The object model
should be an object of class
"ppm"
, "kppm"
or "lppm"
representing a point process model fitted to point pattern data.
The model's trend formula should involve a spatial covariate
named covname
. This could be "x"
or "y"
representing one of the Cartesian coordinates.
More commonly the covariate
is another, external variable that was supplied when fitting the model.
The command effectfun
computes the fitted trend
of the point process model
as a function of the covariate
named covname
.
The return value can be plotted immediately, giving a
plot of the fitted trend against the value of the covariate.
If the model also involves covariates other than covname
,
then these covariates will be held fixed. Values for
these other covariates must be provided as arguments
to effectfun
in the form name=value
.
If se.fit=TRUE
, the algorithm also calculates
the asymptotic standard error of the fitted trend,
and a (pointwise) asymptotic 95% confidence interval for the
true trend.
This command is just a wrapper for the prediction method
predict.ppm
. For more complicated computations
about the fitted intensity, use predict.ppm
.