B.varcount(model, B, ..., dimyx = NULL)"ppm", "kppm" or "dppm")."owin" specifying the region in
which the points are counted.
Alternatively a pixel image (object of class "im")
or a function of spatial coordinates specifying a numerical weight
for eacB when it is a function.as.mask."ppm"B according to the model.
It can also calculate the variance of a sum of weights attached
to each random point. The model should be a fitted point process model
(object of class "ppm", "kppm" or "dppm").
Bis a window, this command calculates the variance
of the number of points falling inB, according to the
fittedmodel. If themodeldepends on spatial covariates other than the
Cartesian coordinates, thenBshould be a subset of the
domain in which these covariates are defined.
B is a pixel image,
this command calculates the variance of
$T = \sum_i B(x_i)$,
the sum of the values of B over all random points
falling in the domain of the image.
If the model depends on spatial covariates other than the
Cartesian coordinates, then the domain of the pixel image,
as.owin(B), should be a subset of the domain in which these
covariates are defined.
B is a function(x,y) or function(x,y,...)
this command calculates the variance of
$T = \sum_i B(x_i)$,
the sum of the values of B over all random points
falling inside the window W=as.owin(model), the window
in which the original data were observed.
predict.ppm,
predict.kppm,
predict.dppmfitT <- kppm(redwood ~ 1, "Thomas")
B <- owin(c(0, 0.5), c(-0.5, 0))
varcount(fitT, B)
fitS <- ppm(swedishpines ~ 1, Strauss(9))
BS <- square(50)
varcount(fitS, BS)Run the code above in your browser using DataLab