Residual G Function
Given a point process model fitted to a point pattern dataset, this function computes the residual G function, which serves as a diagnostic for goodness-of-fit of the model.
Gres(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 G 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 Gres first calls Gcom
to compute both the nonparametric estimate of the G function
and its model compensator. Then Gres computes the
difference between them, which is the residual G-function.
Alternatively, object may be a function value table
(object of class "fv") that was returned by
a previous call to Gcom. Then Gres 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.
Model-fitting:
ppm.
data(cells)
fit0 <- ppm(cells, ~1) # uniform Poisson
G0 <- Gres(fit0)
plot(G0)
# Hanisch correction estimate
plot(G0, hres ~ r)
# uniform Poisson is clearly not correct
fit1 <- ppm(cells, ~1, Strauss(0.08))
plot(Gres(fit1), hres ~ r)
# fit looks approximately OK; try adjusting interaction distance
plot(Gres(cells, interaction=Strauss(0.12)))
# How to make envelopes
if(interactive()) {
E <- envelope(fit1, Gres, model=fit1, nsim=39)
plot(E)
}
# For computational efficiency
Gc <- Gcom(fit1)
G1 <- Gres(Gc)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.