Message-ID: <20010706172705.0b988ebe.gry@ll.mit.edu>
Date: 2001-07-06T21:27:05Z
From: george young
Subject: fit simple surface to 2d data?
I have an array of floating-point measurements on a square (5 by 5) 2d grid.
The data are nominally constant, and somewhat noisy.
I need to find any significant spatial trend, e.g. bigger on the
left, bigger in the middle, etc. I have many thousands of these data sets
that need to be scanned for 'interesting' spatial variations, selecting the
datasets that are beyond some criterion of flatness.
My thought was to fit a 2'nd order polynomial with least-squares or some
such metric, and scan for coefficients bigger than some cutoff. I think
a parabolic surface is probably as complex a surface as the small amount of data merits.
Is there functionality in R that would be appropriate?
Is there some other approach anyone would suggest for the general task?
I'm not very experienced in data crunching, so any suggestion would
be appreciated.
I don't mind committing a lot of cpu to the task, if that helps.
Thanks,
George Young
MIT Lincoln Laboratory
Lexington, Mass, USA
--
I cannot think why the whole bed of the ocean is
not one solid mass of oysters, so prolific they seem. Ah,
I am wandering! Strange how the brain controls the brain!
-- Sherlock Holmes in "The Dying Detective"
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