eem
Exponential Energy Marks
Given a point process model fitted to a point pattern, compute the Stoyan-Grabarnik diagnostic ``exponential energy marks'' for the data points.
- Keywords
- spatial
Usage
eem(fit)Arguments
- fit
- The fitted point process model. An object of class
"ppm".
Details
Stoyan and Grabarnik (1991) proposed a diagnostic
tool for point process models fitted to spatial point pattern data.
Each point $x[i]$ of the data pattern $X$
is given a `mark' or `weight'
$$m[i] = 1/lambda-hat(x[i],X)$$
where $lambda-hat(x[i],X)$
is the conditional intensity of the fitted model.
If the fitted model is correct, then the sum of these marks
for all points in a region $B$ has expected value equal to the
area of $B$.
The argument fit must be a fitted point process model
(object of class "ppm"). Such objects are produced by the maximum
pseudolikelihood fitting algorithm ppm).
This fitted model object contains complete
information about the original data pattern and the model that was
fitted to it.
The value returned by eem is the vector
of weights $m[i]$ associated with the points $x[i]$
of the original data pattern. The original data pattern
(in corresponding order) can be
extracted from fit using data.ppm.
The function diagnose.ppm
produces a set of sensible diagnostic plots based on these weights.
Value
- A vector containing the values of the exponential energy mark for each point in the pattern.
References
Stoyan, D. and Grabarnik, P. (1991) Second-order characteristics for stochastic structures connected with Gibbs point processes. Mathematische Nachrichten, 151:95--100.
See Also
Examples
data(cells)
fit <- ppm(cells, ~x, Strauss(r=0.15), rbord=0.15)
ee <- eem(fit)
sum(ee)/area.owin(cells$window) # should be about 1 if model is correct
Y <- setmarks(cells, ee)
plot(Y, main="Cells data
Exponential energy marks")