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Quantile-based quantize commsim (sequential / Markov chain)

Source: R/quantize_commsim.R
quantize_commsim_seq.Rd

Construct a vegan::commsim() object that uses quantize() as a sequential null model: successive simulated matrices form a Markov chain in the space of numeric community matrices. Internally, each simulation "step" advances the chain by re-applying quantize() to the current matrix using the settings provided via ....

Usage

quantize_commsim_seq(...)

Arguments

...

Arguments passed to quantize(), such as breaks, n_strata, transform, offset, zero_stratum, fixed, method, n_iter, etc. Do not supply x or n_iter here; x is provided by vegan and n_iter is set internally from thin. See quantize() for details.

Value

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

Details

This model is sequential: simulated matrices form a Markov chain. The current matrix is updated in-place by repeated calls to the randomization model, and successive matrices are obtained by advancing the chain.

In vegan::simulate.nullmodel(), the control arguments behave as:

  • nsim: number of matrices to store from the chain.

  • thin: number of trades per step. Each "step" of the chain applies thin trades of the chosen method to the current state before possibly storing it.

  • burnin: number of initial steps to perform (each with thin trades) before storing any matrices, i.e. the Markov chain burn-in.

There is no n_iter argument here: mixing is controlled entirely by thin (trades per step) and burnin (number of initial steps discarded), in the same spirit as sequential swap / curveball models in vegan.

Examples

# \donttest{
  library(vegan)

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

  cs <- quantize_commsim_seq(
    n_strata = 5,
    method   = "curvecat"
  )

  nm <- nullmodel(x, cs)

  sims <- simulate(
    nm,
    nsim   = 999,
    thin   = 10,    # 10 quantize updates between stored states
    burnin = 100    # 100 initial steps discarded
  )
# }