Given a model which has been fitted to point pattern data by recursive partitioning, compute the predicted intensity of the model.
# S3 method for rppm
predict(object, …)# S3 method for rppm
fitted(object, …)
Fitted point process model of class "rppm"
produced by the function rppm.
Optional arguments passed to predict.ppm
to specify the locations where prediction is required.
(Ignored by fitted.rppm)
The result of fitted.rppm is a numeric vector.
The result of predict.rppm is a pixel image, a list of pixel images,
or a numeric vector.
These functions are methods for the generic functions
fitted and predict.
They compute the fitted intensity of a point process model.
The argument object should be a fitted point process model
of class "rppm" produced by the function rppm.
The fitted method computes the fitted intensity at the original data
points, yielding a numeric vector with one entry for each data point.
The predict method computes the fitted intensity at
any locations. By default, predictions are
calculated at a regular grid of spatial locations, and the result
is a pixel image giving the predicted intensity values at these
locations.
Alternatively, predictions can be performed at other
locations, or a finer grid of locations, or only at certain specified
locations, using additional arguments …
which will be interpreted by predict.ppm.
Common arguments are ngrid to increase the grid resolution,
window to specify the prediction region, and locations
to specify the exact locations of predictions.
See predict.ppm for details of these arguments.
Predictions are computed by evaluating the explanatory covariates at each desired location, and applying the recursive partitioning rule to each set of covariate values.
# NOT RUN {
fit <- rppm(unmark(gorillas) ~ vegetation, data=gorillas.extra)
plot(predict(fit))
lambdaX <- fitted(fit)
lambdaX[1:5]
# Mondriaan pictures
plot(predict(rppm(redwoodfull ~ x + y)))
points(redwoodfull)
# }
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