# distfun.lpp

##### Distance Map on Linear Network

Compute the distance function of a point pattern on a linear network.

##### Usage

```
## S3 method for class 'lpp':
distfun(X, ...)
```

##### Arguments

- X
- A point pattern on a linear network
(object of class
`"lpp"`

). - ...
- Extra arguments are ignored.

##### Details

On a linear network $L$, the `f(s)`

is the shortest-path distance from $s$ to $A$.

The command `distfun.lpp`

is a method for the generic command
`distfun`

for the class `"lpp"`

of point patterns on a linear network.

If `X`

is a point pattern on a linear network,
`f <- distfun(X)`

returns a *function*
in the Rlanguage that represents the
distance function of `X`

. Evaluating the function `f`

in the form `v <- f(x,y)`

, where `x`

and `y`

are any numeric vectors of equal length containing coordinates of
spatial locations, yields the values of the distance function at these
locations. More efficiently `f`

can take the arguments
`x, y, seg, tp`

where `seg`

and `tp`

are the local
coordinates on the network.

The function `f`

obtained from `f <- distfun(X)`

also belongs to the class `"linfun"`

.
It can be printed and plotted immediately as shown in the Examples.
It can be
converted to a pixel image using `as.linim`

.

##### Value

- A
`function`

with arguments`x,y`

and optional arguments`seg,tp`

. It also belongs to the class`"linfun"`

which has methods for`plot`

,`print`

etc.

##### See Also

To identify *which* point is the nearest neighbour, see
`nnfun.lpp`

.

##### Examples

```
data(letterR)
X <- runiflpp(3, simplenet)
f <- distfun(X)
f
plot(f)
# using a distfun as a covariate in a point process model:
Y <- runiflpp(4, simplenet)
fit <- lppm(Y, ~D, covariates=list(D=f))
```

*Documentation reproduced from package spatstat, version 1.34-1, License: GPL (>= 2)*