calculate_energy: calculate_energy

Description Usage Arguments Details Value References See Also Examples

View source: R/calculate_energy.R

Description

Calculate mean energy

Usage

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

Arguments

pattern

List with reconstructed patterns.

weights

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

return_mean

Return the mean energy.

comp_fast

If pattern contains more points than threshold, summary functions are estimated in a computational fast way.

verbose

Print progress report.

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.

Value

numeric

References

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.

See Also

plot_energy
reconstruct_pattern_homo
reconstruct_pattern_hetero
reconstruct_pattern_cluster
plot_randomized_pattern

Examples

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

## Not run: 
marks_sub <- spatstat::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)

## End(Not run)

mhesselbarth/SHAR documentation built on Oct. 17, 2020, 8:58 p.m.