do_optim {FielDHub}

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

Description

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.

Author(s)

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

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

Examples

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