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parametric models [Was: Re: musings on performance]
From: |
John Darrington |
Subject: |
parametric models [Was: Re: musings on performance] |
Date: |
Wed, 10 May 2006 19:43:41 +0800 |
User-agent: |
Mutt/1.5.4i |
On Tue, May 09, 2006 at 05:36:16PM -0400, Jason Stover wrote:
This is something I want to take up soon. I have a rough plan below.
Please let me know how this sounds. SPSS can now do little of what I
suggest below. (But what I'm suggesting would make PSPP a good
model-building tool.)
I would like to make PSPP able to:
1. Save models for later use within PSPP. 'Later uses' include
combining them into other models, and assessing by comparing many
models, mostly by checking their performance on 'scratch' data.
'Later uses' might also include fitting other models that could use
some of the sufficient statistics (like sample means and covariance
matrices). Saving models would not take much work if I can use the pool
allocator to do so.
Here's where I'm going to start showing my ignorance of statistical
methods. What exactly do you mean by a "model"? How is it different
(or similar) to the data saved by SPSS's MATRIX subcommand?
2. Export models in some external formats so they can be used by another
program later. The first format I was thinking of was compilable C. I
suppose other formats like XML ought to be supported too, since SPSS
can export some models as XML. Right now, REGRESSION has some ugly
functions that let it write little C programs. I'd like to clean that
code up and move it to a place where other procedures could use
it.
To learn how to do numbers 1 and 2, I should write a modeling procedure
that fits a model quite different from that fit by REGRESSION, but one
whose purpose is, like regression, to find a function f(input) that
predicts some output. I was thinking of a neural network. Another
possibility is a regression tree. I don't want this next procedure to
resemble linear regression too closely, lest I inadvertently write
model-shuffling procedures closely tailored to manipulation of one
particular type of model.
If you go down the neural net path, then I would suggest that a radial
basis function net would be the thing to use.
Saving models requires a standard syntax, usable by any procedure that
fits a model to data, that tells PSPP to save that model. I think the
SAVE subcommand is a good candidate, as in this possibility:
REGRESSION /variables y x1 x2 /DEPENDENT y /SAVE model=m1
...but maybe something else would be better?
Like I mentioned above, it sounds similar to the SPSS /MATRIX
subcommand so maybe that would be the thing to use.
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