spatialcdf(Z, weights = NULL, normalise = FALSE, ..., W = NULL, Zname = NULL)function(x,y,...)
function(x,y,...), a window, a constant value,
or a fitted point process model (object of class "ppm" or
"kppm").
as.mask to determine the pixel
resolution, or extra arguments passed to Z if it is a function.
"owin") defining the spatial
domain.
Z
used in plots.
"spatialcdf",
"ewcdf", "ecdf" and "stepfun".
weights is missing or NULL, it defaults to 1.
The values of the covariate Z
are computed on a grid of pixels. The weighted cumulative distribution
function of Z values is computed, taking each value with weight
equal to the pixel area. The resulting function $F$ is such that
$F(t)$ is the area of the region of space where
$Z <= t$.<="" p=""> If weights is a pixel image or a function, then the
values of weights and of the covariate Z
are computed on a grid of pixels. The
weights are multiplied by the pixel area.
Then the weighted empirical cumulative distribution function
of Z values
is computed using ewcdf. The resulting function
$F$ is such that $F(t)$ is the total weight (or weighted area)
of the region of space where $Z <= t$.<="" p="">
If weights is a fitted point process model, then it should
be a Poisson process. The fitted intensity of the model,
and the value of the covariate Z, are evaluated at the
quadrature points used to fit the model. The weights are
multiplied by the weights of the quadrature points.
Then the weighted empirical cumulative distribution of Z values
is computed using ewcdf. The resulting function
$F$ is such that $F(t)$ is the expected number of points
in the point process that will fall in the region of space
where $Z <= t$.="" if=""
The result can be printed, plotted, and used as a function.
ewcdf,
cdf.test
with(bei.extra, {
plot(spatialcdf(grad))
fit <- ppm(bei ~ elev)
plot(spatialcdf(grad, predict(fit)))
plot(A <- spatialcdf(grad, fit))
A(0.1)
})
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