
Computes the deletion influence measure for each parameter in a fitted point process model.
# S3 method for ppm
dfbetas(model, …,
drop = FALSE, iScore=NULL, iHessian=NULL, iArgs=NULL)
Fitted point process model (object of class "ppm"
).
Ignored, except for the arguments dimyx
and eps
which are passed to as.mask
to control the spatial resolution of the image of the density
component.
Logical. Whether to include (drop=FALSE
) or
exclude (drop=TRUE
) contributions from quadrature
points that were not used to fit the model.
Components of the score vector and Hessian matrix for the irregular parameters, if required. See Details.
List of extra arguments for the functions iScore
,
iHessian
if required.
An object of class "msr"
representing a signed or vector-valued
measure. This object can be printed and plotted.
Given a fitted spatial point process model
,
this function computes the influence measure for each parameter,
as described in Baddeley, Chang and Song (2013)
and Baddeley, Rubak and Turner (2019).
This is a method for the generic function dfbetas
.
The influence measure for each parameter 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
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
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.
leverage.ppm
,
influence.ppm
,
ppmInfluence
.
See msr
for information on how to use a measure.
# NOT RUN {
# }
# NOT RUN {
X <- rpoispp(function(x,y) { exp(3+3*x) })
fit <- ppm(X ~x+y)
# }
# NOT RUN {
plot(dfbetas(fit))
plot(Smooth(dfbetas(fit)))
# }
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