nnorient(X, ..., cumulative = FALSE, correction, k = 1, unit = c("degree", "radian"), domain = NULL, ratio = FALSE)"ppp").
circdensity to control
the kernel smoothing, if cumulative=FALSE.
cumulative=FALSE, the default) or the cumulative
distribution function (cumulative=TRUE).
"none", "bord.modif",
"good" and "best".
Alternatively correction="all" selects all options.
TRUE, the numerator and denominator of
each edge-corrected estimate will also be saved,
for use in analysing replicated point patterns.
"degree" or "radian".
domain.
"fv")
containing the estimates of the probability density or the
cumulative distribution function of angles,
in degrees (if unit="degree")
or radians (if unit="radian").
X
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. If cumulative=FALSE (the default),
a kernel estimate of the probability density of the angles
is calculated using circdensity.
This is the function $theta(phi)$ defined
in Illian et al (2008), equation (4.5.3), page 253.
If 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 plot.fv.
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
correction.
It is also possible to calculate an estimate of the probability
density from the cumulative distribution function,
by numerical differentiation.
Use deriv.fv with the argument Dperiodic=TRUE.
pairorient
rose(nnorient(redwood, adjust=0.6), col="grey")
plot(CDF <- nnorient(redwood, cumulative=TRUE))
Run the code above in your browser using DataLab