multi_location_prep | R Documentation |
Optimized multi-location partially replicated design
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
)
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 |
planter |
Option for |
plotNumber |
Numeric vector with the starting plot number for each location. By default |
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 |
seed |
(optional) Real number that specifies the starting seed to obtain reproducible designs. |
data |
(optional) Data frame with 2 columns: |
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.
Didier Murillo [aut], Salvador Gezan [aut], Jean-Marc Montpetit [ctb], Ana Heilman [ctb]
Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0
# 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)
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