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Stratified randomization of a quantitative community matrix

Source: R/quantize.R
quantize.Rd

quantize() is a community null model for quantitative community data (e.g. abundance, biomass, or occurrence probability). It works by converting quantitative values into discrete strata, randomizing the stratified matrix using a categorical null model, and reassigning quantitative values within strata according to a specified constraint.

Usage

quantize(
  x = NULL,
  prep = NULL,
  method = nullcat_methods(),
  fixed = c("cell", "stratum", "row", "col"),
  breaks = NULL,
  n_strata = 5,
  transform = identity,
  offset = 0,
  zero_stratum = FALSE,
  n_iter = 1000,
  seed = NULL
)

Arguments

x

Community matrix with sites in rows, species in columns, and nonnegative quantitative values in cells. Ignored if prep is supplied.

prep

Optional precomputed object returned by quantize_prep(). If supplied, x is ignored and all overhead (stratification, pools, etc.) is taken from prep, which is typically much faster when generating many randomizations of the same dataset.

method

Character string specifying the null model algorithm. The default "curvecat" uses the categorical curveball algorithm. See nullcat() for alternative options.

fixed

Character string specifying the level at which quantitative values are held fixed during randomization. One of:

  • "cell" (the default; only available when method = "curvecat"): values remain attached to their original cells and move with them during the categorical randomization. Row and column value distributions are not preserved, but the mapping between each original cell and its randomized destination is fixed.

  • "stratum": values are shuffled globally within each stratum, holding only the overall stratum-level value distribution fixed.

  • "row": values are shuffled within strata separately for each row, holding each row’s value multiset fixed. Not compatible with all methods.

  • "col": values are shuffled within strata separately for each column, holding each column’s value multiset fixed.

Note that this interacts with method: different null models fix different margins in the underlying binary representation.

breaks

Numeric vector of stratum breakpoints.

n_strata

Integer giving the number of strata to split the data into. Must be 2 or greater. Larger values yield randomizations with less mixing but higher fidelity to the original marginal distributions. Default is 5. Ignored unless breaks = NULL.

transform

A function used to transform the values in x before assigning them to n_strata equal-width intervals. Examples include sqrt, log, rank (for equal-occupancy strata), etc.; the default is identity. If zero_stratum = TRUE, the transformation is only applied on nonzero values. The function should pass NA values. This argument is ignored unless breaks = NULL.

offset

Numeric value between -1 and 1 (default 0) indicating how much to shift stratum breakpoints relative to the binwidth (applied during quantization as: breaks <- breaks + offset * bw). To assess sensitivity to stratum boundaries, run quantize() multiple times with different offset values. Ignored unless breaks = NULL.

zero_stratum

Logical indicating whether to segregate zeros into their own stratum. If FALSE (the default), zeros will likely be combined into a stratum that also includes small positive numbers. If breaks is specified, zero simply gets added as an additional break; if not, one of the n_strata will represent zeros and the others will be nonzero ranges.

n_iter

Number of iterations. Default is 1000. Larger values yield more thorough mixing. Ignored for non-sequential methods. Minimum burn-in times can be estimated with suggest_n_iter().

seed

Integer used to seed random number generator, for reproducibility.

Value

A randomized version of x, with the same dimensions and dimnames. For method = "curvecat", the quantitative values are reassigned within strata while preserving row and column stratum multisets. For binary methods, the result corresponds to applying the chosen binary null model to each stratum and recombining.

Details

This approach provides a framework for preserving row and/or column value distributions in continuous data. When using fixed = "row" or fixed = "col", one dimension's value multisets are preserved exactly while the other is preserved at the resolution of strata, approximating a fixed-fixed null model for quantitative data. The number of strata controls the tradeoff between preservation fidelity and randomization strength.

By default, quantize() will compute all necessary overhead for a given dataset (strata, pools, etc.) internally. For repeated randomization of the same matrix (e.g. to build a null distribution), this overhead can be computed once using quantize_prep() and reused by supplying the resulting object via the prep argument.

See also

quantize_batch() for efficient generation of multiple randomized matrices; quantize_commsim() for integration with vegan.

Examples

# toy quantitative community matrix
set.seed(1)
comm <- matrix(rexp(50 * 40), nrow = 50,
               dimnames = list(paste0("site", 1:50),
                               paste0("sp", 1:40)))

# default: curvecat-backed stratified randomization
rand1 <- quantize(comm)

# change stratification and preservation mode
rand2 <- quantize(comm, n_strata = 4,
                  transform = sqrt,
                  fixed  = "row",
                  n_iter    = 2000)

# use a different randomization algorithm
rand3 <- quantize(comm, method = "swapcat", n_iter = 10000)

# precompute overhead and reuse for many randomizations
prep  <- quantize_prep(comm, method = "curvecat",
                       n_strata = 5, fixed = "row")
rand4 <- quantize(prep = prep)
rand5 <- quantize(prep = prep)