For a multitype point pattern on a linear network, estimate the multitype pair correlation function from points of type \(i\) to points of type \(j\).

`linearpcfcross(X, i, j, r=NULL, …, correction="Ang")`

X

The observed point pattern,
from which an estimate of the \(i\)-to-any pair correlation function
\(g_{ij}(r)\) will be computed.
An object of class `"lpp"`

which
must be a multitype point pattern (a marked point pattern
whose marks are a factor).

i

Number or character string identifying the type (mark value)
of the points in `X`

from which distances are measured.
Defaults to the first level of `marks(X)`

.

j

Number or character string identifying the type (mark value)
of the points in `X`

to which distances are measured.
Defaults to the second level of `marks(X)`

.

r

numeric vector. The values of the argument \(r\) at which the function \(g_{ij}(r)\) should be evaluated. There is a sensible default. First-time users are strongly advised not to specify this argument. See below for important conditions on \(r\).

correction

Geometry correction.
Either `"none"`

or `"Ang"`

. See Details.

…

Arguments passed to `density.default`

to control the kernel smoothing.

An object of class `"fv"`

(see `fv.object`

).

The argument `i`

is interpreted as a
level of the factor `marks(X)`

. Beware of the usual
trap with factors: numerical values are not
interpreted in the same way as character values.

This is a counterpart of the function `pcfcross`

for a point pattern on a linear network (object of class `"lpp"`

).

The argument `i`

will be interpreted as
levels of the factor `marks(X)`

.
If `i`

is missing, it defaults to the first
level of the marks factor.

The argument `r`

is the vector of values for the
distance \(r\) at which \(g_{ij}(r)\)
should be evaluated.
The values of \(r\) must be increasing nonnegative numbers
and the maximum \(r\) value must not exceed the radius of the
largest disc contained in the window.

Baddeley, A, Jammalamadaka, A. and Nair, G. (to appear)
Multitype point process analysis of spines on the
dendrite network of a neuron.
*Applied Statistics* (Journal of the Royal Statistical
Society, Series C), **63**, 673--694.

# NOT RUN { data(chicago) g <- linearpcfcross(chicago, "assault") # }