multi_location_prep {FielDHub}R Documentation

Optimized multi-location partially replicated design

Description

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.

Author(s)

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

# 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.
## Not run: 
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)

## End(Not run)

[Package FielDHub version 1.4.2 Index]