Model Compensator of K Function
Given a point process model fitted to a point pattern dataset, this function computes the compensator of the K function based on the fitted model (as well as the usual nonparametric estimates of K based on the data alone). Comparison between the nonparametric and model-compensated K functions serves as a diagnostic for the model.
Kcom(object, r = NULL, breaks = NULL, ...,
correction = c("border", "isotropic", "translate"),
conditional = !is.poisson(object),
restrict = FALSE,
model = NULL,
trend = ~1, interaction = Poisson(), rbord = reach(interaction),
compute.var = TRUE,
truecoef = NULL, hi.res = NULL)object |
Object to be analysed.
Either a fitted point process model (object of class |
r |
Optional. Vector of values of the argument r at which the function K(r) should be computed. This argument is usually not specified. There is a sensible default. |
breaks |
This argument is for advanced use only. |
... |
Ignored. |
correction |
Optional vector of character strings specifying the edge
correction(s) to be used. See |
conditional |
Optional. Logical value indicating whether to compute the estimates for the conditional case. See Details. |
restrict |
Logical value indicating
whether to compute the restriction estimator ( |
model |
Optional. A fitted point process model (object of
class |
trend,interaction,rbord |
Optional. Arguments passed to |
compute.var |
Logical value indicating whether to compute the Poincare variance bound for the residual K function (calculation is only implemented for the isotropic correction). |
truecoef |
Optional. Numeric vector. If present, this will be treated as
if it were the true coefficient vector of the point process model,
in calculating the diagnostic. Incompatible with |
hi.res |
Optional. List of parameters passed to |
This command provides a diagnostic for the goodness-of-fit of a point process model fitted to a point pattern dataset. It computes an estimate of the K function of the dataset, together with a model compensator of the K function, which should be approximately equal if the model is a good fit to the data.
The first argument, object, is usually a fitted point process model
(object of class "ppm"), obtained from the
model-fitting function ppm.
For convenience, object can also be a point pattern
(object of class "ppp"). In that case, a point process
model will be fitted to it, by calling ppm using the arguments
trend (for the first order trend),
interaction (for the interpoint interaction)
and rbord (for the erosion distance in the border correction
for the pseudolikelihood). See ppm for details
of these arguments.
The algorithm first extracts the original point pattern dataset
(to which the model was fitted) and computes the
standard nonparametric estimates of the K function.
It then also computes the model compensator of the
K function. The different function estimates are returned
as columns in a data frame (of class "fv").
The argument correction determines the edge correction(s)
to be applied. See Kest for explanation of the principle
of edge corrections. The following table gives the options
for the correction argument, and the corresponding
column names in the result:
correction |
description of correction | nonparametric | compensator |
"isotropic" |
Ripley isotropic correction | iso |
icom |
"translate" |
Ohser-Stoyan translation correction | trans |
tcom |
"border" |
border correction | border |
bcom
|
The nonparametric estimates can all be expressed in the form
K(r) = sum[i] sum[j < i] e(x[i], x[j], r, x) I( d(x[i],x[j]) <= r )
where x[i] is the i-th data point, d(x[i],x[j]) is the distance between x[i] and x[j], and e(x[i],x[j],r,x) is a term that serves to correct edge effects and to re-normalise the sum. The corresponding model compensator is
C K(r) = integral[u] lambda(u,x) sum[j] e(u, x[j], r, x+u) I( d(u,x[j]) <= r )
where the integral is over all locations u in the observation window, lambda(u,x) denotes the conditional intensity of the model at the location u, and x+u denotes the data point pattern x augmented by adding the extra point u.
If the fitted model is a Poisson point process, then the formulae above are exactly what is computed. If the fitted model is not Poisson, the formulae above are modified slightly to handle edge effects.
The modification is determined by the arguments
conditional and restrict.
The value of conditional defaults to FALSE for Poisson models
and TRUE for non-Poisson models.
If conditional=FALSE then the formulae above are not modified.
If conditional=TRUE, then the algorithm calculates
the restriction estimator if restrict=TRUE,
and calculates the reweighting estimator if restrict=FALSE.
See Appendix D of Baddeley, Rubak and Moller (2011).
Thus, by default, the reweighting estimator is computed
for non-Poisson models.
The nonparametric estimates of K(r) are approximately unbiased estimates of the K-function, assuming the point process is stationary. The model compensators are unbiased estimates of the mean values of the corresponding nonparametric estimates, assuming the model is true. Thus, if the model is a good fit, the mean value of the difference between the nonparametric estimates and model compensators is approximately zero.
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.
fit0 <- ppm(cells, ~1) # uniform Poisson
if(interactive()) {
plot(Kcom(fit0))
# compare the isotropic-correction estimates
plot(Kcom(fit0), cbind(iso, icom) ~ r)
# uniform Poisson is clearly not correct
}
fit1 <- ppm(cells, ~1, Strauss(0.08))
K1 <- Kcom(fit1)
K1
if(interactive()) {
plot(K1)
plot(K1, cbind(iso, icom) ~ r)
plot(K1, cbind(trans, tcom) ~ r)
# how to plot the difference between nonparametric estimates and compensators
plot(K1, iso - icom ~ r)
# fit looks approximately OK; try adjusting interaction distance
}
fit2 <- ppm(cells, ~1, Strauss(0.12))
K2 <- Kcom(fit2)
if(interactive()) {
plot(K2)
plot(K2, cbind(iso, icom) ~ r)
plot(K2, iso - icom ~ r)
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