# localKinhom

##### Inhomogeneous Neighbourhood Density Function

Computes spatially-weighted versions of the the local \(K\)-function or \(L\)-function.

- Keywords
- spatial, nonparametric

##### Usage

```
localKinhom(X, lambda, ...,
correction = "Ripley", verbose = TRUE, rvalue=NULL,
sigma = NULL, varcov = NULL)
localLinhom(X, lambda, ...,
correction = "Ripley", verbose = TRUE, rvalue=NULL,
sigma = NULL, varcov = NULL)
```

##### Arguments

- X
A point pattern (object of class

`"ppp"`

).- lambda
Optional. Values of the estimated intensity function. Either a vector giving the intensity values at the points of the pattern

`X`

, a pixel image (object of class`"im"`

) giving the intensity values at all locations, a fitted point process model (object of class`"ppm"`

) or a`function(x,y)`

which can be evaluated to give the intensity value at any location.- …
Extra arguments. Ignored if

`lambda`

is present. Passed to`density.ppp`

if`lambda`

is omitted.- correction
String specifying the edge correction to be applied. Options are

`"none"`

,`"translate"`

,`"Ripley"`

,`"translation"`

,`"isotropic"`

or`"best"`

. Only one correction may be specified.- verbose
Logical flag indicating whether to print progress reports during the calculation.

- rvalue
Optional. A

*single*value of the distance argument \(r\) at which the function L or K should be computed.- sigma, varcov
Optional arguments passed to

`density.ppp`

to control the kernel smoothing procedure for estimating`lambda`

, if`lambda`

is missing.

##### Details

The functions `localKinhom`

and `localLinhom`

are inhomogeneous or weighted versions of the
neighbourhood density function implemented in
`localK`

and `localL`

.

Given a spatial point pattern `X`

, the
inhomogeneous neighbourhood density function
\(L_i(r)\) associated with the \(i\)th point
in `X`

is computed by
$$
L_i(r) = \sqrt{\frac 1 \pi \sum_j \frac{e_{ij}}{\lambda_j}}
$$
where the sum is over all points \(j \neq i\) that lie
within a distance \(r\) of the \(i\)th point,
\(\lambda_j\) is the estimated intensity of the
point pattern at the point \(j\),
and \(e_{ij}\) is an edge correction
term (as described in `Kest`

).
The value of \(L_i(r)\) can also be interpreted as one
of the summands that contributes to the global estimate of the
inhomogeneous L function (see `Linhom`

).

By default, the function \(L_i(r)\) or
\(K_i(r)\) is computed for a range of \(r\) values
for each point \(i\). The results are stored as a function value
table (object of class `"fv"`

) with a column of the table
containing the function estimates for each point of the pattern
`X`

.

Alternatively, if the argument `rvalue`

is given, and it is a
single number, then the function will only be computed for this value
of \(r\), and the results will be returned as a numeric vector,
with one entry of the vector for each point of the pattern `X`

.

##### Value

If `rvalue`

is given, the result is a numeric vector
of length equal to the number of points in the point pattern.

If `rvalue`

is absent, the result is
an object of class `"fv"`

, see `fv.object`

,
which can be plotted directly using `plot.fv`

.
Essentially a data frame containing columns

the vector of values of the argument \(r\) at which the function \(K\) has been estimated

the theoretical value \(K(r) = \pi r^2\) or \(L(r)=r\) for a stationary Poisson process

##### See Also

##### Examples

```
# NOT RUN {
data(ponderosa)
X <- ponderosa
# compute all the local L functions
L <- localLinhom(X)
# plot all the local L functions against r
plot(L, main="local L functions for ponderosa", legend=FALSE)
# plot only the local L function for point number 7
plot(L, iso007 ~ r)
# compute the values of L(r) for r = 12 metres
L12 <- localL(X, rvalue=12)
# }
```

*Documentation reproduced from package spatstat, version 1.55-1, License: GPL (>= 2)*