by.ppp

0th

Percentile

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.

Keywords
manip, methods, spatial
Usage
## S3 method for class 'ppp':
by(data, INDICES=marks(data), FUN, ...)
Arguments
data
Point pattern (object of class "ppp").
INDICES
Grouping variable. Either a factor, a pixel image with factor values, or a tessellation.
FUN
Function to be applied to subsets of data.
...
Additional arguments to FUN.
Details

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 indata. The levels ofINDICESdetermine the destination of each point indata. Theith point ofdatawill be placed in the sub-patternsplit.ppp(data)\$lwherel = f[i].
• a pixel image (object of class"im") with factor values. The pixel value ofINDICESat each point ofdatawill be used as the classifying variable.
• a tessellation (object of class"tess"). Each point ofdatawill 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.

Value

• A list (also of class "listof") containing the results returned from FUN for each of the subpatterns.

ppp, split.ppp, cut.ppp, tess, im.

• by.ppp
Examples
# multitype point pattern, broken down by type
data(amacrine)
by(amacrine, FUN=density)
by(amacrine, FUN=function(x) { min(nndist(x)) } )

# how to pass additional arguments to FUN
by(amacrine, FUN=clarkevans, correction=c("Donnelly","cdf"))

# point pattern broken down by tessellation
data(swedishpines)
unlist(lapply(B, as.numeric))