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Generates a Spatial Partially Replicated Arrangement Design

Source: R/fct_partially_replicated.R
partially_replicated.Rd

Randomly generates a spatial partially replicated (p-rep) design for single or multiple locations.

Usage

partially_replicated(
  nrows = NULL,
  ncols = NULL,
  repGens = NULL,
  repUnits = NULL,
  planter = "serpentine",
  l = 1,
  plotNumber = 101,
  seed = NULL,
  exptName = NULL,
  locationNames = NULL,
  multiLocationData = FALSE,
  dist_method = "euclidean",
  border_penalization = 0.5,
  data = NULL
)

Arguments

nrows

Numeric vector with the number of rows field at each location.

ncols

Numeric vector with the number of columns field at each location.

repGens

Numeric vector with the amount genotypes to replicate.

repUnits

Numeric vector with the number of reps of each genotype.

planter

Option for serpentine or cartesian movement. By default planter = 'serpentine'.

l

Number of locations. By default l = 1.

plotNumber

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

seed

(optional) Real number that specifies the starting seed to obtain reproducible designs.

exptName

(optional) Name of the experiment.

locationNames

(optional) Name for each location.

multiLocationData

(optional) Option to pass an entry list for multiple locations.

dist_method

The method used for distance calculation. Options are "euclidean" (default) and "manhattan". By default multiLocationData = FALSE.

border_penalization

A tuning parameter for the centrality penalty. Default is 0.5.

data

(optional) Data frame with 3 columns: ENTRY | NAME | REPS. If multiLocationData = TRUE then the data must have 4 columns: LOCATION | ENTRY | NAME | REPS

Value

A list 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.

Details

This function generates and optimizes a partially replicated (p-rep) experimental design for a given set of treatments and replication levels. The design is represented by a matrix and optimized using a pairwise distance metric. The function outputs various information about the optimized design including the field layout, replicated and unreplicated treatments, and pairwise distances between treatments. Note that the design generation needs the dimension of the field (number of rows and columns).

References

Cullis, S., B. R., & Coombes, N. E. (2006). On the design of early generation variety trials with correlated data. Journal of Agricultural, Biological, and Environmental Statistics, 11, 381–393. https://doi.org/10.1198/108571106X154443

Author

Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Jean-Marc Montpetit [ctb], Richard Horsley [ctb]

Examples

# Example 1: Generates a spatial optimized partially replicated arrangement design in one 
# location with 335 genotypes for a field with dimensions 15 rows x 28 cols. 
# Note that there are 250 genotypes unreplicated (only one time), 85 genotypes replicated 
# two times, and three checks 8 times each.
if (FALSE) { # \dontrun{
prep_deseign1 <- partially_replicated(
 nrows = 12, 
 ncols = 37,  
 repGens = c(250, 85, 3),
 repUnits = c(1, 2, 8),
 planter = "cartesian", 
 plotNumber = 101,
 seed = 77
)
prep_deseign1$infoDesign
prep_deseign1$layoutRandom
prep_deseign1$plotNumber
head(prep_deseign1$fieldBook, 12)
} # }

# Example 2: Generates a spatial optimized partially replicated arrangement design with 492 
# genotypes in a field with dimensions 30 rows x 20 cols. Note that there 384 genotypes 
# unreplicated (only one time), 108 genotypes replicated two times. 
# In this case we don't have check plots.
# As example, we set up the data option with the entries list.
if (FALSE) { # \dontrun{
NAME <- paste("G", 1:492, sep = "")
repGens = c(108, 384);repUnits = c(2,1)
REPS <- rep(repUnits, repGens)
treatment_list <- data.frame(list(ENTRY = 1:492, NAME = NAME, REPS = REPS))
head(treatment_list, 12) 
tail(treatment_list, 12)
prep_deseign2 <- partially_replicated(
  nrows = 30, 
  ncols = 20, 
  planter = "serpentine", 
  plotNumber = 101,
  seed = 41,
  data = treatment_list
)
prep_deseign2$infoDesign
prep_deseign2$layoutRandom
prep_deseign2$plotNumber
head(prep_deseign2$fieldBook, 10)
} # }