fit_point_process: fit_point_process

Description

Fit point process to randomize data

Usage

fit_point_process(
  pattern,
  n_random = 1,
  process = "poisson",
  return_para = FALSE,
  return_input = TRUE,
  simplify = FALSE,
  verbose = TRUE
)

Value

rd_pat

Arguments

pattern: ppp object with point pattern
n_random: Integer with number of randomizations.
process: Character specifying which point process model to use. Either "poisson" or "cluster".
return_para: Logical if fitted parameters should be returned.
return_input: Logical if the original input data is returned.
simplify: Logical if only pattern will be returned if n_random = 1 and return_input = FALSE.
verbose: Logical if progress report is printed.

Details

The functions randomizes the observed point pattern by fitting a point process to the data and simulating n_random patterns using the fitted point process. It is possible to choose between a Poisson process or a Thomas cluster process model. For more information about the point process models, see e.g. Wiegand & Moloney (2014).

References

Plotkin, J.B., Potts, M.D., Leslie, N., Manokaran, N., LaFrankie, J.V., Ashton, P.S., 2000. Species-area curves, spatial aggregation, and habitat specialization in tropical forests. Journal of Theoretical Biology 207, 81–99. <https://doi.org/10.1006/jtbi.2000.2158>

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

Run this code

pattern_fitted <- fit_point_process(pattern = species_a, n_random = 39)

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