Lest | R Documentation |
Calculates an estimate of the L
-function (Besag's
transformation of Ripley's K
-function)
for a spatial point pattern.
Lest(X, ..., correction)
X |
The observed point pattern,
from which an estimate of |
correction,... |
Other arguments passed to |
This command computes an estimate of the L
-function
for the spatial point pattern X
.
The L
-function is a transformation of Ripley's K
-function,
L(r) = \sqrt{\frac{K(r)}{\pi}}
where K(r)
is the K
-function.
See Kest
for information
about Ripley's K
-function. The transformation to L
was
proposed by Besag (1977).
The command Lest
first calls
Kest
to compute the estimate of the K
-function,
and then applies the square root transformation.
For a completely random (uniform Poisson) point pattern,
the theoretical value of the L
-function is L(r) = r
.
The square root also has the effect of stabilising
the variance of the estimator, so that L(r)
is more appropriate
for use in simulation envelopes and hypothesis tests.
See Kest
for the list of arguments.
An object of class "fv"
, see fv.object
,
which can be plotted directly using plot.fv
.
Essentially a data frame containing columns
r |
the vector of values of the argument |
theo |
the theoretical value |
together with columns named
"border"
, "bord.modif"
,
"iso"
and/or "trans"
,
according to the selected edge corrections. These columns contain
estimates of the function L(r)
obtained by the edge corrections
named.
If the argument var.approx=TRUE
is given, the return value
includes columns rip
and ls
containing approximations
to the variance of \hat L(r)
under CSR.
These are obtained by the delta method from the variance
approximations described in Kest
.
and \rolf
Besag, J. (1977) Discussion of Dr Ripley's paper. Journal of the Royal Statistical Society, Series B, 39, 193–195.
Kest
,
pcf
L <- Lest(cells)
plot(L, main="L function for cells")
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