Apply a Function to a Point Pattern Broken Down by Factor
Splits a point pattern into sub-patterns, and applies the function to each sub-pattern.
## S3 method for class 'ppp' by(data, INDICES=marks(data), FUN, ...)
data |
Point pattern (object of class |
INDICES |
Grouping variable. Either a factor, a pixel image with factor values, or a tessellation. |
FUN |
Function to be applied to subsets of |
... |
Additional arguments to |
This is a method for the generic function by
for point patterns (class "ppp").
The point pattern data is first divided into subsets
according to INDICES. Then the function FUN
is applied to each subset. The results of each computation are
returned in a list.
The argument INDICES may be
a factor, of length equal to the number of points in data.
The levels of INDICES
determine the destination of each point in data.
The ith point of data will be placed in the sub-pattern
split.ppp(data)$l where l = f[i].
a pixel image (object of class "im") with factor values.
The pixel value of INDICES
at each point of data will be used as the classifying variable.
a tessellation (object of class "tess").
Each point of data will be classified according to
the tile of the tessellation into which it falls.
If INDICES is missing, then data must be a multitype point pattern
(a marked point pattern whose marks vector is a factor).
Then the effect is that the points of each type
are separated into different point patterns.
A list (also of class "anylist" or "solist" as
appropriate) containing the results returned
from FUN for each of the subpatterns.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
# multitype point pattern, broken down by type
data(amacrine)
by(amacrine, FUN=minnndist)
by(amacrine, FUN=function(x) { intensity(unmark(x)) })
if(require(spatstat.core)) {
# how to pass additional arguments to FUN
by(amacrine, FUN=clarkevans, correction=c("Donnelly","cdf"))
}
# point pattern broken down by tessellation
data(swedishpines)
tes <- quadrats(swedishpines, 4,4)
## compute minimum nearest neighbour distance for points in each tile
B <- by(swedishpines, tes, minnndist)
if(require(spatstat.core)) {
B <- by(swedishpines, tes, clarkevans, correction="Donnelly")
simplify2array(B)
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