reconstruct_pattern_marks

Pattern reconstruction of marked pattern

Usage

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
)

Arguments

pattern: ppp object with pattern.
marked_pattern: ppp object with marked pattern. See Details section for more information.
n_random: Integer with number of randomizations.
e_threshold: Double with minimum energy to stop reconstruction.
max_runs: Integer with maximum number of iterations if e_threshold is not reached.
no_change: Integer with number of iterations at which the reconstruction will stop if the energy does not decrease.
annealing: Double with probability to keep relocated point even if energy did not decrease.
r_length: Integer with number of intervals from r = 0 to r = rmax for which the summary functions are evaluated.
r_max: Double with maximum distance used during calculation of summary functions. If NULL, will be estimated from data.
return_input: Logical if the original input data is returned.
simplify: Logical if only pattern will be returned if n_random = 1 and return_input = FALSE.
verbose: Logical if progress report is printed.
plot: Logical if pcf(r) function is plotted and updated during optimization.

Value

rd_mar

Details

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.

References

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

Examples

if (FALSE) { # \dontrun{
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)
} # }