Hello,

I have more of a conceptual question so pardon the lack of simulated code.

I am in the process of performing Regression Kriging using proportional
data ("cbind" response in R, family=binomial).  The tricky part is that I
am using field plot data to fit the GLM, and then apply my returned
binomial "p" (probability of infection) to an external, spatially-explicit
data set of mapped "n"(the number of trees in a hectare, regardless of
infection). I then multiply these two parameters of the binomial
distribution (n*p)-- in the form of two separate overlapping raster grids--
to spatially predict the number of infected trees per hectare.

However, I now have to interpolate my model residuals in order to implement
Regression Kriging. Yet, these residuals come from plot-level data and will
be very small (e.g., 5-20 trees either over- or under-predicted) compared
to what the residuals should be at the hectare level (ranging upwards of
hundreds of trees being infected).

*MY QUESTION IS AS FOLLOWS:*  If I know the plot size in relation to a
hectare (e.g., plots from one monitoring network are 1/2o of a hectare),
can I just use brute-force to upscale these residuals to the hectare level?
That is, if the model shows that 5 trees were overpredicted in a given plot
that overlays one of the 1-ha grids in my model, can I just say 5*20=100
trees overpredicted in that hectare?

Sarah

<http://webpages.uncc.edu/%7Eshaas1/index.htm>

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