reconstruct_pattern: reconstruct_pattern

Description Usage Arguments Details Value References See Also Examples

View source: R/reconstruct_pattern.R

Description

Pattern reconstruction

Usage

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reconstruct_pattern(
  pattern,
  method = "homo",
  n_random = 1,
  e_threshold = 0.01,
  max_runs = 1000,
  no_change = Inf,
  annealing = 0.01,
  comp_fast = 1000,
  n_points = NULL,
  window = NULL,
  weights = c(0.5, 0.5),
  r_length = 250,
  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.

comp_fast

Integer with threshold at which summary functions are estimated in a computational fast way.

n_points

Integer with number of points to be simulated.

window

owin object with window of simulated pattern.

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.

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.

For large patterns (n > comp_fast) the pair correlation function can be estimated from Ripley's K-function without edge correction. This decreases the computational time. For more information see estimate_pcf_fast.

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(0.5, 0.5).

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

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

## End(Not run)

shar documentation built on Jan. 4, 2022, 5:08 p.m.