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 three elements.

list_locs is a list with each location list of entries.
allocation is a matrix with the allocation of treatments.
size_locations is a data frame with one column for each location and one row with the size of the location.

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
)