"dfbetas"(model, ..., drop = FALSE, iScore=NULL,
iHessian=NULL, iArgs=NULL)"ppm").
drop=FALSE) or
exclude (drop=TRUE) contributions from quadrature
points that were not used to fit the model.
iScore,
iHessian if required.
"msr" representing a signed or vector-valued
measure.
model,
this function computes the influence measure for each parameter,
as described in Baddeley, Chang and Song (2013).
This is a method for the generic function dfbetas. The influence measure for each parameter $\theta$ is a
signed measure in two-dimensional space. It consists of a discrete
mass on each data point (i.e. each point in the point pattern to which
the model was originally fitted) and a continuous density at
all locations. The mass at a data point represents the change in the
fitted value of the parameter $\theta$ that would occur
if this data point were to be deleted.
The density at other non-data locations represents the
effect (on the fitted value of $\theta$)
of deleting these locations (and their associated covariate values)
from the input to the fitting procedure.
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.
leverage.ppm,
influence.ppm,
ppmInfluence
X <- rpoispp(function(x,y) { exp(3+3*x) })
fit <- ppm(X, ~x+y)
plot(dfbetas(fit))
plot(Smooth(dfbetas(fit)))
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