# linim

##### Create Pixel Image on Linear Network

Creates an object of class `"linim"`

that represents
a pixel image on a linear network.

- Keywords
- spatial

##### Usage

`linim(L, Z, ..., df=NULL)`

##### Arguments

- L
- Linear network (object of class
`"linnet"`

). - Z
- Pixel image (object of class
`"im"`

). - ...
- Ignored.
- 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.

##### 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

##### Examples

```
example(linnet)
M <- as.mask.psp(as.psp(letterA))
Z <- as.im(function(x,y) {x-y}, W=M)
X <- linim(letterA, Z)
X
```

*Documentation reproduced from package spatstat, version 1.42-2, License: GPL (>= 2)*