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
)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
Value
A list with four elements.
infoDesignis a list with information on the design parameters.resolvableBlocksa list with the resolvable row columns blocks.concurrenceis the concurrence matrix.fieldBookis a data frame with the row-column field book.
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
Author
Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Richard Horsley [ctb]
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
#> $rows
#> [1] 6
#>
#> $columns
#> [1] 4
#>
#> $reps
#> [1] 2
#>
#> $treatments
#> [1] 24
#>
#> $locations
#> [1] 1
#>
#> $location_names
#> [1] "Loc1"
#>
#> $seed
#> [1] 21
#>
#> $id_design
#> [1] 9
#>
rowcold1$resolvableBlocks
#> $Loc_Loc1
#> $Loc_Loc1$rep1
#> [,1] [,2] [,3] [,4]
#> [1,] NA NA NA NA
#> [2,] NA NA NA NA
#> [3,] NA NA NA NA
#> [4,] NA NA NA NA
#> [5,] NA NA NA NA
#> [6,] NA NA NA NA
#>
#> $Loc_Loc1$rep2
#> [,1] [,2] [,3] [,4]
#> [1,] NA NA NA NA
#> [2,] NA NA NA NA
#> [3,] NA NA NA NA
#> [4,] NA NA NA NA
#> [5,] NA NA NA NA
#> [6,] NA NA NA NA
#>
#>
head(rowcold1$fieldBook,12)
#> ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT
#> 1 1 Loc1 101 1 1 1 13 G-13
#> 7 2 Loc1 102 1 1 2 23 G-23
#> 13 3 Loc1 103 1 1 3 10 G-10
#> 19 4 Loc1 104 1 1 4 12 G-12
#> 2 5 Loc1 105 1 2 1 20 G-20
#> 8 6 Loc1 106 1 2 2 8 G-8
#> 14 7 Loc1 107 1 2 3 6 G-6
#> 20 8 Loc1 108 1 2 4 19 G-19
#> 3 9 Loc1 109 1 3 1 24 G-24
#> 9 10 Loc1 110 1 3 2 11 G-11
#> 15 11 Loc1 111 1 3 3 21 G-21
#> 21 12 Loc1 112 1 3 4 5 G-5
# Example 2: Generates a row-column design with 2 full blocks and 30 treatments
# and 5 rows, for one location. This example uses 100 iterations for the optimization
# but 1000 is the default and recommended value.
# In this case, we show how to use the option data.
treatments <- paste("ND-", 1:30, sep = "")
ENTRY <- 1:30
treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments))
head(treatment_list)
#> ENTRY TREATMENT
#> 1 1 ND-1
#> 2 2 ND-2
#> 3 3 ND-3
#> 4 4 ND-4
#> 5 5 ND-5
#> 6 6 ND-6
rowcold2 <- row_column(
t = 30,
nrows = 5,
r = 2,
l = 1,
plotNumber= 1001,
locationNames = "A",
seed = 15,
iterations = 100,
data = treatment_list
)
rowcold2$infoDesign
#> $rows
#> [1] 5
#>
#> $columns
#> [1] 6
#>
#> $reps
#> [1] 2
#>
#> $treatments
#> [1] 30
#>
#> $locations
#> [1] 1
#>
#> $location_names
#> [1] "A"
#>
#> $seed
#> [1] 15
#>
#> $id_design
#> [1] 9
#>
rowcold2$resolvableBlocks
#> $Loc_A
#> $Loc_A$rep1
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] NA NA NA NA NA NA
#> [2,] NA NA NA NA NA NA
#> [3,] NA NA NA NA NA NA
#> [4,] NA NA NA NA NA NA
#> [5,] NA NA NA NA NA NA
#>
#> $Loc_A$rep2
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] NA NA NA NA NA NA
#> [2,] NA NA NA NA NA NA
#> [3,] NA NA NA NA NA NA
#> [4,] NA NA NA NA NA NA
#> [5,] NA NA NA NA NA NA
#>
#>
head(rowcold2$fieldBook,12)
#> ID LOCATION PLOT REP ROW COLUMN ENTRY TREATMENT
#> 1 1 A 1001 1 1 1 5 ND-5
#> 6 2 A 1002 1 1 2 7 ND-7
#> 11 3 A 1003 1 1 3 14 ND-14
#> 16 4 A 1004 1 1 4 23 ND-23
#> 21 5 A 1005 1 1 5 9 ND-9
#> 26 6 A 1006 1 1 6 15 ND-15
#> 2 7 A 1007 1 2 1 10 ND-10
#> 7 8 A 1008 1 2 2 17 ND-17
#> 12 9 A 1009 1 2 3 13 ND-13
#> 17 10 A 1010 1 2 4 6 ND-6
#> 22 11 A 1011 1 2 5 29 ND-29
#> 27 12 A 1012 1 2 6 20 ND-20