For a model that was fitted by optimisation, compute the values of the objective function in a neighbourhood of the optimal value.
objsurf(x, …)# S3 method for dppm
objsurf(x, ..., ngrid = 32, ratio = 1.5, verbose = TRUE)
# S3 method for kppm
objsurf(x, ..., ngrid = 32, ratio = 1.5, verbose = TRUE)
# S3 method for minconfit
objsurf(x, ..., ngrid = 32, ratio = 1.5, verbose = TRUE)
Some kind of model that was fitted
by finding the optimal value of an objective function.
An object of class "dppm", "kppm" or "minconfit".
Extra arguments are usually ignored.
Number of grid points to evaluate along each axis.
Either a single integer, or a pair of integers.
For example ngrid=32 would mean a 32 * 32 grid.
Number greater than 1 determining the range of parameter values
to be considered. If the optimal parameter value is opt
then the objective function will be evaluated for
values between opt/ratio and opt * ratio.
Logical value indicating whether to print progress reports.
An object of class "objsurf" which can be
printed and plotted.
Essentially a list containing entries x, y, z
giving the parameter values and objective function values.
The object x should be some kind of model that was fitted
by maximising or minimising the value of an objective function.
The objective function will be evaluated on a grid of
values of the model parameters.
Currently the following types of objects are accepted:
an object of class "dppm" representing a
determinantal point process.
See dppm.
an object of class "kppm" representing a
cluster point process or Cox point process.
See kppm.
an object of class "minconfit" representing a
minimum-contrast fit between a summary function and its
theoretical counterpart.
See mincontrast.
The result is an object of class "objsurf" which can be
printed and plotted: see methods.objsurf.
# NOT RUN {
fit <- kppm(redwood ~ 1, "Thomas")
os <- objsurf(fit)
if(interactive()) {
plot(os)
contour(os, add=TRUE)
persp(os)
}
# }
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