Residual K Function
Given a point process model fitted to a point pattern dataset, this function computes the residual K function, which serves as a diagnostic for goodness-of-fit of the model.
Kres(object, ...)
This command provides a diagnostic for the goodness-of-fit of a point process model fitted to a point pattern dataset. It computes a residual version of the K function of the dataset, which should be approximately zero if the model is a good fit to the data.
In normal use, object is a fitted point process model
or a point pattern. Then Kres first calls Kcom
to compute both the nonparametric estimate of the K function
and its model compensator. Then Kres computes the
difference between them, which is the residual K-function.
Alternatively, object may be a function value table
(object of class "fv") that was returned by
a previous call to Kcom. Then Kres computes the
residual from this object.
A function value table (object of class "fv"),
essentially a data frame of function values.
There is a plot method for this class. See fv.object.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Ege Rubak rubak@math.aau.dk and Jesper Moller.
Baddeley, A., Rubak, E. and Moller, J. (2011) Score, pseudo-score and residual diagnostics for spatial point process models. Statistical Science 26, 613–646.
Point process models: ppm.
data(cells)
fit0 <- ppm(cells, ~1) # uniform Poisson
K0 <- Kres(fit0)
K0
plot(K0)
# isotropic-correction estimate
plot(K0, ires ~ r)
# uniform Poisson is clearly not correct
fit1 <- ppm(cells, ~1, Strauss(0.08))
K1 <- Kres(fit1)
if(interactive()) {
plot(K1, ires ~ r)
# fit looks approximately OK; try adjusting interaction distance
plot(Kres(cells, interaction=Strauss(0.12)))
}
# How to make envelopes
# E <- envelope(fit1, Kres, model=fit1, nsim=19)
# plot(E)
# For computational efficiency
Kc <- Kcom(fit1)
K1 <- Kres(Kc)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.