[R-sig-Geo] model fitting of randomly generated data in spatstat

[Robert Leaf]

I was generating some data for analysis and was curious to see if we could
fit a “MatClust” model using the function *spatstat*::kppm to some of our
observed data. As a first cut, and to see if we get values that conform to
our expectations, I fit models to simulated data and was curious about the
results. I am hoping that the group can help me understand the departures
from expecations.

Is it reasonable that the kppm function should return parameters values
that are similar to the those that generated the data?

We are not getting value that are anywhere close to what we would expect.

library(*spatstat*)
(point.vals <- rMatClust(kappa = 2, r = 2, mu = 2000)) # generate random
points

if (point.vals$n > 0) { # some realizations of the model return .ppp
variables of with no data
print(point.vals$n)
plot(point.vals)
kppm(point.vals, ~1, "MatClust") } # stationary MatClust fitting

I am sure that I am missing something here? I would appreciate any input
that group members have to help us with this. Thanks in advance, Robert

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