Nearest Neighbour Orientation Distribution
Computes the distribution of the orientation of the vectors from each point to its nearest neighbour.
nnorient(X, ..., cumulative = FALSE, correction, k = 1, unit = c("degree", "radian"), domain = NULL, ratio = FALSE)
- Point pattern (object of class
- Arguments passed to
circdensityto control the kernel smoothing, if
- Logical value specifying whether to estimate the probability density
cumulative=FALSE, the default) or the cumulative distribution function (
- Character vector specifying edge correction or corrections.
- Integer. The $k$th nearest neighbour will be used.
TRUE, the numerator and denominator of each edge-corrected estimate will also be saved, for use in analysing replicated point patterns.
- 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 $k$th 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 $\vartheta(\phi)$ 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
"fv") containing the estimates of the probability density or the cumulative distribution function of angles, in degrees (if
unit="degree") or radians (if
Illian, J., Penttinen, A., Stoyan, H. and Stoyan, D. (2008) Statistical Analysis and Modelling of Spatial Point Patterns. Wiley.
rose(nnorient(redwood, adjust=0.6), col="grey") plot(CDF <- nnorient(redwood, cumulative=TRUE))