# predict.rppm

##### Make Predictions From a Recursively Partitioned Point Process Model

Given a model which has been fitted to point pattern data by recursive partitioning, compute the predicted intensity of the model.

##### Usage

```
# S3 method for rppm
predict(object, …)
```# S3 method for rppm
fitted(object, …)

##### Arguments

- 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`

)

##### Details

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.

##### Value

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.

##### See Also

##### Examples

```
# 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)
# }
```

*Documentation reproduced from package spatstat, version 1.49-0, License: GPL (>= 2)*