From: Scionforbai <scionforbai_at_gmail.com>

Date: Wed, 5 Dec 2007 16:47:34 +0100

R-help_at_r-project.org mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Wed 05 Dec 2007 - 15:50:49 GMT

Date: Wed, 5 Dec 2007 16:47:34 +0100

> I just read the description in ?Krig in the package fields which says:

*> " Fits a surface to irregularly spaced data. "
*

Yes, that is the most general case. Regular data location is a subset of irregular. Anyway, kriging, just one g, after the name of Danie Krige, the south african statistician who first applied such method for minig survey.

> My problem is simpler

...

> So it is really purely numerical.

...

> I just hoped that R had that already coded ...

Of course R has ... ;) If your grids are really as simple as the example you posted above, and you have a really little variability, all you need is a "moving average", the arithmetic mean of the two nearest points belonging to grid1 and grid2 respectively. I assume that your regularly shaped grids are values stored in matrix objects.

The functions comes from the "diff.default" code (downloading the R source code, I assure, is worth):

my.interp <- function(x, lag = 1)

{

g1 <- apply(grid1val,1,my.interp)

g2 <- apply(grid2val,2,my.interp)

If you want the mean from 4 points, you apply once more with lag=3,
cbind/rbind to the result columns/rows o NAs, and you calculate the
mean of the points of the two matrixes.

This is the simplest (and quickest) moving average that you can do.
For more complicated examples, and for 3d, you have to go a little
further, but the principle holds.

ScionForbai

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Wed 05 Dec 2007 - 15:50:49 GMT

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