reconstruct_pattern_hetero: reconstruct_pattern_hetero
In mhesselbarth/SHAR: Species-Habitat Associations

Description Usage Arguments Details Value References See Also Examples

View source: R/reconstruct_pattern_hetero.R

Pattern reconstruction for heterogenous patterns

reconstruct_pattern_hetero(
  pattern,
  n_random = 1,
  e_threshold = 0.01,
  max_runs = 1000,
  no_change = Inf,
  annealing = 0.01,
  comp_fast = 1000,
  weights = c(0.5, 0.5),
  r_length = 250,
  return_input = TRUE,
  simplify = FALSE,
  verbose = TRUE,
  plot = FALSE
)

`pattern`	ppp.
`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.
`comp_fast`	If pattern contains more points than threshold, summary functions are estimated in a computational fast way.
`weights`	Weights used to calculate energy. The first number refers to Gest(r), the second number to pcf(r).
`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 pcf function during optimization.

The functions randomizes the observed pattern by using pattern reconstruction as described in Tscheschel & Stoyan (2006) and Wiegand & Moloney (2014). The algorithm starts with a random but heterogenous pattern, 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.

list

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.

calculate_energy
plot_randomized_pattern reconstruct_pattern_homo
reconstruct_pattern_cluster
reconstruct_pattern_marks

## Not run: 
input_pattern <- spatstat::rpoispp(lambda = function(x, y) {100 * exp(-3 * x)}, nsim = 1)

pattern_recon <- reconstruct_pattern_hetero(input_pattern, n_random = 19, max_runs = 1000)

## End(Not run)