Fit point process to randomize data
fit_point_process(
pattern,
n_random = 1,
process = "poisson",
return_para = FALSE,
return_input = TRUE,
simplify = FALSE,
verbose = TRUE
)
rd_pat
ppp object with point pattern
Integer with number of randomizations.
Character specifying which point process model to use.
Either "poisson"
or "cluster"
.
Logical if fitted parameters should be returned.
Logical if the original input data is returned.
Logical if only pattern will be returned if n_random = 1
and return_input = FALSE
.
Logical if progress report is printed.
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).
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
pattern_fitted <- fit_point_process(pattern = species_a, n_random = 39)
Run the code above in your browser using DataLab