On Aug 25, 2011, at 12:38 AM, Andra Isan wrote:
Hi All,
I have a set of features of size p and I would like to separate my
feature space into two sets so that p = p1 + p2, p1 is a set of
features and p2 is another set of features and I want to fit a glm
model for each sets of features separately.
You will be extracting the parameters and creating a summary variable
for each model? That sounds pretty straight-forward. That's not the
point you have questions about, right? Let's call teh functions that
you create p1pred and p2pred.
Then I want to combine the results of two glm models with a
parameter beta. For example, beta * F(p1) + (1-beta) * F(p2) where
F(p1) is a learned model for feature set p1 and F(p2) is the learned
model for feature set p2. Is there any way to do that in R?
In any GLM fitting program that provides an offset term (as does R's
glm(.) ), you can construct:
y = beta1*(p1pred -p2pred) + offset(p2pred)
# you would create the difference score first or wrap the difference
of predictions in the I() function.
This has the same fitted values as would:
y = beta1*p1pred + (1-beta2)*p2pred
I'm not sure about the inferential statistics. Seems to me that they
would be acceptable, but I am not a statistician. Have you looked at
the 'flexmix' package? I suspect its got that all mapped out in the
vignette. If it can model difference components with varying
distributions, it should be able to model "parceled features". Too bad
I don't understand the theoretical presentation.
http://finzi.psych.upenn.edu/R/library/flexmix/doc/regression-examples.pdf
There is a package called mixtools which can fit a mixture of two
regression models but it does not separate the features. I would
also like to separate features and fit a model for each feature set
and then combine them.
Thanks,
Andra
--
David Winsemius, MD
West Hartford, CT
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