sparse_allocation {FielDHub} | R Documentation |
Unreplicated designs using the sparse allocation approach
Description
Unreplicated designs using the sparse allocation approach
Usage
sparse_allocation(
lines,
nrows,
ncols,
l,
planter = "serpentine",
plotNumber,
copies_per_entry,
checks = NULL,
exptName = NULL,
locationNames,
sparse_list,
seed,
data = NULL
)
Arguments
lines |
Number of genotypes, experimental lines or treatments. |
nrows |
Number of rows in the field. |
ncols |
Number of columns in the field. |
l |
Number of locations or sites. By default |
planter |
Option for |
plotNumber |
Numeric vector with the starting plot number for each location.
By default |
copies_per_entry |
Number of copies per plant.
When design is |
checks |
Number of genotypes checks. |
exptName |
(optional) Name of the experiment. |
locationNames |
(optional) Names each location. |
sparse_list |
(optional) A class "Sparse" object generated by |
seed |
(optional) Real number that specifies the starting seed to obtain reproducible designs. |
data |
(optional) Data frame with 2 columns: |
Value
A list with four elements.
-
designs
is a list with each location unreplicated randomization. -
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 <- sparse_allocation(
lines = 120,
l = 4,
copies_per_entry = 3,
checks = 4,
locationNames = c("LOC1", "LOC2", "LOC3", "LOC4", "LOC5"),
seed = 1234
)