reconstruct_pattern: reconstruct_pattern

In mhesselbarth/SHAR: Species-Habitat Associations

reconstruct_pattern

Description

Pattern reconstruction

Usage

reconstruct_pattern(
  pattern,
  method = "homo",
  n_random = 1,
  e_threshold = 0.01,
  max_runs = 10000,
  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
)

Arguments

pattern	ppp object with pattern.
method	Character with specifying the method. Either "homo", "cluster" or "hetero".
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.
weights	Vector with weights used to calculate energy. The first number refers to Gest(r), the second number to pcf(r).
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.
stoyan	Coefficient for Stoyan's bandwidth selection rule.
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 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, use weights = c(1, 1).

spatstat sets r_length to 513 by default. However, a lower value decreases the computational time, while increasing the "bumpiness" of the summary function.

The arguments n_points and window are used for method="homo" only.

method="homo":

The algorithm starts with a random pattern.

method="cluster":

The algorithm starts with a random but clustered pattern.

method="hetero":

The algorithm starts with a random but heterogeneous pattern.

Value

rd_pat

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

calculate_energy
reconstruct_pattern_marks

Examples

## Not run: 
pattern_recon <- reconstruct_pattern(species_b, n_random = 19, max_runs = 1000)

## End(Not run)

mhesselbarth/SHAR documentation built on Dec. 17, 2024, 3:45 p.m.