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Given two point patterns X
and Y
,
finds the nearest neighbour in Y
of each point of X
.
Alternatively Y
may be a line segment pattern.
nncross(X, Y, ...) # S3 method for ppp
nncross(X, Y,
iX=NULL, iY=NULL,
what = c("dist", "which"),
...,
k = 1,
sortby=c("range", "var", "x", "y"),
is.sorted.X = FALSE,
is.sorted.Y = FALSE,
metric=NULL)
# S3 method for default
nncross(X, Y, ...)
A data frame, or a vector if the data frame would contain only one column.
By default (if what=c("dist", "which")
and k=1
)
a data frame with two columns:
Nearest neighbour distance
Nearest neighbour index in Y
If what="dist"
and k=1
, a vector of nearest neighbour distances.
If what="which"
and k=1
, a vector of nearest neighbour
indices.
If k
is specified, the result is a data frame with
columns containing the k
-th nearest neighbour distances
and/or nearest neighbour indices.
Point pattern (object of class "ppp"
).
Either a point pattern (object of class "ppp"
)
or a line segment pattern (object of class "psp"
).
Optional identifiers, applicable only in the case where
Y
is a point pattern, used to determine whether a point in
X
is identical to a point in Y
. See Details.
Character string specifying what information should be returned.
Either the nearest neighbour distance ("dist"
),
the identifier of the nearest neighbour ("which"
),
or both.
Integer, or integer vector. The algorithm will compute the distance to the
k
th nearest neighbour.
Determines which coordinate to use to sort the point patterns. See Details.
Logical values attesting whether the point patterns X
and
Y
have been sorted. See Details.
Optional. A distance metric
(object of class "metric"
, see metric.object
)
which will be used to compute the distances.
Ignored.
Read this section if you care about the speed of computation.
For efficiency, the algorithm sorts the point patterns X
and Y
into increasing order of the
By default (if sortby="range"
),
the sorting will occur on the coordinate that has the larger range of
values (according to the frame of the enclosing window of Y
).
If sortby = "var"
), sorting will occur on the coordinate that
has the greater variance (in the pattern Y
).
Setting sortby="x"
or sortby = "y"
will specify that
sorting should occur on the
If the point pattern X
is already
sorted, then the corresponding argument is.sorted.X
should be set to TRUE
, and sortby
should be set
equal to "x"
or "y"
to indicate which coordinate
is sorted.
Similarly if Y
is already sorted, then is.sorted.Y
should be set to TRUE
, and sortby
should be set
equal to "x"
or "y"
to indicate which coordinate
is sorted.
If both X
and Y
are sorted on the same coordinate
axis then both is.sorted.X
and is.sorted.Y
should be set to TRUE
, and sortby
should be set
equal to "x"
or "y"
to indicate which coordinate
is sorted.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner rolfturner@posteo.net, and Jens Oehlschlaegel
Given two point patterns X
and Y
this
function finds, for each point of X
,
the nearest point of Y
. The distance between these points
is also computed.
If the argument k
is specified, then the k
-th nearest
neighbours will be found.
Alternatively if X
is a point pattern and Y
is a line
segment pattern, the function finds the nearest line segment to each point
of X
, and computes the distance.
The return value is a data frame, with rows corresponding to
the points of X
. The first column gives the nearest neighbour
distances (i.e. the i
th entry is the distance
from the i
th point of X
to the nearest element of
Y
). The second column gives the indices of the nearest
neighbours (i.e.\ the i
th entry is the index of
the nearest element in Y
.)
If what="dist"
then only the vector of distances is returned.
If what="which"
then only the vector of indices is returned.
The argument k
may be an integer or an integer vector.
If it is a single integer, then the k
-th nearest neighbours
are computed. If it is a vector, then the k[i]
-th nearest
neighbours are computed for each entry k[i]
. For example, setting
k=1:3
will compute the nearest, second-nearest and
third-nearest neighbours. The result is a data frame.
Note that this function is not symmetric in X
and Y
.
To find the nearest neighbour in X
of each point in Y
,
where Y
is a point pattern, use nncross(Y,X)
.
The arguments iX
and iY
are used when
the two point patterns X
and Y
have some points in
common. In this situation nncross(X, Y)
would return some zero
distances. To avoid this, attach a unique integer identifier to
each point, such that two points are identical if their
identifying numbers are equal. Let iX
be the vector of
identifier values for the points in X
, and iY
the vector of identifiers for points in Y
. Then the code
will only compare two points if they have different values of the
identifier. See the Examples.
nndist
for nearest neighbour
distances in a single point pattern.
# two different point patterns
X <- runifrect(15)
Y <- runifrect(20)
N <- nncross(X,Y)$which
# note that length(N) = 15
plot(superimpose(X=X,Y=Y), main="nncross", cols=c("red","blue"))
arrows(X$x, X$y, Y[N]$x, Y[N]$y, length=0.15)
# third-nearest neighbour
NXY <- nncross(X, Y, k=3)
NXY[1:3,]
# second and third nearest neighbours
NXY <- nncross(X, Y, k=2:3)
NXY[1:3,]
# two patterns with some points in common
Z <- runifrect(50)
X <- Z[1:30]
Y <- Z[20:50]
iX <- 1:30
iY <- 20:50
N <- nncross(X,Y, iX, iY)$which
N <- nncross(X,Y, iX, iY, what="which") #faster
plot(superimpose(X=X, Y=Y), main="nncross", cols=c("red","blue"))
arrows(X$x, X$y, Y[N]$x, Y[N]$y, length=0.15)
# point pattern and line segment pattern
X <- runifrect(15)
Y <- psp(runif(10), runif(10), runif(10), runif(10), square(1))
N <- nncross(X,Y)
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