diagonal_arrangement {FielDHub}

Spatial Un-replicated Diagonal Arrangement Design

Description

Randomly generates an spatial un-replicated diagonal arrangement design.

Usage

diagonal_arrangement(
  nrows = NULL,
  ncols = NULL,
  lines = NULL,
  checks = NULL,
  planter = "serpentine",
  l = 1,
  plotNumber = 101,
  kindExpt = "SUDC",
  splitBy = "row",
  seed = NULL,
  blocks = NULL,
  exptName = NULL,
  locationNames = NULL,
  multiLocationData = FALSE,
  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.

checks

Number of genotypes checks.

planter

Option for serpentine or cartesian plot arrangement. By default planter = 'serpentine'.

l

Number of locations or sites. By default l = 1.

plotNumber

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

kindExpt

Type of diagonal design, with single options: Single Un-replicated Diagonal Checks 'SUDC' and Decision Blocks Un-replicated Design with Diagonal Checks 'DBUDC' for multiple experiments. By default kindExpt = 'SUDC'.

splitBy

Option to split the field when kindExpt = 'DBUDC' is selected. By default splitBy = 'row'.

seed

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

blocks

Number of experiments or blocks to generate an DBUDC design. If kindExpt = 'DBUDC' and data is null, blocks are mandatory.

exptName

(optional) Name of the experiment.

locationNames

(optional) Names each location.

multiLocationData

(optional) Option to pass an entry list for multiple locations. By default multiLocationData = FALSE.

data

(optional) Data frame with 2 columns: ENTRY | NAME .

Value

A list with five elements.

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

• layoutRandom is a matrix with the randomization layout.

• plotsNumber is a matrix with the layout plot number.

• data_entry is a data frame with the data input.

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

Author(s)

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

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.

Examples

# Example 1: Generates a spatial single diagonal arrangement design in one location
# with 270 treatments and 30 check plots for a field with dimensions 15 rows x 20 cols
# in a serpentine arrangement.
spatd <- diagonal_arrangement(
  nrows = 15, 
  ncols = 20, 
  lines = 270, 
  checks = 4, 
  plotNumber = 101, 
  kindExpt = "SUDC", 
  planter = "serpentine", 
  seed = 1987,
  exptName = "20WRY1", 
  locationNames = "MINOT"
)
spatd$infoDesign
spatd$layoutRandom
spatd$plotsNumber
head(spatd$fieldBook, 12)

# Example 2: Generates a spatial decision block diagonal arrangement design in one location
# with 720 treatments allocated in 5 experiments or blocks for a field with dimensions
# 30 rows x 26 cols in a serpentine arrangement. In this case, we show how to set up the data 
# option with the entries list.
checks <- 5;expts <- 5
list_checks <- paste("CH", 1:checks, sep = "")
treatments <- paste("G", 6:725, sep = "")
treatment_list <- data.frame(list(ENTRY = 1:725, NAME = c(list_checks, treatments)))
head(treatment_list, 12) 
tail(treatment_list, 12)
spatDB <- diagonal_arrangement(
  nrows = 30, 
  ncols = 26,
  checks = 5, 
  plotNumber = 1, 
  kindExpt = "DBUDC", 
  planter = "serpentine", 
  splitBy = "row", 
  blocks = c(150,155,95,200,120),
  data = treatment_list
)
spatDB$infoDesign
spatDB$layoutRandom
spatDB$plotsNumber
head(spatDB$fieldBook,12)

# Example 3: Generates a spatial decision block diagonal arrangement design in one location
# with 270 treatments allocated in 3 experiments or blocks for a field with dimensions
# 20 rows x 15 cols in a serpentine arrangement. Which in turn is an augmented block (3 blocks).
spatAB <- diagonal_arrangement(
  nrows = 20, 
  ncols = 15, 
  lines = 270, 
  checks = 4, 
  plotNumber = c(1,1001,2001), 
  kindExpt = "DBUDC", 
  planter = "serpentine",
  exptName = c("20WRA", "20WRB", "20WRC"), 
  blocks = c(90, 90, 90),
  splitBy = "column"
)
spatAB$infoDesign
spatAB$layoutRandom
spatAB$plotsNumber
head(spatAB$fieldBook,12)