Gres {spatstat.model} | R Documentation |
Residual G Function
Description
Given a point process model fitted to a point pattern dataset,
this function computes the residual function,
which serves as a diagnostic for goodness-of-fit of the model.
Usage
Gres(object, ...)
Arguments
object |
Object to be analysed.
Either a fitted point process model (object of class |
... |
Arguments passed to |
Details
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 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 function
and its model compensator. Then
Gres
computes the
difference between them, which is the residual -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.
Value
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
.
Author(s)
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Ege Rubak rubak@math.aau.dk and Jesper Moller.
References
Baddeley, A., Rubak, E. and Moller, J. (2011) Score, pseudo-score and residual diagnostics for spatial point process models. Statistical Science 26, 613–646.
See Also
Related functions:
Gcom
,
Gest
.
Alternative functions:
Kres
,
psstA
,
psstG
,
psst
.
Model-fitting:
ppm
.
Examples
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)