# 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, …, 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.

##### 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

```
# NOT RUN {
Z <- as.im(function(x,y) {x-y}, Frame(simplenet))
X <- linim(simplenet, Z)
X
# }
```

*Documentation reproduced from package spatstat, version 1.59-0, License: GPL (>= 2)*