R: Fit Point Process Model to Point Pattern on Linear Network

lppm {spatstat.linnet} | R Documentation

## Fit Point Process Model to Point Pattern on Linear Network

### Description

Fit a point process model to a point pattern dataset on a linear network

### Usage

    lppm(X, ...)

    ## S3 method for class 'formula'
    lppm(X, interaction=NULL, ..., data=NULL)

    ## S3 method for class 'lpp'
    lppm(X, ..., eps=NULL, nd=1000, random=FALSE)

### Arguments

`X` | Either an object of class `"lpp"` specifying a point pattern on a linear network, or a `formula` specifying the point process model.
`...` | Arguments passed to `ppm`.
`interaction` | An object of class `"interact"` describing the point process interaction structure, or `NULL` indicating that a Poisson process (stationary or nonstationary) should be fitted.
`data` | Optional. The values of spatial covariates (other than the Cartesian coordinates) required by the model. A list whose entries are images, functions, windows, tessellations or single numbers.
`eps` | Optional. Spacing between dummy points along each segment of the network.
`nd` | Optional. Total number of dummy points placed on the network. Ignored if `eps` is given.
`random` | Logical value indicating whether the grid of dummy points should be placed at a randomised starting position.

### Details

This function fits a point process model to data that specify a point pattern on a linear network. It is a counterpart of the model-fitting function `ppm` designed to work with objects of class `"lpp"` instead of `"ppp"`.

The function `lppm` is generic, with methods for the classes `formula` and `lppp`.

In `lppm.lpp` the first argument `X` should be an object of class `"lpp"` (created by the command `lpp`) specifying a point pattern on a linear network.

In `lppm.formula`, the first argument is a `formula` in the R language describing the spatial trend model to be fitted. It has the general form `pattern ~ trend` where the left hand side `pattern` is usually the name of a point pattern on a linear network (object of class `"lpp"`) to which the model should be fitted, or an expression which evaluates to such a point pattern; and the right hand side `trend` is an expression specifying the spatial trend of the model.

Other arguments `...` are passed from `lppm.formula` to `lppm.lpp` and from `lppm.lpp` to `ppm`.

### Value

An object of class `"lppm"` representing the fitted model. There are methods for `print`, `predict`, `coef` and similar functions.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Greg McSwiggan.

### 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. Scandinavian Journal of Statistics 39, 591–617.

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

`methods.lppm`, `predict.lppm`, `ppm`, `lpp`.

### Examples

      X <- runiflpp(15, simplenet)
      lppm(X ~1)
      lppm(X ~x)
      marks(X) <- factor(rep(letters[1:3], 5))
      lppm(X ~ marks)
      lppm(X ~ marks * x)

[Package spatstat.linnet version 2.0-0 Index]