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Generates an Spatial Un-replicated Optimized Arrangement Design

Source: R/fct_optimized_arrangement.R
optimized_arrangement.Rd

Randomly generates a spatial un-replicated optimized arrangement design, where the distance between checks is maximized in such a way that each row and column have control plots. Note that design generation needs the dimension of the field (number of rows and columns).

Usage

optimized_arrangement(
  nrows = NULL,
  ncols = NULL,
  lines = NULL,
  amountChecks = NULL,
  checks = NULL,
  planter = "serpentine",
  l = 1,
  plotNumber = 101,
  seed = NULL,
  exptName = NULL,
  locationNames = NULL,
  optim = TRUE,
  data = NULL
)

Arguments

nrows

Number of rows in the field.

ncols

Number of columns in the field.

lines

Number of genotypes, experimental lines or treatments.

amountChecks

Integer with the amount total of checks or a numeric vector with the replicates of each check label.

checks

Number of genotypes as checks.

planter

Option for serpentine or cartesian arrangement. 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.

optim

By default optim = TRUE.

data

(optional) Data frame with 3 columns: ENTRY | NAME | REPS.

Value

A list with five 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.

  • dataEntry is a data frame with the data input.

  • genEntries is a list with the entries for replicated and no replicated part.

  • fieldBook is a data frame with field book design. This includes the index (Row, Column).

References

Clarke, G. P. Y., & Stefanova, K. T. (2011). Optimal design for early-generation plant breeding trials with unreplicated or partially replicated test lines. Australian & New Zealand Journal of Statistics, 53(4), 461–480.

Author

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

Examples

# Example 1: Generates a spatial unreplicated optimized arrangement design in one location
# with 120 genotypes + 20 check plots (4 checks) for a field with dimension 14 rows x 10 cols.
if (FALSE) { # \dontrun{
optim_unrep1 <- optimized_arrangement(
  nrows = 14, 
  ncols = 10, 
  lines = 120, 
  amountChecks = 20, 
  checks = 1:4,
  planter = "cartesian", 
  plotNumber = 101,
  exptName = "20RW1",
  locationNames = "CASSELTON",
  seed = 14124
)
optim_unrep1$infoDesign
optim_unrep1$layoutRandom
optim_unrep1$plotNumber
head(optim_unrep1$fieldBook, 12)
} # }
                  
# Example 2: Generates a spatial unreplicated optimized arrangement design in one location
# with 200 genotypes + 20 check plots (4 checks) for a field with dimension 10 rows x 22 cols.
# As example, we set up the data option with the entries list.
if (FALSE) { # \dontrun{
checks <- 4
list_checks <- paste("CH", 1:checks, sep = "")
treatments <- paste("G", 5:204, sep = "")
REPS <- c(5, 5, 5, 5, rep(1, 200))
treatment_list <- data.frame(list(ENTRY = 1:204, NAME = c(list_checks, treatments), REPS = REPS))
head(treatment_list, 12) 
tail(treatment_list, 12)
optim_unrep2 <- optimized_arrangement(
  nrows = 10, 
  ncols = 22, 
  planter = "serpentine", 
  plotNumber = 101,
  seed = 120,
  exptName = "20YWA2",
  locationNames = "MINOT",
  data = treatment_list
)
optim_unrep2$infoDesign
optim_unrep2$layoutRandom
optim_unrep2$plotNumber
head(optim_unrep2$fieldBook,12)
} # }