I haven't had time to go through your extensive e-mail
in detail, but here are a couple of thoughts to be
going on with:
simple correlation: if you calculate the
non-co-located semi-variogram (or covariance function)
the apparent nugget effect is a direct estimate of
what the correlation between the two variables would
be if you had them co-located. This allows for the
spatial 'auto-correlation' as well as statistical
correlation. I (personally) favour a rank transform as
this is a well established way of finding a
correlation for non-Normal data.
With a rank transform, your zeroes would be given
arbitrary (randomised) ranks and you could do a few
repeats to see if this makes a lot of difference.
References for MUCK can be found at
Some packages (including EcoSSe) define 'distance' in
a specified module which is used by all of the
routines. This module can be replaced to allow the use
of an algebraic function of look up table for
distances. All routines then use that definition
instead of Euclidean distance.
Technically constructing a semi-variogram or other
spatial dependence analysis on the residuals from a
GLM (trend) surface is incorrect. However, we have all
been doing it very successfully for almost 30 years,
so I wouldn't worry about it over muchly. cf.
Practical Geostatistics 2000, Chapter 12.
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