# 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) {
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) {
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)
}
```