reconstruct_pattern_marks {shar}

reconstruct_pattern_marks

Description

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.

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.

Value

rd_mar

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

See Also

fit_point_process
reconstruct_pattern

Examples

## Not run: 
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)

## End(Not run)