Computes the influence measure for a fitted spatial point process model.
Fitted point process model (object of class
Logical. Whether to include (
Components of the score vector and Hessian matrix for the irregular parameters, if required. See Details.
List of extra arguments for the functions
Given a fitted spatial point process model
this function computes the influence measure
described in Baddeley, Chang and Song (2013).
influence is generic,
influence.ppm is the method for objects of class
"ppm" representing point process models.
The influence of a point process model is a value attached to each data point
(i.e. each point of the point pattern to which the
The influence value s(x[i]) at a data point
x[i] represents the change in the maximised log (pseudo)likelihood
that occurs when the point x[i] is deleted.
A relatively large value of s(x[i]) indicates a
data point with a large influence on the fitted model.
If the point process model trend has irregular parameters that were
then the influence calculation requires the first and second
derivatives of the log trend with respect to the irregular parameters.
iScore should be a list,
with one entry for each irregular parameter, of R functions that compute the
partial derivatives of the log trend (i.e. log intensity or
log conditional intensity) with respect to each irregular
parameter. The argument
iHessian should be a list,
with p^2 entries where p is the number of irregular
parameters, of R functions that compute the second order
partial derivatives of the
log trend with respect to each pair of irregular parameters.
The result of
an object of class
"influence.ppm". It can be plotted
plot.influence.ppm), or converted to a marked
point pattern by
An object of class
"influence.ppm" that can be plotted
plot.influence.ppm). There are also methods
Baddeley, A. and Chang, Y.M. and Song, Y. (2013) Leverage and influence diagnostics for spatial point process models. Scandinavian Journal of Statistics 40, 86–104.
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