Description Usage Arguments Details Value Author(s) See Also Examples

Given a point process model fitted to a point pattern on a linear network, compute the fitted intensity of the model at the points of the pattern, or at the points of the quadrature scheme used to fit the model.

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`object` |
Fitted point process model on a linear network
(object of class |

`...` |
Ignored. |

`dataonly` |
Logical value indicating whether to computed fitted intensities
at the points of the original point pattern dataset
( |

`new.coef` |
Numeric vector of parameter values to replace the
fitted model parameters |

`leaveoneout` |
Logical. If |

This is a method for the generic function `fitted`

for the class `"lppm"`

of fitted point process models on a linear
network.

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 influence function
computed in `dfbetas.ppm`

.

A vector containing the values of the fitted spatial trend.

Entries in this vector correspond to the quadrature points (data or
dummy points) used to fit the model.
The quadrature points can be extracted from `object`

by `union.quad(quad.ppm(object))`

.

and \ege

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