# predict.lppm

##### Predict Point Process Model on Linear Network

Given a fitted point process model on a linear network, compute the fitted intensity or conditional intensity of the model.

##### Usage

```
## S3 method for class 'lppm':
predict(object, ..., type = "trend", locations = NULL)
```

##### Arguments

- object
- The fitted model. An object of class
`"lppm"`

, see`lppm`

. - type
- Type of values to be computed. Either
`"trend"`

,`"cif"`

or`"se"`

. - locations
- Optional. Locations at which predictions should be computed. Either a data frame with two columns of coordinates, or a binary image mask.
- ...
- Optional arguments passed to
`as.mask`

to determine the pixel resolution (if`locations`

is missing).

##### Details

This function computes the fitted poin process intensity,
fitted conditional intensity, or standard error of the fitted
intensity, for a point process model on a linear network.
It is a method for the generic `predict`

for the class `"lppm"`

.

The argument `object`

should be an object of class `"lppm"`

(produced by `lppm`

) representing a point process model
on a linear network.

Predicted values are computed at the locations given by the
argument `locations`

. If this argument is missing,
then predicted values are computed at a fine grid of points
on the linear network.

- If
`locations`

is missing or`NULL`

(the default), the return value is a pixel image (object of class`"linim"`

which inherits class`"im"`

) corresponding to a discretisation of the linear network, with numeric pixel values giving the predicted values at each location on the linear network. - If
`locations`

is a data frame, the result is a numeric vector of predicted values at the locations specified by the data frame. - If
`locations`

is a binary mask, the result is a pixel image with predicted values computed at the pixels of the mask.

##### Value

- A pixel image (object of class
`"linim"`

which inherits class`"im"`

) or a numeric vector, depending on the argument`locations`

. See Details.

##### References

Ang, Q.W. (2010)
*Statistical methodology for events on a network*.
Master's thesis, School of Mathematics and Statistics, University of
Western Australia.
Ang, Q.W., Baddeley, A. and Nair, G. (2012)
Geometrically corrected second-order analysis of
events on a linear network, with applications to
ecology and criminology.
To appear in *Scandinavian Journal of Statistics*.

McSwiggan, G., Nair, M.G. and Baddeley, A. (2012) Fitting Poisson point process models to events on a linear network. Manuscript in preparation.

##### See Also

##### Examples

```
example(lpp)
fit <- lppm(X, ~x)
v <- predict(fit, type="trend")
plot(v)
```

*Documentation reproduced from package spatstat, version 1.27-0, License: GPL (>= 2)*