fitted.lppm: Fitted Intensity for Point Process on Linear Network In spatstat.linnet: Linear Networks Functionality of the 'spatstat' Family

Description

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.

Usage

 ```1 2 3 4``` ```## S3 method for class 'lppm' fitted(object, ..., dataonly = FALSE, new.coef = NULL, leaveoneout = FALSE) ```

Arguments

 `object` Fitted point process model on a linear network (object of class `"lppm"`). `...` Ignored. `dataonly` Logical value indicating whether to computed fitted intensities at the points of the original point pattern dataset (`dataonly=TRUE`) or at all the quadrature points of the quadrature scheme used to fit the model (`dataonly=FALSE`, the default). `new.coef` Numeric vector of parameter values to replace the fitted model parameters `coef(object)`. `leaveoneout` Logical. If `TRUE` the fitted value at each data point will be computed using a leave-one-out method. See Details.

Details

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`.

Value

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))`.

Author(s)

and \ege

`lppm`, `predict.lppm`
 ```1 2 3``` ``` fit <- lppm(spiders~x+y) a <- fitted(fit) b <- fitted(fit, dataonly=TRUE) ```