R help - Jun 2003 - Question: nonlinear covariate terms in spatial regression

Hi all,

I am trying to model (continuous) spatial variation in a response variable 
as a function of one or more of several explanatory variables.  I am 
principally interested in obtaining some measure of the relative 
"importance" of the explanatory variables.  I have found several R 
libraries that are tailored to this sort of problem (geoR, geoRglm, gstat, 
etc.); however, as near as I can tell, the appropriate functions (e.g., 
krige.bayes in geoR), only allow for linear modelling of the large-scale 
variation.  In my case, covariate effects are probably 
nonlinear.  Moreover, although there is theory to suggest the general form 
of some of these relationships (e.g., "monotonically increasing"),
there is
no theory that specifies the specific functional form.  I have two questions:

(1) Are there R functions available that allow one to model nonlinear 
covariate effects on the large scale variation within a "universal
kriging"
type framework?

(2) Are there any (spatial or otherwise) regression models in R that could 
be used to fit models where only general restrictions are placed on the 
functional form?  I am thinking of something like a GAM, where the fit is 
constrained to be monotonically increasing, to be monotonically increasing 
with a negative second derivative, etc.

Thanks in advance.

Regards,
Sean


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Sean R. Connolly, PhD
Senior Lecturer
Department of Marine Biology
James Cook University
Townsville, QLD 4811
AUSTRALIA
Ph: 61 7 4781 4242
Fax: 61 7 4725 1570
http://www.jcu.edu.au/school/mbiolaq/mbiol/staff/sconnolly.html

VISIT THE NEW CENTRE FOR CORAL REEF BIODIVERSITY
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