FielDHub (version 1.4.2)

row_column: Generates a Resolvable Row-Column Design (RowColD)

Description

It randomly generates a resolvable row-column design (RowColD). The design is optimized in both rows and columns blocking factors. The randomization can be done across multiple locations.

Usage

row_column(
  t = NULL,
  nrows = NULL,
  r = NULL,
  l = 1,
  plotNumber = 101,
  locationNames = NULL,
  seed = NULL,
  iterations = 1000,
  data = NULL
)

Value

A list with four elements.

• infoDesign is a list with information on the design parameters.

• resolvableBlocks a list with the resolvable row columns blocks.

• concurrence is the concurrence matrix.

• fieldBook is a data frame with the row-column field book.

Arguments

t

Number of treatments.

nrows

Number of rows of a full resolvable replicate.

r

Number of blocks (full resolvable replicates).

l

Number of locations. By default l = 1.

plotNumber

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

locationNames

(optional) Names for each location.

seed

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

iterations

Number of iterations for design optimization. By default iterations = 1000.

data

(optional) Data frame with label list of treatments

Author

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

Details

The Row-Column design in FielDHub is built in two stages. The first step constructs the blocking factor Columns using Incomplete Block Units from an incomplete block design that sets the number of incomplete blocks as the number of Columns in the design, each of which has a dimension equal to the number of Rows. Once this design is generated, the Rows are used as the Row blocking factor that is optimized for A-Efficiency, but levels within the original Columns are fixed. To optimize the Rows while maintaining the current optimized Columns, we use a heuristic algorithm that swaps at random treatment positions within a given Column (Block) also selected at random. The algorithm begins by calculating the A-Efficiency on the initial design, performs a swap iteration, recalculates the A-Efficiency on the resulting design, and compares it with the previous one to decide whether to keep or discard the new design. This iterative process is repeated, by default, 1,000 times.

References

Edmondson., R. N. (2021). blocksdesign: Nested and crossed block designs for factorial and unstructured treatment sets. https://CRAN.R-project.org/package=blocksdesign

Examples

# Example 1: Generates a row-column design with 2 full blocks and 24 treatments
# and 6 rows. This for one location. This example uses 100 iterations for the optimization
# but 1000 is the default and recomended value.
rowcold1 <- row_column(
  t = 24, 
  nrows = 6, 
  r = 2, 
  l = 1, 
  plotNumber= 101, 
  locationNames = "Loc1",
  iterations = 100,
  seed = 21
)
rowcold1$infoDesign
rowcold1$resolvableBlocks
head(rowcold1$fieldBook,12)

# Example 2: Generates a row-column design with 2 full blocks and 30 treatments
# and 5 rows, for one location. This example uses 100 iterations for the optimization
# but 1000 is the default and recommended value.
# In this case, we show how to use the option data.
treatments <- paste("ND-", 1:30, sep = "")
ENTRY <- 1:30
treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments))
head(treatment_list)
rowcold2 <- row_column(
  t = 30, 
  nrows = 5, 
  r = 2, 
  l = 1, 
  plotNumber= 1001, 
  locationNames = "A",
  seed = 15,
  iterations = 100,
  data = treatment_list
)
rowcold2$infoDesign
rowcold2$resolvableBlocks
head(rowcold2$fieldBook,12)