predictive role so that given a new observation
on the 5 explanatory variables, I want to predict the probability of a
positive outcome (actually whether a protein has a certain conformation
or not). At the boundary determined by the value of X, my prediction has
a discontinuity, which is not very satisfactory. My solution has been to
take a weighted average of the results of adjacent models for cases where X
is close to a boundary so as to smooth over the discontinuities. Although
this works, it seems rather simplistic and arbitrary in terms of choices
about how and where the weighed averages are computed.  It seems to me
that what I am doing is a kind of poor mans loess.

Can anyone suggest a better way to deal with this analysis ? I have only
a sketchy knowledge of loess.

Thanks,

Luke Whitaker
Inpharmatica



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