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Generate the sparse or p-rep allocation to multiple locations.

Source: R/fct_do_optim.R
do_optim.Rd

Generate the sparse or p-rep allocation to multiple locations.

Usage

do_optim(
  design = "sparse",
  lines,
  l,
  copies_per_entry,
  add_checks = FALSE,
  checks = NULL,
  rep_checks = NULL,
  force_balance = TRUE,
  seed,
  data = NULL
)

Arguments

design

Type of experimental design. It can be prep or sparse

lines

Number of genotypes, experimental lines or treatments.

l

Number of locations or sites. By default l = 1.

copies_per_entry

Number of copies per plant. When design is sparse then copies_per_entry should be less than l

add_checks

Option to add checks. Optional if design = "prep"

checks

Number of genotypes checks.

rep_checks

Replication for each check.

force_balance

Get balanced unbalanced locations. By default force_balance = TRUE.

seed

(optional) Real number that specifies the starting seed to obtain reproducible designs.

data

(optional) Data frame with 2 columns: ENTRY | NAME . ENTRY must be numeric.

Value

A list with five elements, classed c("fieldhub_sparse_optimization", "Sparse") or c("fieldhub_multi_prep_optimization", "MultiPrep"): the legacy Sparse/MultiPrep class is retained for compatibility, with an additional fieldhub_* class alongside it.

  • multi_location_data is a data frame with the entries of every location: LOCATION | ENTRY | NAME, with a REPS column for p-rep allocations.

  • list_locs is a list with each location list of entries.

  • allocation is a data frame of test-entry copy counts, with one column per location.

  • size_locations is a named vector of test-entry copies per location, excluding checks.

  • metadata records the allocation type, schema version, seed, random-number settings, package version and evaluated input parameters. With the same package versions and RNG settings, rebuild the allocation with do.call(do_optim, x$metadata$parameters).

References

Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0

Author

Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb]

Examples

sparse_example <- do_optim(
   design = "sparse",
   lines = 120,
   l = 4,
   copies_per_entry = 3,
   add_checks = TRUE,
   checks = 4,
   seed = 15
)