Quantize-based commsim (non-sequential)

Construct a vegan::commsim() object that uses quantize() as a non-sequential null model for numeric community matrices. Each simulated matrix is generated independently by applying quantize() with n_iter trades (via its internal call to nullcat()) starting from the original matrix.

Usage

quantize_commsim(n_iter = 10000, ...)

Arguments

n_iter

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

...

Arguments passed to quantize(), such as breaks, n_strata, transform, offset, zero_stratum, fixed, method, etc. Do not supply x or n_iter here; these are set internally by quantize_commsim(). See quantize() for details.

Value

An object of class "commsim" suitable for 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

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

Examples

# \donttest{
  library(vegan)

  x <- matrix(rexp(50), 10, 5)

  cs <- quantize_commsim(
    n_strata = 10,
    method   = "curvecat",
    n_iter   = 1000L
  )

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