R help - Dec 2007 - 2/3d interpolation from a regular grid to another regular grid

Hello R users,

I have numerical data sampled on two grids, each one shifted by 0.5  
from the other.

For example:

grid1 = expand.grid(x=0:3, y=0.5:2.5)
grid2 = expand.grid(x=0.5:2.5, y=0:3)
gridFinal = expand.grid(x=0.5:2.5, y=0.5:2.5)

plot(gridFinal, xlim=c(0,3), ylim=c(0,3), col="black", pch=19)
points(grid1, xlim=c(0,3), ylim=c(0,3), col="red", pch=19)
points(grid2, xlim=c(0,3), ylim=c(0,3), col="blue", pch=19)

I would like to interpolate the quantities on grid1 (red) and grid2  
(blue) on the same grid (black). This scenario is very common in  
geophysical data and models. I only found:
- functions in package akima which are designed for irregular grids
- krigging in package fields, which also requires irregular spaced data
- approx or spline which works in 1D and which I could apply line by  
line and column by column and use a mean of both estimates
I am sure there are plenty of functions already available to do this  
but searching R-help and the packages site did not help. Pointer to a  
function/package would be highly appreciated.

Eventually, the same scenario will occur in 3D so if the function is  
3D capable it would be a plus (but I am sure the solution to this is  
generic enough to work in nD)

Thank you in advance.

JiHO
---
http://jo.irisson.free.fr/

> - krigging in package fields, which also requires irregular spaced data
That kriging requires irregularly spaced data sounds new to me ;) It
cannot be, you misread something (I feel free to say that even if I
never used that package).
It can be tricky doing kriging, though, if you're not comfortable with
a little bit of geostatistics. You have to infer a variogram model for
each data set; you possibly run into non-stationarity or anisotropy,
which are indeed very well treated (maybe at best) by kriging in one
of its forms, but ... it takes more than this list to help you then;
basically kriging requires modelling, so it is often very difficult to
set up an automatic procedure. I can reccomend kriging if the spatial
variability of your data (compared to grid refinement) is quite
important.

In other simple cases, a wheighted mean using the (squared) inverse of
the distance as wheight and a spherical neighbourhood could be the
simpliest way to perform the interpolation.