# Ldot.inhom

##### Inhomogeneous Multitype L Dot Function

For a multitype point pattern, estimate the inhomogeneous version of the dot $L$ function.

- Keywords
- spatial, nonparametric

##### Usage

`Ldot.inhom(X, i, ...)`

##### Arguments

- X
- The observed point pattern, from which an estimate of the inhomogeneous cross type $L$ function $L_{i\bullet}(r)$ will be computed. It must be a multitype point pattern (a marked point pattern whose marks are a factor). See under Details.
- i
- The type (mark value)
of the points in
`X`

from which distances are measured. A character string (or something that will be converted to a character string). Defaults to the first level of`marks(X)`

. - ...
- Other arguments passed to
`Kdot.inhom`

.

##### Details

This a generalisation of the function `Ldot`

to include an adjustment for spatially inhomogeneous intensity,
in a manner similar to the function `Linhom`

.

All the arguments are passed to `Kdot.inhom`

, which
estimates the inhomogeneous multitype K function
$K_{i\bullet}(r)$ for the point pattern.
The resulting values are then
transformed by taking $L(r) = \sqrt{K(r)/\pi}$.

##### Value

- An object of class
`"fv"`

(see`fv.object`

).Essentially a data frame containing numeric columns

r the values of the argument $r$ at which the function $L_{i\bullet}(r)$ has been estimated theo the theoretical value of $L_{i\bullet}(r)$ for a marked Poisson process, identical to $r$. - together with a column or columns named
`"border"`

,`"bord.modif"`

,`"iso"`

and/or`"trans"`

, according to the selected edge corrections. These columns contain estimates of the function $L_{i\bullet}(r)$ obtained by the edge corrections named.

##### Warnings

The argument `i`

is interpreted as
a level of the factor `X$marks`

. It is converted to a character
string if it is not already a character string.
The value `i=1`

does **not**
refer to the first level of the factor.

##### References

Moller, J. and Waagepetersen, R. Statistical Inference and Simulation for Spatial Point Processes Chapman and Hall/CRC Boca Raton, 2003.

##### See Also

##### Examples

```
# Lansing Woods data
data(lansing)
lansing <- lansing[seq(1,lansing$n, by=10)]
ma <- split(lansing)$maple
lg <- unmark(lansing)
# Estimate intensities by nonparametric smoothing
lambdaM <- density.ppp(ma, sigma=0.15, at="points")
lambdadot <- density.ppp(lg, sigma=0.15, at="points")
L <- Ldot.inhom(lansing, "maple", lambdaI=lambdaM,
lambdadot=lambdadot)
# synthetic example: type A points have intensity 50,
# type B points have intensity 50 + 100 * x
lamB <- as.im(function(x,y){50 + 100 * x}, owin())
lamdot <- as.im(function(x,y) { 100 + 100 * x}, owin())
X <- superimpose(A=runifpoispp(50), B=rpoispp(lamB))
L <- Ldot.inhom(X, "B", lambdaI=lamB, lambdadot=lamdot)
```

*Documentation reproduced from package spatstat, version 1.27-0, License: GPL (>= 2)*