shar (version 2.0.4)

calculate_energy: calculate_energy

Description

Calculate mean energy

Usage

calculate_energy(
  pattern,
  weights = c(0.5, 0.5),
  return_mean = FALSE,
  comp_fast = 1000,
  verbose = TRUE
)

Value

vector

Arguments

pattern

List with reconstructed patterns.

weights

Vector with weights used to calculate energy. The first number refers to Gest(r), the second number to pcf(r).

return_mean

Logical if the mean energy is returned.

comp_fast

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

verbose

Logical if progress report is printed.

Details

The function calculates the mean energy (or deviation) between the observed pattern and all reconstructed patterns (for more information see Tscheschel & Stoyan (2006) or Wiegand & Moloney (2014)). The pair correlation function and the nearest neighbour distance function are used to describe the patterns. For large patterns comp_fast = TRUE decreases the computational demand, because no edge correction is used and the pair correlation function is estimated based on Ripley's K-function. For more information see estimate_pcf_fast.

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

plot_energy
reconstruct_pattern
fit_point_process

Examples

pattern_random <- fit_point_process(species_a, n_random = 19)
calculate_energy(pattern_random)
calculate_energy(pattern_random, return_mean = TRUE)

if (FALSE) {
marks_sub <- spatstat.geom::subset.ppp(species_a, select = dbh)
marks_recon <- reconstruct_pattern_marks(pattern_random$randomized[[1]], marks_sub,
n_random = 19, max_runs = 1000)
calculate_energy(marks_recon, return_mean = FALSE)
}