This is a convenient way to collect diagnostic information
for several different point process models fitted to the same
point pattern dataset, or for point process models of the same form fitted to
several different datasets, etc. The first argument, object, is usually a list of
fitted point process models
(objects of class "ppm"), obtained from the
model-fitting function ppm.
For convenience, object can also be a list of point patterns
(objects of class "ppp").
In that case, point process models will be fitted to
each of the point pattern datasets,
by calling ppm using the arguments
trend (for the first order trend),
interaction (for the interpoint interaction)
and rbord (for the erosion distance in the border correction
for the pseudolikelihood). See ppm for details
of these arguments.
Alternatively object can be a single point pattern
(object of class "ppp") and one or more of the arguments
trend, interaction or rbord
can be a list. In this case, point process models will be fitted to
the same point pattern dataset, using each of the model specifications
listed.
The diagnostic function Fun will be applied to each of the
point process models. The results will be collected into a single
function value table. The modelnames are used to label the
results from each fitted model.