Calculates an estimate of the \(L\)-function (Besag's transformation of Ripley's \(K\)-function) for a spatial point pattern.
Lest(X, ...)The observed point pattern,
from which an estimate of \(L(r)\) will be computed.
An object of class "ppp", or data
in any format acceptable to as.ppp().
Other arguments passed to Kest
to control the estimation procedure.
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 \(r\) at which the function \(L\) has been estimated
the theoretical value \(L(r) = r\) for a stationary Poisson process
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.
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.
Besag, J. (1977) Discussion of Dr Ripley's paper. Journal of the Royal Statistical Society, Series B, 39, 193--195.
# NOT RUN {
data(cells)
L <- Lest(cells)
plot(L, main="L function for cells")
# }
Run the code above in your browser using DataLab