Computes the distribution of the orientation of the vectors from each point to its nearest neighbour.
1 2 3
Point pattern (object of class
Arguments passed to
Logical value specifying whether to estimate the probability density
Character vector specifying edge correction or corrections.
Integer. The kth nearest neighbour will be used.
Unit in which the angles should be expressed.
Optional window. The first point x[i] of each pair of points
will be constrained to lie in
This algorithm considers each point in the pattern
and finds its nearest neighbour (or kth nearest neighour).
The direction of the arrow joining the data point to its neighbour
is measured, as an angle in degrees or radians,
anticlockwise from the x axis.
cumulative=FALSE (the default),
a kernel estimate of the probability density of the angles
is calculated using
This is the function theta(phi) defined
in Illian et al (2008), equation (4.5.3), page 253.
cumulative=TRUE, then the cumulative distribution
function of these angles is calculated.
In either case the result can be plotted as a rose diagram by
rose, or as a function plot by
The algorithm gives each observed direction a weight,
determined by an edge correction, to adjust for the fact that some
interpoint distances are more likely to be observed than others.
The choice of edge correction or corrections is determined by the argument
It is also possible to calculate an estimate of the probability
density from the cumulative distribution function,
by numerical differentiation.
deriv.fv with the argument
A function value table (object of class
containing the estimates of the probability density or the
cumulative distribution function of angles,
in degrees (if
or radians (if
Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D. (2008) Statistical Analysis and Modelling of Spatial Point Patterns. Wiley.
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.