Influence Measure for Spatial Point Process Model
Computes the influence measure for a fitted spatial point process model.
## S3 method for class 'ppm'
influence(model, ...,
drop = FALSE, iScore=NULL, iHessian=NULL, iArgs=NULL)model |
Fitted point process model (object of class |
... |
Ignored. |
drop |
Logical. Whether to include ( |
iScore,iHessian |
Components of the score vector and Hessian matrix for the irregular parameters, if required. See Details. |
iArgs |
List of extra arguments for the functions |
Given a fitted spatial point process model model,
this function computes the influence measure
described in Baddeley, Chang and Song (2013)
and Baddeley, Rubak and Turner (2019).
The function influence is generic,
and 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 model
was fitted).
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
fitted (using ippm)
then the influence calculation requires the first and second
derivatives of the log trend with respect to the irregular parameters.
The argument 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.
An object of class "influence.ppm".
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
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.
Baddeley, A., Rubak, E. and Turner, R. (2019) Leverage and influence diagnostics for Gibbs spatial point processes. Spatial Statistics 29, 15–48.
X <- rpoispp(function(x,y) { exp(3+3*x) })
fit <- ppm(X ~x+y)
plot(influence(fit))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.