Monte Carlo assessment of an assignment reference baseline

Repeatedly remove known reference individuals, treat them as an artificial mixture of unknown origin, and assign them against the remaining reference baseline. This evaluates assignment performance without allowing test individuals to contribute to reference allele frequencies.

Usage

assess_assignment_mc(
  data,
  strata = NULL,
  repetitions = 50L,
  mixture.size = 100L,
  min.remaining = 5L,
  random.seed = NULL,
  ...,
  verbose = TRUE
)

Arguments

data

Genomic data accepted by [assign_individuals()].

strata

Optional strata metadata accepted by [genometranslator::read_strata()]. Default: strata = NULL.

repetitions

Number of Monte Carlo replicates. Default: repetitions = 50.

mixture.size

Total number of reference individuals held out in every replicate. Default: mixture.size = 100.

min.remaining

Minimum individuals retained in every source population. Default: min.remaining = 5.

random.seed

Optional simulation seed. Default: random.seed = NULL.

...

Additional arguments passed to [assign_individuals()].

verbose

Logical. Display progress messages. Default: verbose = TRUE.

Value

A list with replicate-level assignments, accuracy summaries, and the identities held out in every replicate.

Details

Holdout numbers are allocated approximately in proportion to the reference population sizes, while retaining at least `min.remaining` individuals in every population. Marker selection and other data-dependent preprocessing should be performed independently within each training baseline when they form part of the inferential workflow.

References

Moran BM, Anderson EC (2019). Bayesian inference from the conditional genetic stock identification model. Canadian Journal of Fisheries and Aquatic Sciences, 76(4), 551-560. doi:10.1139/cjfas-2018-0016 .