do_optim | R Documentation |
Generate the sparse or p-rep allocation to multiple locations.
do_optim(
design = "sparse",
lines,
l,
copies_per_entry,
add_checks = FALSE,
checks = NULL,
rep_checks = NULL,
force_balance = TRUE,
seed,
data = NULL
)
design |
Type of experimental design. It can be |
lines |
Number of genotypes, experimental lines or treatments. |
l |
Number of locations or sites. By default |
copies_per_entry |
Number of copies per plant.
When design is |
add_checks |
Option to add checks. Optional if |
checks |
Number of genotypes checks. |
rep_checks |
Replication for each check. |
force_balance |
Get balanced unbalanced locations. By default |
seed |
(optional) Real number that specifies the starting seed to obtain reproducible designs. |
data |
(optional) Data frame with 2 columns: |
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.
Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb]
Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0
sparse_example <- do_optim(
design = "sparse",
lines = 120,
l = 4,
copies_per_entry = 3,
add_checks = TRUE,
checks = 4,
seed = 15
)
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