R: Create Pixel Image on Linear Network

linim {spatstat.linnet} | R Documentation

## Create Pixel Image on Linear Network

### Description

Creates an object of class `"linim"` that represents a pixel image on a linear network.

### Usage

      linim(L, Z, ..., restrict=TRUE, df=NULL)

### Arguments

`L` | Linear network (object of class `"linnet"`).
`Z` | Pixel image (object of class `"im"`).
`...` | Ignored.
`restrict` | Advanced use only. Logical value indicating whether to ensure that all pixels in `Z` which do not lie on the network `L` have pixel value `NA`. This condition must be satisfied, but if you set `restrict=FALSE` it will not be checked, and the code will run faster.
`df` | Advanced use only. Data frame giving full details of the mapping between the pixels of `Z` and the lines of `L`. See Details.

### Details

This command creates an object of class `"linim"` that represents a pixel image defined on a linear network. Typically such objects are used to represent the result of smoothing or model-fitting on the network. Most users will not need to call `linim` directly.

The argument `L` is a linear network (object of class `"linnet"`). It gives the exact spatial locations of the line segments of the network, and their connectivity.

The argument `Z` is a pixel image object of class `"im"` that gives a pixellated approximation of the function values.

For increased efficiency, advanced users may specify the optional argument `df`. This is a data frame giving the precomputed mapping between the pixels of `Z` and the line segments of `L`. It should have columns named `xc, yc` containing the coordinates of the pixel centres, `x,y` containing the projections of these pixel centres onto the linear network, `mapXY` identifying the line segment on which each projected point lies, and `tp` giving the parametric position of `(x,y)` along the segment.

### Value

Object of class `"linim"` that also inherits the class `"im"`. There is a special method for plotting this class.

### Author(s)

Adrian Baddeley Adrian.Baddeley@curtin.edu.au

### 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

`plot.linim`, `linnet`, `eval.linim`, `Math.linim`, `im`.

### Examples

      Z <- as.im(function(x,y) {x-y}, Frame(simplenet))
      X <- linim(simplenet, Z)
      X

[Package spatstat.linnet version 2.3-1 Index]