Optimized multi-location partially replicated design

Usage

multi_location_prep(
  lines,
  nrows,
  ncols,
  l,
  planter = "serpentine",
  plotNumber,
  desired_avg,
  copies_per_entry,
  checks = NULL,
  rep_checks = NULL,
  exptName,
  locationNames,
  optim_list,
  seed,
  data = NULL
)

Arguments

lines: Number of genotypes, experimental lines or treatments.
nrows: Numeric vector with the number of rows field at each location.
ncols: Numeric vector with the number of columns field at each location.
l: Number of locations. By default l = 1.
planter: Option for serpentine or cartesian movement. By default planter = 'serpentine'.
plotNumber: Numeric vector with the starting plot number for each location. By default plotNumber = 101.
desired_avg: (optional) Desired average of treatments across locations.
copies_per_entry: Number of total copies per treatment.
checks: Number of checks.
rep_checks: Number of replications per check.
exptName: (optional) Name of the experiment.
locationNames: (optional) Name for each location.
optim_list: (optional) A list object of class "MultiPrep"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.

Value

A list of class FielDHub with several elements.

infoDesign is a list with information on the design parameters.
layoutRandom is a matrix with the randomization layout.
plotNumber is a matrix with the layout plot number.
binaryField is a matrix with the binary field.
dataEntry is a data frame with the data input.
genEntries is a list with the entries for replicated and non-replicated parts.
fieldBook is a data frame with field book design. This includes the index (Row, Column).
min_pairwise_distance is a data frame with the minimum pairwise distance between each pair of locations.
reps_info is a data frame with information on the number of replicated and non-replicated treatments at each location.
pairsDistance is a data frame with the pairwise distances between each pair of treatments.
treatments_with_reps is a list with the entries for the replicated part of the design.
treatments_with_no_reps is a list with the entries for the non-replicated part of the design.
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.

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

Author

Didier Murillo [aut], Salvador Gezan [aut], Jean-Marc Montpetit [ctb], Ana Heilman [ctb]

Examples

# Example 1: Generates a spatially optimized multi-location p-rep design with 142 
# genotypes. The number of copies per plant available for this experiment is 9. 
# This experiment is carried out in 5 locations, and there are seven seeds available 
# for each plant to make replications.
# In this case, we add three controls (checks) with six reps each.
# With this setup, the experiment will have 142 treatments + 3 checks = 145 
# entries and the number of plots per location after the allocation process 
# will be 196. 
# The average genotype allocation will be 1.5 copies per location.
if (FALSE) {
optim_multi_prep <- multi_location_prep(
  lines = 150,  
  l = 5, 
  copies_per_entry = 7, 
  checks = 3, 
  rep_checks = c(6,6,6),
  locationNames = c("LOC1", "LOC2", "LOC3", "LOC4", "LOC5"), 
  seed = 1234
)
designs <- optim_multi_prep$designs
field_book_loc_1 <- designs$LOC1$fieldBook
head(field_book_loc_1, 10)
}