Lcross.inhom(X, i, j, ...)
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)
.X
to which distances are measured.
A character string (or something that will be
converted to a character string).
Defaults to the second level of marks(X)
.Kcross.inhom
."fv"
(see fv.object
).Essentially a data frame containing numeric columns
r
"border"
, "bord.modif"
,
"iso"
and/or "trans"
,
according to the selected edge corrections. These columns contain
estimates of the function $L_{ij}(r)$
obtained by the edge corrections named.Lcross
to include an adjustment for spatially inhomogeneous intensity,
in a manner similar to the function Linhom
. All the arguments are passed to Kcross.inhom
, which
estimates the inhomogeneous multitype K function
$K_{ij}(r)$ for the point pattern.
The resulting values are then
transformed by taking $L(r) = \sqrt{K(r)/\pi}$.
i
and j
are always interpreted as
levels of the factor X$marks
. They are converted to character
strings if they are not already character strings.
The value i=1
does not
refer to the first level of the factor.
}
Lcross
,
Linhom
,
Kcross.inhom
# method (1): estimate intensities by nonparametric smoothing lambdaM <- density.ppp(ma, sigma=0.15, at="points") lambdaW <- density.ppp(wh, sigma=0.15, at="points") L <- Lcross.inhom(lansing, "whiteoak", "maple", lambdaW, lambdaM)
# method (2): fit parametric intensity model fit <- ppm(lansing, ~marks * polynom(x,y,2)) # evaluate fitted intensities at data points # (these are the intensities of the sub-processes of each type) inten <- fitted(fit, dataonly=TRUE) # split according to types of points lambda <- split(inten, lansing$marks) L <- Lcross.inhom(lansing, "whiteoak", "maple", lambda$whiteoak, lambda$maple) # synthetic example: type A points have intensity 50, # type B points have intensity 100 * x lamB <- as.im(function(x,y){50 + 100 * x}, owin()) X <- superimpose(A=runifpoispp(50), B=rpoispp(lamB)) L <- Lcross.inhom(X, "A", "B", lambdaI=as.im(50, X$window), lambdaJ=lamB)