[R] fit simple surface to 2d data?

[Prof Brian Ripley]

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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