This low-level function calculates estimates of
the cumulative distribution function
$$F(r) = P(D \le r)$$
of a distance variable \(D\), given a vector of observed values of
\(D\) and other information.
Examples of this concept include the empty space distance function
computed by Fest and the nearest-neighbour distance
distribution function Gest.
This function compileCDF
and its siblings compileK and compilepcf
are useful for code development and for teaching,
because they perform a common task, and do the housekeeping required to
make an object of class "fv" that represents the estimated
function. However, they are not very efficient.
The argument D should be a numeric vector of shortest distances
measured from each ‘query’ point to the ‘target’ set.
The argument B should be a numeric vector of shortest distances
measured from each ‘query’ point to the boundary of the window
of observation.
All entries of D and B should be non-negative.
compileCDF calculates estimates of the cumulative distribution
function \(F(r)\) using the border method (reduced sample
estimator), the Kaplan-Meier estimator and, if han.denom is
given, the Hanisch-Chiu-Stoyan estimator.
See Chapter 8 of Baddeley, Rubak and Turner (2015).
The result is an object of class "fv" representing the
estimated function.
Additional columns (such as a column giving the theoretical
value) must be added by the user, with the aid of
bind.fv.