run_optimization_min_conf: run_optimization_min_conf
In spectre: Predict Regional Community Composition

Description Usage Arguments Details Value References

View source: R/run_optimization_min_conf.R

Generate an optimized estimate of community composition (species presences and absences) for every site in the study area.

run_optimization_min_conf(
  alpha_list,
  total_gamma,
  target,
  max_iterations,
  partial_solution = NULL,
  fixed_species = NULL,
  seed = NA,
  verbose = TRUE,
  interruptible = TRUE
)

`alpha_list`	`Matrix` of predicted alpha diversity (species richness) in each cell.
`total_gamma`	Total number of species present throughout the entire landscape.
`target`	Pairwise matrix of species in common between each site by site pair. Only the upper triangle of the matrix is actually needed.
`max_iterations`	The maximum number of iterations that the optimization algorithm may run through before stopping.
`partial_solution`	An initial `matrix` of species presences and absences for each site in the landscape. The total number of presences must match the estimated species richness of each site.
`fixed_species`	Fixed partial solution with species that are considered as given. Those species are not going to be changed during optimization.
`seed`	Seed for random number generator. Seed must be a positive integer value. `seed = NA` means that a random integer is used as seed.
`verbose`	If `TRUE` (default), a progress report is printed during the optimization run.
`interruptible`	Allow a run to be interrupted before completion.

run_optimization_min_conf is the core function of the spectre package. The underlying algorithm of this function is adapted from Mokany et al. (2011). A pairwise commonness matrix (having the same structure as the target matrix) is calculated from the partial_solution matrix and the value difference with the target determined. If a difference is present and depending on the set stopping criteria the algorithm continues. A random site in the presence/absence matrix is selected, and a random presence record at this site replaced with an absence. Every absence in the selected site is then individually flipped to a presence and the value difference with the objective recorded. The presence record which resulted in the lowest value difference (minimum conflict) is retained. This cycle continues, with a random site selected every iteration, until the pairwise commonness and objective matrices match or the algorithm runs beyond the max_iterations.

A species presence/absence matrix of the study landscape.

Mokany, K., Harwood, T.D., Overton, J.M., Barker, G.M., & Ferrier, S. (2011). Combining α and β diversity models to fill gaps in our knowledge of biodiversity. Ecology Letters, 14(10), 1043-1051.

spectre documentation built on July 17, 2021, 9:08 a.m.