reconstruct_pattern( pattern, method = "homo", n_random = 1, e_threshold = 0.01, max_runs, no_change = Inf, annealing = 0.01, weights = c(1, 1), r_length = 255, r_max = NULL, stoyan = 0.15, return_input = TRUE, simplify = FALSE, verbose = TRUE, plot = FALSE )
ppp object with pattern.
Character with specifying the method. Either
Integer with number of randomizations.
Double with minimum energy to stop reconstruction.
Integer with maximum number of iterations if
e_thresholdis not reached.
Integer with number of iterations at which the reconstruction will stop if the energy does not decrease.
Double with probability to keep relocated point even if energy did not decrease.
Vector with weights used to calculate energy. The first number refers to Gest(r), the second number to pcf(r).
Integer with number of intervals from
r=rmaxfor which the summary functions are evaluated.
Double with maximum distance used during calculation of summary functions. If
NULL, will be estimated from data.
Coefficient for Stoyan's bandwidth selection rule.
Logical if the original input data is returned.
Logical if only pattern will be returned if
Logical if progress report is printed.
Logical if pcf(r) function is plotted and updated during optimization.
The functions randomizes the observed pattern by using pattern reconstruction as described in Tscheschel & Stoyan (2006) and Wiegand & Moloney (2014). The algorithm shifts a point to a new location and keeps the change only, if the deviation between the observed and the reconstructed pattern decreases. The pair correlation function and the nearest neighbour distance function are used to describe the patterns.
The reconstruction can be stopped automatically if for n steps the energy does not
decrease. The number of steps can be controlled by
no_change and is set to
no_change = Inf as default to never stop automatically.
The weights must be 0 < sum(weights) <= 1. To weight both summary functions identical,
weights = c(1, 1).
r_length to 513 by default. However, a lower value decreases
the computational time, while increasing the "bumpiness" of the summary function.
window are used for
Kirkpatrick, S., Gelatt, C.D.Jr., Vecchi, M.P., 1983. Optimization by simulated annealing. Science 220, 671–680. <https://doi.org/10.1126/science.220.4598.671>
Tscheschel, A., Stoyan, D., 2006. Statistical reconstruction of random point patterns. Computational Statistics and Data Analysis 51, 859–871. <https://doi.org/10.1016/j.csda.2005.09.007>
Wiegand, T., Moloney, K.A., 2014. Handbook of spatial point-pattern analysis in ecology. Chapman and Hall/CRC Press, Boca Raton. ISBN 978-1-4200-8254-8