The argument object
must be a fitted point process model
(object of class "ppm"
). Such objects are produced by the
model-fitting algorithm ppm
).
This function evaluates the conditional intensity
\(\hat\lambda(u, x)\)
or spatial trend \(\hat b(u)\) of the fitted point process
model for certain locations \(u\),
where x
is the original point pattern dataset to which
the model was fitted.
The locations \(u\) at which the fitted conditional intensity/trend
is evaluated, are the points of the
quadrature scheme used to fit the model in ppm
.
They include the data points (the points of the original point pattern
dataset x
) and other ``dummy'' points
in the window of observation.
If leaveoneout=TRUE
, fitted values will be computed
for the data points only, using a ‘leave-one-out’ rule:
the fitted value at X[i]
is effectively computed by
deleting this point from the data and re-fitting the model to the
reduced pattern X[-i]
, then predicting the value at
X[i]
. (Instead of literally performing this calculation,
we apply a Taylor approximation using the leverage
computed in dfbetas.ppm
.
The argument drop
is explained in quad.ppm
.
Use predict.ppm
to compute the fitted conditional
intensity at other locations or with other values of the
explanatory variables.