Paul Hiemstra
Wed, 01 Sep 2010 04:09:17 -0700
Hi Erik,IDW does not include a formal interpretation of the inverse distance power. You could seperate your dataset into a validation and interpolation set and try different idp's and see which one preforms best.
Another option would be to use kriging. Kriging fits the spatial dependence vs distance to the data. In my view this makes kriging, as long as the assumptions are honored, a preferable approach. The automap package provides easy acces to kriging by providing some wrapper code around gstat.
cheers, Paul On 08/30/2010 06:31 PM, Mudrak, Erika [EEOBS] wrote:
I used the gstat package to interpolate measurements of eight environmental
variables in a square 15.4 m x 15.4 m, and then I used model selection from
another package to build models of dependence of plant population locations on
those environmental variables. I used the idw() function to interpolate the
environmental variables. The model selection procedure defined which of the
eight variables helped to explain the patterns seen in my plant populations.
Are there any guidelines for the choice of the inverse distance weighting power
(idp)? I had been using idp=2, because it was the default, but for some
variables it made the surface look not very smooth. I have tried my models on
surfaces with other values of idp, and changing this parameter causes the model
selection procedure to arrive at different models.
Does anyone have any advice or guidelines about the choice of the ipd parameter, other
than "tweaking" it until the surfaces look smooth?
Thank you,
Erika Mudrak
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-- Drs. Paul Hiemstra Department of Physical Geography Faculty of Geosciences University of Utrecht Heidelberglaan 2 P.O. Box 80.115 3508 TC Utrecht Phone: +3130 253 5773 http://intamap.geo.uu.nl/~paul http://nl.linkedin.com/pub/paul-hiemstra/20/30b/770 _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo