I'm looking for the best way to do the following:
     run a set of GAM models, and then make predictions with new data.

My problem is the size of the gam model object, I would like to strip it
down to the bare minimum of information needed to apply the model to new
data.  For example, if this were a linear model, I would just keep the
betas. If this were an ordinary spline fit, I think I would just need the
coefficients and the basis generating function.

I've been looking at Chapter 5 (p243-247) in Wood (2006) "GAMs: An
Introduction with R", and I've been trying to understand what is needed for
the smooth.construct and the predict.gam(type='lpmatrix') functions.

It's possible to generate a new prediction matrix with predict(gm, newdata,
type='lpmatrix), but the entire model object is needed as input.  I looked
into it further, and the predict method (using type='lpmatrix") depends on
the following model components:
    object$model
    object$terms
    object$coefficients
    object$contrasts
    object$xlevels
    object$pterms
    object$nsdf
    object$smooth
    object$Xcentre

Stripping out the other object parts saves some space, but the "smooth" part
seems to still be storing all the original data.  Isn't there some way that
I can just use the coefficients?

Thanks,

Gene

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