
Computes the neighbourhood density function, a local version of
the
localK(X, ..., rmax = NULL, correction = "Ripley", verbose = TRUE, rvalue=NULL)
localL(X, ..., rmax = NULL, correction = "Ripley", verbose = TRUE, rvalue=NULL)
If rvalue
is given, the result is a numeric vector
of length equal to the number of points in the point pattern.
If rvalue
is absent, the result is
an object of class "fv"
, see fv.object
,
which can be plotted directly using plot.fv
.
Essentially a data frame containing columns
the vector of values of the argument
the theoretical value
together with columns containing the values of the
neighbourhood density function for each point in the pattern.
Column i
corresponds to the i
th point.
The last two columns contain the r
and theo
values.
A point pattern (object of class "ppp"
).
Ignored.
Optional. Maximum desired value of the argument
String specifying the edge correction to be applied.
Options are "none"
, "translate"
, "translation"
,
"Ripley"
,
"isotropic"
or "best"
.
Only one correction may be specified.
Logical flag indicating whether to print progress reports during the calculation.
Optional. A single value of the distance argument
Adrian Baddeley Adrian.Baddeley@curtin.edu.au and Rolf Turner rolfturner@posteo.net
The command localL
computes the neighbourhood density function,
a local version of the localK
computes the corresponding
local analogue of the K-function.
Given a spatial point pattern X
, the neighbourhood density function
X
is computed by
X
, and Kest
).
The value of
By default, the function "fv"
) with a column of the table
containing the function estimates for each point of the pattern
X
.
Alternatively, if the argument rvalue
is given, and it is a
single number, then the function will only be computed for this value
of X
.
Inhomogeneous counterparts of localK
and localL
are computed by localKinhom
and localLinhom
.
Getis, A. and Franklin, J. (1987) Second-order neighbourhood analysis of mapped point patterns. Ecology 68, 473--477.
Kest
,
Lest
,
localKinhom
,
localLinhom
.
X <- ponderosa
# compute all the local L functions
L <- localL(X)
# plot all the local L functions against r
plot(L, main="local L functions for ponderosa", legend=FALSE)
# plot only the local L function for point number 7
plot(L, iso007 ~ r)
# compute the values of L(r) for r = 12 metres
L12 <- localL(X, rvalue=12)
# Spatially interpolate the values of L12
# Compare Figure 5(b) of Getis and Franklin (1987)
X12 <- X %mark% L12
Z <- Smooth(X12, sigma=5, dimyx=128)
plot(Z, col=topo.colors(128), main="smoothed neighbourhood density")
contour(Z, add=TRUE)
points(X, pch=16, cex=0.5)
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