fit simple surface to 2d data?
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 -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._