
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

	[[alternative HTML version deleted]]

