Nullcat-based commsim (non-sequential)

Construct a vegan::commsim() object that uses nullcat() as a non-sequential null model for categorical / integer matrices. Each simulated matrix is generated independently by applying nullcat() with n_iter trades starting from the original matrix.

Usage

nullcat_commsim(
  n_iter = 10000,
  method = nullcat_methods(),
  output = c("category", "index")
)

Arguments

n_iter

Integer, number of iterations (trades) per simulated matrix. Must be a positive integer. Default is 1e4.

method

Character specifying which nullcat randomization algorithm to use. See nullcat() and nullcat_methods() for details.

output

Character, passed to nullcat(output = ...). Typically "category" (default) or "index".

Value

An object of class "commsim" suitable for use with vegan::nullmodel() and vegan::oecosimu().

Details

This generates a commsim object that is non-sequential: each simulated matrix starts from the original matrix and is randomized independently using n_iter trades of the chosen method.

When used via vegan::simulate.nullmodel(), the arguments behave as:

• nsim: number of simulated matrices to generate.

• n_iter (here, in nullcat_commsim()): number of trades per simulated matrix (controls how strongly each replicate is shuffled).

• burnin and thin: are ignored for this commsim, because isSeq = FALSE (the simulations are not a Markov chain).

In other words, treat n_iter as the tuning parameter for how thoroughly each independent null matrix is randomized.

See also

nullcat_batch() if you just want a batch of null matrices without going through vegan.

Examples

# \donttest{
  library(vegan)
#> Loading required package: permute

  x  <- matrix(sample(1:5, 50, replace = TRUE), 10, 5)
  cs <- nullcat_commsim(n_iter = 1e4, method = "curvecat")

  nm   <- nullmodel(x, cs)
  sims <- simulate(nm, nsim = 999)
# }