shar (version 1.1)

reconstruct_pattern_marks: reconstruct_pattern_marks

Description

Pattern reconstruction of marks

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, return_input = TRUE,
  simplify = FALSE, verbose = TRUE, plot = FALSE)

Arguments

pattern

ppp.

marked_pattern

ppp (marked; see details).

n_random

Number of randomizations.

e_threshold

Minimum energy to stop reconstruction.

max_runs

Maximum number of iterations of e_threshold is not reached.

no_change

Reconstrucction will stop if energy does not decrease for this number of iterations.

annealing

Probability to keep relocated point even if energy did not decrease.

r_length

Number of intervals from r = 0 to r = rmax the summary functions are evaluated.

return_input

The original input data is returned as last list entry

simplify

If n_random = 1 and return_input = FALSE only pattern will be returned.

verbose

Print progress report.

plot

Plot kmmr function during optimization.

Value

list

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

Tscheschel, A., & Stoyan, D. (2006). Statistical reconstruction of random point patterns. Computational Statistics and Data Analysis, 51(2), 859-871.

Wiegand, T., & Moloney, K. A. (2014). Handbook of spatial point-pattern analysis in ecology. Boca Raton: Chapman and Hall/CRC Press.

See Also

fit_point_process reconstruct_pattern_homo reconstruct_pattern_hetero reconstruct_pattern_cluster

Examples

# NOT RUN {
pattern_recon <- reconstruct_pattern_homo(species_a, n_random = 1, max_runs = 1000,
simplify = TRUE, return_input = FALSE)
marks_sub <- spatstat::subset.ppp(species_a, select = dbh)
marks_recon <- reconstruct_pattern_marks(pattern_recon, marks_sub, n_random = 19, max_runs = 1000)
# }
# NOT RUN {
# }