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Stochastic Resampling of Ozkan's pTO Index (Islem 2 / Run 2)

Source: R/ozkan_pto_resample.R
ozkan_pto_resample.Rd

Implements the stochastic resampling procedure from Ozkan's Excel macro (Islem 2). First performs a jackknife (Islem 1) to identify "happy" (contributing) and "unhappy" (non-contributing) species, then runs stochastic resampling where unhappy species are always included and happy species are randomly included with 50\

Usage

ozkan_pto_resample(community, tax_tree, n_iter = 101L, seed = NULL)

Arguments

community

A named numeric vector of species abundances. Names must match the first column of tax_tree.

tax_tree

A data frame with taxonomic hierarchy. First column is species names, subsequent columns are taxonomic ranks.

n_iter

Number of stochastic iterations to run (default: 101). Must be >= 101.

seed

Optional random seed for reproducibility (default: NULL).

Value

A named list with components:

uTO_plus_max

Maximum unweighted taxonomic distance across iterations

TO_plus_max

Maximum weighted taxonomic distance across iterations

uTO_max

Maximum unweighted taxonomic diversity across iterations

TO_max

Maximum weighted taxonomic diversity across iterations

uTO_plus_det

Deterministic uTO+ (first iteration, same as ozkan_pto())

TO_plus_det

Deterministic TO+ (first iteration)

uTO_det

Deterministic uTO (first iteration)

TO_det

Deterministic TO (first iteration)

n_iter

Number of iterations performed

species_status

Named logical vector from jackknife (TRUE = happy)

jackknife

Full jackknife result from ozkan_pto_jackknife()

n_positive

Number of iterations with positive uTO+

iteration_results

Data frame with all iteration results

Details

The algorithm follows the Excel macro's Islem 1 + Islem 2 logic:

  1. Run jackknife (ozkan_pto_jackknife()) to classify each species as happy or unhappy.

  2. Iteration 1: Use the original community (deterministic).

  3. Iterations 2..n_iter: For each species:

    • Unhappy species (AA = 0): always included with original abundance.

    • Happy species (AA > 0): randomly included (50\ or excluded. Uses RANDBETWEEN(0,1) * abundance.

  4. Return the maximum of each component across all iterations.

References

Ozkan, K. (2018). A new proposed measure for estimating taxonomic diversity. Turkish Journal of Forestry, 19(4), 336-346. DOI: 10.18182/tjf.441061

See also

ozkan_pto_jackknife() for the jackknife step, ozkan_pto_sensitivity() for Run 3, ozkan_pto_full() for the full pipeline.

Examples

comm <- c(sp1 = 4, sp2 = 2, sp3 = 3, sp4 = 1, sp5 = 2)
tax <- data.frame(
  Species = paste0("sp", 1:5),
  Genus   = c("G1", "G1", "G1", "G2", "G2"),
  Family  = c("F1", "F1", "F1", "F1", "F1"),
  stringsAsFactors = FALSE
)
set.seed(42)
result <- ozkan_pto_resample(comm, tax, n_iter = 101)
result$species_status  # Happy/unhappy classification
#>  sp1  sp2  sp3  sp4  sp5 
#> TRUE TRUE TRUE TRUE TRUE