R help - Jul 2001 - 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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On Fri, 6 Jul 2001, george young wrote:
> 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?
Trend surfaces in package spatial do that, and I would rather do an anova,
which Roger Bivand has kindly contributed.
> 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.
That's more or less what I would do, the anova bit being the difference.


-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272860 (secr)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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