Pattern reconstruction of marked pattern
reconstruct_pattern_marks(
pattern,
marked_pattern,
n_random = 1,
e_threshold = 0.01,
max_runs = 10000,
no_change = Inf,
annealing = 0.01,
r_length = 250,
r_max = NULL,
return_input = TRUE,
simplify = FALSE,
verbose = TRUE,
plot = FALSE
)
rd_mar
ppp object with pattern.
ppp object with marked pattern. See Details section for more information.
Integer with number of randomizations.
Double with minimum energy to stop reconstruction.
Integer with maximum number of iterations if e_threshold
is 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.
Integer with number of intervals from r = 0
to r = rmax
for which
the summary functions are evaluated.
Double with maximum distance used during calculation of summary functions. If NULL
,
will be estimated from data.
Logical if the original input data is returned.
Logical if only pattern will be returned if n_random = 1
and return_input = FALSE
.
Logical if progress report is printed.
Logical if pcf(r) function is plotted and updated during optimization.
The function randomizes the numeric marks of a point pattern using pattern reconstruction
as described in Tscheschel & Stoyan (2006) and Wiegand & Moloney (2014). Therefore,
an unmarked as well as a marked pattern must be provided. The unmarked pattern must have
the spatial characteristics and the same observation window and number of points
as the marked one (see reconstruct_pattern_*
or fit_point_process
).
Marks must be numeric because the mark-correlation function is used as summary function.
Two randomly chosen marks are switch each iterations and changes only kept if the
deviation between the observed and the reconstructed pattern decreases.
spatstat
sets r_length
to 513 by default. However, a lower value decreases
the computational time while increasing the "bumpiness" of the summary function.
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
fit_point_process
reconstruct_pattern
if (FALSE) {
pattern_recon <- reconstruct_pattern(species_a, n_random = 1, max_runs = 1000,
simplify = TRUE, return_input = FALSE)
marks_sub <- spatstat.geom::subset.ppp(species_a, select = dbh)
marks_recon <- reconstruct_pattern_marks(pattern_recon, marks_sub,
n_random = 19, max_runs = 1000)
}
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