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Optimized multi-location partially replicated design

Source: R/fct_multi_location_prep.R
multi_location_prep.Rd

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,
  spread_reps = TRUE,
  data = NULL,
  allow_fillers = FALSE,
  max_fillers = NULL,
  year = 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.

spread_reps

A logical value indicating whether to maximize the spatial distance between replicated treatments in the field. Default is TRUE.

data

(optional) Data frame with 2 columns: ENTRY | NAME . ENTRY must be numeric.

allow_fillers

A logical value indicating whether field dimensions may exceed the number of experimental plots. Fillers occupy the terminal cells of the selected planter path. Default is FALSE.

max_fillers

Maximum number of filler plots considered when dimensions are selected automatically. When NULL, up to 10 filler plots are considered.

year

(optional) Year recorded in the YEAR column of the field book. By default the current year.

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.

  • fillerField is a logical matrix identifying filler plots.

  • 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.

  • multi_location_data is a data frame with the entries of every location: LOCATION | ENTRY | NAME | REPS.

  • allocation is a matrix with the allocation of treatments.

  • size_locations is a named vector with the size of each location.

Reproducibility

The result records effective inputs and the resolved seed in metadata$parameters. Under the same package versions and RNG settings, rebuild a result x with do.call(multi_location_prep, x$metadata$parameters).

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) { # \dontrun{
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
)
field_book <- optim_multi_prep$fieldBook
head(subset(field_book, LOCATION == "LOC1"), 10)
} # }