shar (version 1.1)

fit_point_process: fit_point_process

Description

Create random patterns by point process fitting

Usage

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

Arguments

pattern

List with reconstructed patterns.

n_random

Number of randomized RasterLayers.

process

What point process to use. Either 'poisson' or 'cluster'.

return_input

The original input data is returned as last list entry.

simplify

If n_random = 1 and return_input = FALSE only pattern will be returned.

verbose

Print progress report.

Value

list

Details

The functions randomizes the observed pattern by fitting a point process to the data. It is possible to choose between a Poisson process or a Thomas cluster process.

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(1), 81-99.

Examples

# NOT RUN {
pattern_fitted <- fit_point_process(pattern = species_a, n_random = 39)

# }