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A faster way to compute the minimum or maximum nearest-neighbour distance in a point pattern.
minnndist(X, positive=FALSE, by=NULL)
maxnndist(X, positive=FALSE, by=NULL)
A point pattern (object of class "ppp"
).
Logical. If FALSE
(the default), compute the usual
nearest-neighbour distance.
If TRUE
, ignore coincident points, so that the
nearest neighbour distance for each point is greater than zero.
Optional. A factor, which separates X
into groups.
The algorithm will compute the distance to
the nearest point in each group.
A single numeric value (possibly NA
).
If by
is given, the result is a numeric matrix
giving the minimum or maximum nearest neighbour distance
between each subset of X
.
These functions find the minimum and maximum values
of nearest-neighbour distances in the point pattern X
.
minnndist(X)
and maxnndist(X)
are
equivalent to, but faster than, min(nndist(X))
and max(nndist(X))
respectively.
The value is NA
if npoints(X) < 2
.
# NOT RUN {
min(nndist(swedishpines))
minnndist(swedishpines)
max(nndist(swedishpines))
maxnndist(swedishpines)
minnndist(lansing, positive=TRUE)
if(interactive()) {
X <- runifrect(1e6)
system.time(min(nndist(X)))
system.time(minnndist(X))
}
minnndist(amacrine, by=marks(amacrine))
maxnndist(amacrine, by=marks(amacrine))
# }
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