FielDHub (version 1.4.2)

sparse_allocation: 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
)

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.

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 l = 1.

planter

Option for serpentine or cartesian plot arrangement. By default planter = 'serpentine'.

plotNumber

Numeric vector with the starting plot number for each location. By default plotNumber = 101.

copies_per_entry

Number of copies per plant. When design is sparse then copies_per_entry < l

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 do_optim() function.

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 <- sparse_allocation(
  lines = 120, 
  l = 4, 
  copies_per_entry = 3, 
  checks = 4, 
  locationNames = c("LOC1", "LOC2", "LOC3", "LOC4", "LOC5"), 
  seed = 1234
)