FielDHub (version 1.4.2)

do_optim: 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
)

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.

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.

Author

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
)