Calculate mean energy
Usage
calculate_energy(
pattern,
weights = c(1, 1),
return_mean = FALSE,
verbose = TRUE
)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.
- 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.
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
Examples
pattern_random <- fit_point_process(species_a, n_random = 19)
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calculate_energy(pattern_random)
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#> randomized_1 randomized_2 randomized_3 randomized_4 randomized_5
#> 0.11628685 0.11424406 0.14504193 0.13808568 0.12817459
#> randomized_6 randomized_7 randomized_8 randomized_9 randomized_10
#> 0.14515877 0.12545576 0.12915436 0.14249547 0.13247994
#> randomized_11 randomized_12 randomized_13 randomized_14 randomized_15
#> 0.16342716 0.09590454 0.11600398 0.11044160 0.09659266
#> randomized_16 randomized_17 randomized_18 randomized_19
#> 0.13816854 0.13149486 0.08214429 0.13619203
calculate_energy(pattern_random, return_mean = TRUE)
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#> [1] 0.1256288
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)
}