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.

vector

## 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

`plot_energy`
`reconstruct_pattern`
`fit_point_process`

## Examples

``````pattern_random <- fit_point_process(species_a, n_random = 19)
#> Unmarking provided input pattern.
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calculate_energy(pattern_random)
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#>  randomized_1  randomized_2  randomized_3  randomized_4  randomized_5
#>    0.15879942    0.15783350    0.10696079    0.12465796    0.08076916
#>  randomized_6  randomized_7  randomized_8  randomized_9 randomized_10
#>    0.10200352    0.09417464    0.10533838    0.11251767    0.12803654
#> randomized_11 randomized_12 randomized_13 randomized_14 randomized_15
#>    0.12981006    0.16507257    0.12181468    0.14356497    0.14001074
#> randomized_16 randomized_17 randomized_18 randomized_19
#>    0.12373462    0.13498715    0.14821297    0.14825450
calculate_energy(pattern_random, return_mean = TRUE)
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#> [1] 0.1277134

if (FALSE) { # \dontrun{
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)
} # }

``````