linearKdot.inhom(X, i, lambdaI, lambdadot, r=NULL, ...,
correction="Ang", normalise=TRUE)
"lpp"
which
must be a multitype point pattern (a marked point pattern
whose marks areX
from which distances are measured.
Defaults to the first level of marks(X)
.i
. Either a numeric vector,
a function
, a pixel image
(object of class "im"
or "linim"
) or
a fitted point process model (object of class "ppm"
X
. Either a numeric vector,
a function
, a pixel image
(object of class "im"
or "linim"
) or
a fitted point process model (object of class "ppm"
"none"
or "Ang"
. See Details.lambdaI
and lambdadot
if
they are functions.TRUE
(the default), the denominator of the estimator is
data-dependent (equal to the sum of the reciprocal intensities at
the points of type i
), which reduces the sampling variability.
If FALSE
"fv"
(see fv.object
).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.Kdot.inhom
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 $K_{i\bullet}(r)$ 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.
If lambdaI
or lambdadot
is a fitted point process model,
the default behaviour is to update the model by re-fitting it to
the data, before computing the fitted intensity.
This can be disabled by setting update=FALSE
.
linearKdot
,
linearK
.lam <- table(marks(chicago))/(summary(chicago)$totlength)
lamI <- function(x,y,const=lam[["assault"]]){ rep(const, length(x)) }
lam. <- function(x,y,const=sum(lam)){ rep(const, length(x)) }
K <- linearKdot.inhom(chicago, "assault", lamI, lam.)
fit <- lppm(chicago, ~marks + x)
linearKdot.inhom(chicago, "assault", fit, fit)
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