Package 'FielDHub'

Title: A Shiny App for Design of Experiments in Life Sciences
Description: A shiny design of experiments (DOE) app that aids in the creation of traditional, un-replicated, augmented and partially-replicated designs applied to agriculture, plant breeding, forestry, animal and biological sciences.
Authors: Didier Murillo [cre, aut], Salvador Gezan [aut], Ana Heilman [ctb], Thomas Walk [ctb], Johan Aparicio [ctb], Matthew Seefeldt [ctb], Jean-Marc Montpetit [ctb], Richard Horsley [ctb], North Dakota State University [cph]
Maintainer: Didier Murillo <[email protected]>
License: MIT + file LICENSE
Version: 1.5.0
Built: 2026-05-28 06:57:35 UTC
Source: https://github.com/didiermurillof/fieldhub

Help Index

Generates an Alpha Design

Description

Randomly generates an alpha design like alpha(0,1) across multiple locations.

Usage

alpha_lattice(
  t = NULL,
  k = NULL,
  r = NULL,
  l = 1,
  plotNumber = 101,
  locationNames = NULL,
  seed = NULL,
  data = NULL
)

Arguments

t

Number of treatments.
k

Size of incomplete blocks (number of units per incomplete block).
r

Number of full blocks (or resolvable replicates) (also number of replicates per treatment).
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) String with names for each of the l locations.
seed

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

(optional) Data frame with label list of treatments.

Value

A list with two elements.

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

  • fieldBook is a data frame with the alpha design field book.

Author(s)

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

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 an alpha design with 4 full blocks and 15 treatments.
# Size of IBlocks k = 3.
alphalattice1 <- alpha_lattice(t = 15, 
                               k = 3, 
                               r = 4, 
                               l = 1, 
                               plotNumber = 101, 
                               locationNames = "GreenHouse", 
                               seed = 1247)
alphalattice1$infoDesign
head(alphalattice1$fieldBook, 10)

# Example 2: Generates an alpha design with 3 full blocks and 25 treatment.
# Size of IBlocks k = 5. 
# In this case, we show how to use the option data.
treatments <- paste("G-", 1:25, sep = "")
ENTRY <- 1:25
treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments))
head(treatment_list) 
alphalattice2 <- alpha_lattice(t = 25,
                               k = 5,
                               r = 3, 
                               l = 1, 
                               plotNumber = 1001, 
                               locationNames = "A", 
                               seed = 1945,
                               data = treatment_list)
alphalattice2$infoDesign
head(alphalattice2$fieldBook, 10)

Generates a Completely Randomized Design (CRD)

Description

It randomly generates a completely randomized design.

Usage

CRD(
  t = NULL,
  reps = NULL,
  plotNumber = 101,
  locationName = NULL,
  seed = NULL,
  data = NULL
)

Arguments

t

An integer number with total number of treatments or a vector of dimension t with labels.
reps

Number of replicates of each treatment.
plotNumber

Starting plot number. By default plotNumber = 101.
locationName

(optional) Name of the location.
seed

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

(optional) Data frame with the 2 columns with labels of each treatments and its number of replicates.

Value

A list with two elements.

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

  • fieldBook is a data frame with the CRD field book.

Author(s)

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

References

Federer, W. T. (1955). Experimental Design. Theory and Application. New York, USA. The Macmillan Company.

Examples

# Example 1: Generates a CRD design with 10 treatments and 5 reps each.
crd1 <- CRD(
  t = 10,
  reps = 5,
  plotNumber = 101,
  seed = 1987,
  locationName = "Fargo"
)
crd1$infoDesign
head(crd1$fieldBook, 10)

# Example 2: Generates a CRD design with 15 treatments and 6 reps each.
Gens <- paste("Wheat", 1:15, sep = "")
crd2 <- CRD(
  t = Gens,
  reps = 6,
  plotNumber = 1001,
  seed = 1654,
  locationName = "Fargo"
)
crd2$infoDesign
head(crd2$fieldBook, 10)

# Example 3: Generates a CRD design with 12 treatments and 4 reps each.
# In this case, we show how to use the option data.
treatments <- paste("ND-", 1:12, sep = "")
treatment_list <- data.frame(list(TREATMENT = treatments, REP = 4))
head(treatment_list)
crd3 <- CRD(
  t = NULL,
  reps = NULL,
  plotNumber = 2001,
  seed = 1655,
  locationName = "Cali",
  data = treatment_list
)
crd3$infoDesign
head(crd3$fieldBook, 10)

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)

Generate the sparse or p-rep allocation to multiple locations.

Description

Generate the sparse or p-rep allocation to multiple locations.

Usage

do_optim(
  design = "sparse",
  lines,
  l,
  copies_per_entry,
  add_checks = FALSE,
  checks = NULL,
  rep_checks = NULL,
  force_balance = TRUE,
  seed,
  data = NULL
)

Arguments

design

Type of experimental design. It can be prep or sparse
lines

Number of genotypes, experimental lines or treatments.
l

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

Number of copies per plant. When design is sparse then copies_per_entry should be less than l
add_checks

Option to add checks. Optional if design = "prep"
checks

Number of genotypes checks.
rep_checks

Replication for each check.
force_balance

Get balanced unbalanced locations. By default force_balance = TRUE.
seed

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

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

Value

A list with three elements.

  • list_locs is a list with each location list of entries.

  • allocation is a matrix with the allocation of treatments.

  • size_locations is a data frame with one column for each location and one row with the size of the location.

Author(s)

Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb]

References

Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0

Examples

sparse_example <- do_optim(
   design = "sparse",
   lines = 120, 
   l = 4, 
   copies_per_entry = 3, 
   add_checks = TRUE, 
   checks = 4,
   seed = 15
)

Generates a Full Factorial Design

Description

It randomly generates a full factorial design across locations.

Usage

full_factorial(
  setfactors = NULL,
  reps = NULL,
  l = 1,
  type = 2,
  plotNumber = 101,
  continuous = FALSE,
  planter = "serpentine",
  seed = NULL,
  locationNames = NULL,
  factorLabels = TRUE,
  data = NULL
)

Arguments

setfactors

Numeric vector with levels of each factor.
reps

Number of replicates (full blocks).
l

Number of locations. By default l = 1.
type

Option for CRD or RCBD designs. Values are type = 1 (CRD) or type = 2 (RCBD). By default type = 2.
plotNumber

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

Logical for plot number continuous or not. By default continuous = FALSE.
planter

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

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

(optional) Names for each location.
factorLabels

(optional) If TRUE retain the levels labels from the original data set otherwise, numeric labels will be assigned. Default is factorLabels =TRUE.
data

(optional) Data frame with the labels of factors.

Value

A list with two elements.

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

  • fieldBook is a data frame with the full factorial field book.

Author(s)

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

References

Federer, W. T. (1955). Experimental Design. Theory and Application. New York, USA. The Macmillan Company.

Examples

# Example 1: Generates a full factorial with 3 factors each with 2 levels.
# This in an RCBD arrangement with 3 reps.
fullFact1 <- full_factorial(setfactors = c(2,2,2), reps = 3, l = 1, type = 2,
                            plotNumber = 101,
                            continuous = TRUE,
                            planter = "serpentine",
                            seed = 325,
                            locationNames = "FARGO")
fullFact1$infoDesign
head(fullFact1$fieldBook,10)

# Example 2: Generates a full factorial with 3 factors and each with levels: 2,3,
# and 2, respectively. In this case, we show how to use the option data
FACTORS <- rep(c("A", "B", "C"), c(2,3,2))
LEVELS <- c("a0", "a1", "b0", "b1", "b2", "c0", "c1")
data_factorial <- data.frame(list(FACTOR = FACTORS, LEVEL = LEVELS))
print(data_factorial)
# This in an RCBD arrangement with 5 reps in 3 locations.
fullFact2 <- full_factorial(setfactors = NULL, reps = 5, l = 3, type = 2,
                            plotNumber = c(101,1001,2001),
                            continuous = FALSE,
                            planter = "serpentine",
                            seed = 326,
                            locationNames = c("Loc1","Loc2","Loc3"),
                            data = data_factorial)
fullFact2$infoDesign
head(fullFact2$fieldBook,10)

Generates a Resolvable Incomplete Block Design

Description

Randomly generates a resolvable incomplete block design (IBD) of characteristics (t, k, r). The randomization can be done across locations.

Usage

incomplete_blocks(
  t = NULL,
  k = NULL,
  r = NULL,
  l = 1,
  plotNumber = 101,
  locationNames = NULL,
  seed = NULL,
  data = NULL
)

Arguments

t

Number of treatments.
k

Size of incomplete blocks (number of units per incomplete block).
r

Number of full blocks (or resolvable replicates) (also number of replicates per treatment).
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.
data

(optional) Data frame with label list of treatments.

Value

A list with two elements.

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

  • fieldBook is a data frame with the incomplete block design field book.

Author(s)

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

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 resolvable IBD of characteristics (t,k,r) = (12,4,2).
# 1-resolvable IBDs
ibd1 <- incomplete_blocks(t = 12,
                          k = 4,
                          r = 2,
                          seed = 1984)
ibd1$infoDesign
head(ibd1$fieldBook)

# Example 2: Generates a balanced resolvable IBD of characteristics (t,k,r) = (15,3,7).
# In this case, we show how to use the option data.
treatments <- paste("TX-", 1:15, sep = "")
ENTRY <- 1:15
treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments))
head(treatment_list)
ibd2 <- incomplete_blocks(t = 15,
                          k = 3,
                          r = 7,
                          seed = 1985,
                          data = treatment_list)
ibd2$infoDesign
head(ibd2$fieldBook)

Generates a Latin Square Design

Description

Randomly generates a latin square design of up 10 treatments.

Usage

latin_square(
  t = NULL,
  reps = 1,
  plotNumber = 101,
  planter = "serpentine",
  seed = NULL,
  locationNames = NULL,
  data = NULL
)

Arguments

t

Number of treatments.
reps

Number of full resolvable squares. By default reps = 1.
plotNumber

Starting plot number. By default plotNumber = 101.
planter

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

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

(optional) Name for the location.
data

(optional) Data frame with label list of treatments.

Value

A list with information on the design parameters.

Data frame with the latin square field book.

A list with two elements.

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

  • fieldBook is a data frame with the latin square field book.

Author(s)

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

References

Federer, W. T. (1955). Experimental Design. Theory and Application. New York, USA. The Macmillan Company.

Examples

# Example 1: Generates a latin square design with 4 treatments and 2 reps.
latinSq1 <- latin_square(t = 4,
                         reps = 2,
                         plotNumber = 101,
                         planter = "cartesian",
                         seed = 1980)
print(latinSq1)
summary(latinSq1)
head(latinSq1$fieldBook)

# Example 2: Generates a latin square design with 5 treatments and 3 reps.
latin_data <- data.frame(list(ROW = paste("Period", 1:5, sep = ""),
                              COLUMN = paste("Cow", 1:5, sep = ""),
                              TREATMENT = paste("Diet", 1:5, sep = "")))
print(latin_data)
latinSq2 <- latin_square(t = NULL,
                         reps = 3,
                         plotNumber = 101,
                         planter = "cartesian",
                         seed = 1981,
                         data = latin_data)
latinSq2$squares
latinSq2$plotSquares
head(latinSq2$fieldBook)

Optimized multi-location partially replicated design

Description

Optimized multi-location partially replicated design

Usage

multi_location_prep(
  lines,
  nrows,
  ncols,
  l,
  planter = "serpentine",
  plotNumber,
  desired_avg,
  copies_per_entry,
  checks = NULL,
  rep_checks = NULL,
  exptName,
  locationNames,
  optim_list,
  seed,
  spread_reps = TRUE,
  data = NULL
)

Arguments

lines

Number of genotypes, experimental lines or treatments.
nrows

Numeric vector with the number of rows field at each location.
ncols

Numeric vector with the number of columns field at each location.
l

Number of locations. By default l = 1.
planter

Option for serpentine or cartesian movement. By default planter = 'serpentine'.
plotNumber

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

(optional) Desired average of treatments across locations.
copies_per_entry

Number of total copies per treatment.
checks

Number of checks.
rep_checks

Number of replications per check.
exptName

(optional) Name of the experiment.
locationNames

(optional) Name for each location.
optim_list

(optional) A list object of class "MultiPrep"generated by do_optim() function.
seed

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

A logical value indicating whether to maximize the spatial distance between replicated treatments in the field. Default is TRUE.
data

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

Value

A list of class FielDHub with several 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.

  • binaryField is a matrix with the binary field.

  • dataEntry is a data frame with the data input.

  • genEntries is a list with the entries for replicated and non-replicated parts.

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

  • min_pairwise_distance is a data frame with the minimum pairwise distance between each pair of locations.

  • reps_info is a data frame with information on the number of replicated and non-replicated treatments at each location.

  • pairsDistance is a data frame with the pairwise distances between each pair of treatments.

  • treatments_with_reps is a list with the entries for the replicated part of the design.

  • treatments_with_no_reps is a list with the entries for the non-replicated part of the design.

  • list_locs is a list with each location list of entries.

  • allocation is a matrix with the allocation of treatments.

  • size_locations is a data frame with one column for each location and one row with the size of the location.

Author(s)

Didier Murillo [aut], Salvador Gezan [aut], Jean-Marc Montpetit [ctb], Ana Heilman [ctb]

References

Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0

Examples

# Example 1: Generates a spatially optimized multi-location p-rep design with 142 
# genotypes. The number of copies per plant available for this experiment is 9. 
# This experiment is carried out in 5 locations, and there are seven seeds available 
# for each plant to make replications.
# In this case, we add three controls (checks) with six reps each.
# With this setup, the experiment will have 142 treatments + 3 checks = 145 
# entries and the number of plots per location after the allocation process 
# will be 196. 
# The average genotype allocation will be 1.5 copies per location.
## Not run: 
optim_multi_prep <- multi_location_prep(
  lines = 150,  
  l = 5, 
  copies_per_entry = 7, 
  checks = 3, 
  rep_checks = c(6,6,6),
  locationNames = c("LOC1", "LOC2", "LOC3", "LOC4", "LOC5"), 
  seed = 1234
)
designs <- optim_multi_prep$designs
field_book_loc_1 <- designs$LOC1$fieldBook
head(field_book_loc_1, 10)

## End(Not run)

Generates an Spatial Un-replicated Optimized Arrangement Design

Description

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,
  spread_reps = 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.
spread_reps

A logical value indicating whether to maximize the spatial distance between replicated treatments in the field. Default is 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).

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 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.
## Not run: 
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)

## End(Not run)
                  
# 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.
## Not run: 
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)

## End(Not run)

Generates a Spatial Partially Replicated Arrangement Design

Description

Randomly generates a spatial partially replicated (p-rep) design for single or multiple locations.

Usage

partially_replicated(
  nrows = NULL,
  ncols = NULL,
  repGens = NULL,
  repUnits = NULL,
  planter = "serpentine",
  l = 1,
  plotNumber = 101,
  spread_reps = TRUE,
  seed = NULL,
  exptName = NULL,
  locationNames = NULL,
  multiLocationData = FALSE,
  dist_method = "euclidean",
  border_penalization = 0.5,
  data = NULL
)

Arguments

nrows

Numeric vector with the number of rows field at each location.
ncols

Numeric vector with the number of columns field at each location.
repGens

Numeric vector with the amount genotypes to replicate.
repUnits

Numeric vector with the number of reps of each genotype.
planter

Option for serpentine or cartesian movement. 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.
spread_reps

A logical value indicating whether to maximize the spatial distance between replicated treatments in the field. Default is TRUE.
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.
multiLocationData

(optional) Option to pass an entry list for multiple locations.
dist_method

The method used for distance calculation. Options are "euclidean" (default) and "manhattan". By default multiLocationData = FALSE.
border_penalization

A tuning parameter for the centrality penalty. Default is 0.5.
data

(optional) Data frame with 3 columns: ENTRY | NAME | REPS. If multiLocationData = TRUE then the data must have 4 columns: LOCATION | ENTRY | NAME | REPS

Details

This function generates and optimizes a partially replicated (p-rep) experimental design for a given set of treatments and replication levels. The design is represented by a matrix and optimized using a pairwise distance metric. The function outputs various information about the optimized design including the field layout, replicated and unreplicated treatments, and pairwise distances between treatments. Note that the design generation needs the dimension of the field (number of rows and columns).

Value

A list with several 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.

  • binaryField is a matrix with the binary field.

  • dataEntry is a data frame with the data input.

  • genEntries is a list with the entries for replicated and non-replicated parts.

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

  • min_pairwise_distance is a data frame with the minimum pairwise distance between each pair of locations.

  • reps_info is a data frame with information on the number of replicated and non-replicated treatments at each location.

  • pairsDistance is a data frame with the pairwise distances between each pair of treatments.

  • treatments_with_reps is a list with the entries for the replicated part of the design.

  • treatments_with_no_reps is a list with the entries for the non-replicated part of the design.

Author(s)

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

References

Cullis, S., B. R., & Coombes, N. E. (2006). On the design of early generation variety trials with correlated data. Journal of Agricultural, Biological, and Environmental Statistics, 11, 381–393. https://doi.org/10.1198/108571106X154443

Examples

# Example 1: Generates a spatial optimized partially replicated arrangement design in one 
# location with 335 genotypes for a field with dimensions 15 rows x 28 cols. 
# Note that there are 250 genotypes unreplicated (only one time), 85 genotypes replicated 
# two times, and three checks 8 times each.
## Not run: 
prep_deseign1 <- partially_replicated(
 nrows = 12, 
 ncols = 37,  
 repGens = c(250, 85, 3),
 repUnits = c(1, 2, 8),
 planter = "cartesian", 
 plotNumber = 101,
 seed = 77
)
prep_deseign1$infoDesign
prep_deseign1$layoutRandom
prep_deseign1$plotNumber
head(prep_deseign1$fieldBook, 12)

## End(Not run)

# Example 2: Generates a spatial optimized partially replicated arrangement design with 492 
# genotypes in a field with dimensions 30 rows x 20 cols. Note that there 384 genotypes 
# unreplicated (only one time), 108 genotypes replicated two times. 
# In this case we don't have check plots.
# As example, we set up the data option with the entries list.
## Not run: 
NAME <- paste("G", 1:492, sep = "")
repGens = c(108, 384);repUnits = c(2,1)
REPS <- rep(repUnits, repGens)
treatment_list <- data.frame(list(ENTRY = 1:492, NAME = NAME, REPS = REPS))
head(treatment_list, 12) 
tail(treatment_list, 12)
prep_deseign2 <- partially_replicated(
  nrows = 30, 
  ncols = 20, 
  planter = "serpentine", 
  plotNumber = 101,
  seed = 41,
  data = treatment_list
)
prep_deseign2$infoDesign
prep_deseign2$layoutRandom
prep_deseign2$plotNumber
head(prep_deseign2$fieldBook, 10)

## End(Not run)

Plot a FielDHub object

Description

Draw a field layout plot for a FielDHub object.

Usage

## S3 method for class 'FielDHub'
plot(x, ...)

Arguments

x

a object inheriting from class FielDHub
...

further arguments passed to utility function plot_layout().

  • layout a integer. Options available depend on the type of design and its characteristics

  • l a integer to specify the location to plot.

  • planter it can be serpentine or cartesian.

  • stacked it can be vertical or horizontal stacked layout.

Value

  • a plot object inheriting from class fieldLayout

  • field_book a data frame with the fieldbook that includes the coordinates ROW and COLUMN.

Author(s)

Didier Murillo [aut]

Examples

## Not run: 
# Example 1: Plot a RCBD design with 24 treatments and 3 reps.
s <- RCBD(t = 24, reps = 3, plotNumber = 101, seed = 12)
plot(s)

## End(Not run)

Print a FielDHub object

Description

Prints information about any FielDHub function.

Usage

## S3 method for class 'FielDHub'
print(x, n, ...)

Arguments

x

an object inheriting from class
n

a single integer. If positive or zero, size for the resulting object: number of elements for a vector (including lists), rows for a matrix or data frame or lines for a function. If negative, all but the n last/first number of elements of x.
...

further arguments passed to head.

Value

an object inheriting from class FielDHub

Author(s)

Thiago de Paula Oliveira, [email protected] [aut], Didier Murillo [aut]

Examples

# Example 1: Generates a CRD design with 5 treatments and 5 reps each.
crd1 <- CRD(t = 5, reps = 5, plotNumber = 101,
seed = 1985, locationName = "Fargo")
crd1$infoDesign
print(crd1)

Print a fieldLayout plot object

Description

Prints a plot object of class fieldLayout.

Usage

## S3 method for class 'fieldLayout'
print(x, ...)

Arguments

x

a plot object inheriting from class fieldLayout.
...

unused, for extensibility.

Value

a plot object inheriting from class fieldLayout.

Author(s)

Didier Murillo [aut]

Print the summary of a FielDHub object

Description

Print summary information on the design parameters, and data frame structure

Usage

## S3 method for class 'summary.FielDHub'
print(x, ...)

Arguments

x

an object inheriting from class FielDHub
...

Unused, for extensibility

Value

an object inheriting from class FielDHub

Author(s)

Thiago de Paula Oliveira, [email protected] [aut], Didier Murillo [aut]

Generates a Randomized Complete Block Design (RCBD)

Description

It randomly generates a randomized complete block design (RCBD) across locations.

Usage

RCBD(
  t = NULL,
  reps = NULL,
  l = 1,
  plotNumber = 101,
  continuous = FALSE,
  planter = "serpentine",
  seed = NULL,
  locationNames = NULL,
  data = NULL
)

Arguments

t

An integer number with total number of treatments or a vector of dimension t with labels.
reps

Number of replicates (full blocks) of each treatment.
l

Number of locations. By default l = 1.
plotNumber

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

Logical value for plot number continuous or not. By default continuous = FALSE.
planter

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

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

(optional) Names for each location.
data

(optional) Data frame with the labels of treatments.

Value

A list with five elements.

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

  • layoutRandom is the RCBD layout randomization for each location.

  • plotNumber is the plot number layout for each location.

  • fieldBook is a data frame with the RCBD field book design.

Author(s)

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

References

Federer, W. T. (1955). Experimental Design. Theory and Application. New York, USA. The Macmillan Company.

Examples

# Example 1: Generates a RCBD design with 3 blocks and 20 treatments across 3 locations.
rcbd1 <- RCBD(t = LETTERS[1:20], reps = 5, l = 3, 
              plotNumber = c(101,1001, 2001), 
              continuous = TRUE,
              planter = "serpentine", 
              seed = 1020, 
              locationNames = c("FARGO", "MINOT", "CASSELTON"))
rcbd1$infoDesign                  
rcbd1$layoutRandom
rcbd1$plotNumber
head(rcbd1$fieldBook)

# Example 2: Generates a RCBD design with 6 blocks and 18 treatments in one location.
# In this case, we show how to use the option data.
treatments <- paste("ND-", 1:18, sep = "")
treatment_list <- data.frame(list(TREATMENT = treatments))
head(treatment_list)
rcbd2 <- RCBD(reps = 6, l = 1, 
              plotNumber = 101, 
              continuous = FALSE, 
              planter = "serpentine", 
              seed = 13, 
              locationNames = "IBAGUE",
              data = treatment_list)
rcbd2$infoDesign                  
rcbd2$layoutRandom
rcbd2$plotNumber
head(rcbd2$fieldBook)

Generates an Augmented Randomized Complete Block Design (ARCBD)

Description

It randomly generates an augmented randomized complete block design across locations (ARCBD).

Usage

RCBD_augmented(
  lines = NULL,
  checks = NULL,
  b = NULL,
  l = 1,
  planter = "serpentine",
  plotNumber = 101,
  repsStack = c("vertical", "horizontal"),
  exptName = NULL,
  seed = NULL,
  locationNames = NULL,
  repsExpt = 1,
  random = TRUE,
  data = NULL,
  nrows = NULL,
  ncols = NULL
)

Arguments

lines

Treatments, number of lines for test.
checks

Number of checks per augmented block.
b

Number of augmented blocks.
l

Number of locations. By default l = 1.
planter

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

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

Option for horizontal or vertical layout By default repsStack = 'vertical'.
exptName

(optional) Name of experiment.
seed

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

(optional) Name for each location.
repsExpt

(optional) Number of reps of experiment. By default repsExpt = 1.
random

Logical value to randomize treatments or not. By default random = TRUE.
data

(optional) Data frame with the labels of treatments.
nrows

(optional) Number of rows in the field.
ncols

(optional) Number of columns in the field.

Value

A list with five elements.

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

  • layoutRandom is the ARCBD layout randomization for the first location.

  • plotNumber is the plot number layout for the first location.

  • exptNames is the experiment names layout.

  • data_entry is a data frame with the data input.

  • fieldBook is a data frame with the ARCBD field book.

Author(s)

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

References

Federer, W. T. (1955). Experimental Design. Theory and Application. New York, USA. The Macmillan Company.

Examples

# Example 1: Generates an ARCBD with 6 blocks, 3 checks for each, and 50 treatments 
# in two locations.
ARCBD1 <- RCBD_augmented(lines = 50, checks = 3, b = 6, l = 2, 
                         planter = "cartesian", 
                         plotNumber = c(1,1001),
                         seed = 23, 
                         locationNames = c("FARGO", "MINOT"))
ARCBD1$infoDesign
ARCBD1$layoutRandom
ARCBD1$exptNames
ARCBD1$plotNumber
head(ARCBD1$fieldBook, 12)
                   
# Example 2: Generates an ARCBD with 17 blocks, 4 checks for each, and 350 treatments 
# in 3 locations.
# In this case, we show how to use the option data.
checks <- 4;
list_checks <- paste("CH", 1:checks, sep = "")
treatments <- paste("G", 5:354, sep = "")
treatment_list <- data.frame(list(ENTRY = 1:354, NAME = c(list_checks, treatments)))
head(treatment_list, 12)
ARCBD2 <- RCBD_augmented(lines = 350, checks = 4, b = 17, l = 3, 
                         planter = "serpentine", 
                         plotNumber = c(101,1001,2001), 
                         seed = 24, 
                         locationNames = LETTERS[1:3],
                         data = treatment_list)
ARCBD2$infoDesign
ARCBD2$layoutRandom
ARCBD2$exptNames
ARCBD2$plotNumber
head(ARCBD2$fieldBook, 12)

Generates a Rectangular Lattice Design.

Description

It randomly generates a rectangular lattice design across locations.

Usage

rectangular_lattice(
  t = NULL,
  k = NULL,
  r = NULL,
  l = 1,
  plotNumber = 101,
  locationNames = NULL,
  seed = NULL,
  data = NULL
)

Arguments

t

Number of treatments.
k

Size of incomplete blocks (number of units per incomplete block).
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.
data

(optional) Data frame with label list of treatments.

Value

A list with two elements.

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

  • fieldBook is a data frame with the rectangular lattice design field book.

Author(s)

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

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 rectangular lattice design with 6 full blocks, 4 units per IBlock (k)
# and 20 treatments in one location.
rectangularLattice1 <- rectangular_lattice(t = 20, k = 4, r = 6, l = 1, 
                                           plotNumber = 101,
                                           locationNames = "FARGO", 
                                           seed = 126)
rectangularLattice1$infoDesign
head(rectangularLattice1$fieldBook,12)

# Example 2: Generates a rectangular lattice design with 5 full blocks, 7 units per IBlock (k)
# and 56 treatments across 2 locations.
# In this case, we show how to use the option data.
treatments <- paste("ND-", 1:56, sep = "")
ENTRY <- 1:56
treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments))
head(treatment_list) 
rectangularLattice2 <- rectangular_lattice(t = 56, k = 7, r = 5, l = 2, 
                                           plotNumber = c(1001,2001),
                                           locationNames = c("Loc1", "Loc2"), 
                                           seed = 127,
                                           data = treatment_list)
rectangularLattice2$infoDesign
head(rectangularLattice2$fieldBook,12)

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
)

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

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.

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.

Author(s)

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

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)

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(..., launch.browser = TRUE)

Arguments

...

Unused, for extensibility
launch.browser

Logical. If ‘TRUE', the application is launched in the system’s default web browser.

Value

A shiny app object

Unreplicated designs using the sparse allocation approach

Description

Unreplicated designs using the sparse allocation approach

Usage

sparse_allocation(
  lines,
  nrows,
  ncols,
  l,
  planter = "serpentine",
  plotNumber,
  copies_per_entry,
  checks = NULL,
  exptName = NULL,
  locationNames,
  sparse_list,
  seed,
  data = NULL
)

Arguments

lines

Number of genotypes, experimental lines or treatments.
nrows

Number of rows in the field.
ncols

Number of columns in the field.
l

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

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

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

Number of copies per plant. When design is sparse then copies_per_entry < l
checks

Number of genotypes checks.
exptName

(optional) Name of the experiment.
locationNames

(optional) Names each location.
sparse_list

(optional) A class "Sparse" object generated by do_optim() function.
seed

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

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

Value

A list with four elements.

  • designs is a list with each location unreplicated randomization.

  • list_locs is a list with each location list of entries.

  • allocation is a matrix with the allocation of treatments.

  • size_locations is a data frame with one column for each location and one row with the size of the location.

Author(s)

Didier Murillo [aut], Salvador Gezan [aut], Ana Heilman [ctb]

References

Edmondson, R.N. Multi-level Block Designs for Comparative Experiments. JABES 25, 500–522 (2020). https://doi.org/10.1007/s13253-020-00416-0

Examples

sparse <- sparse_allocation(
  lines = 120, 
  l = 4, 
  copies_per_entry = 3, 
  checks = 4, 
  locationNames = c("LOC1", "LOC2", "LOC3", "LOC4", "LOC5"), 
  seed = 1234
)

Split a population of genotypes randomly into several locations.

Description

Split a population of genotypes randomly into several locations, with the aim of having approximatelly the same number of replicates of each genotype, line or treatment per location.

Usage

split_families(l = NULL, data = NULL)

Arguments

l

Number of locations.
data

Data frame with the entry (ENTRY) and the labels of each treatment (NAME) and number of individuals per family group (FAMILY).

Value

A list with two elements.

  • rowsEachlist is a table with a summary of cases.

  • data_locations is a data frame with the entries for each location

Author(s)

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

Examples

# Example 1: Split a population of 3000 and 200 families into 8 locations. 
# Original dataset is been simulated.
set.seed(77)
N <- 2000; families <- 100
ENTRY <- 1:N
NAME <- paste0("SB-", 1:N)
FAMILY <- vector(mode = "numeric", length = N)
x <- 1:N
for (i in x) { FAMILY[i] <- sample(1:families, size = 1, replace = TRUE) }
gen.list <- data.frame(list(ENTRY = ENTRY, NAME = NAME, FAMILY = FAMILY))
head(gen.list)
# Now we are going to use the split_families() function.
split_population <- split_families(l = 8, data = gen.list)
print(split_population)
summary(split_population)
head(split_population$data_locations,12)

Generates a Split Plot Design

Description

It randomly generates a split plot design (SPD) across locations.

Usage

split_plot(
  wp = NULL,
  sp = NULL,
  reps = NULL,
  type = 2,
  l = 1,
  plotNumber = 101,
  seed = NULL,
  locationNames = NULL,
  factorLabels = TRUE,
  data = NULL
)

Arguments

wp

Number of whole plots, as an integer or a vector.
sp

Number of sub plots per whole plot, as an integer or a vector.
reps

Number of blocks (full replicates).
type

Option for CRD or RCBD designs. Values are type = 1 (CRD) or type = 2 (RCBD). By default type = 2.
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.
locationNames

(optional) Names for each location.
factorLabels

(optional) If TRUE retain the levels labels from the original data set otherwise, numeric labels will be assigned. Default is factorLabels =TRUE.
data

(optional) Data frame with label list of treatments.

Value

A list with two elements.

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

  • fieldBook is a data frame with the split plot field book.

Author(s)

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

References

Federer, W. T. (1955). Experimental Design. Theory and Application. New York, USA. The Macmillan Company.

Examples

# Example 1: Generates a split plot design SPD with 4 whole plots, 2 sub plots per whole plot,
# and 4 reps in an RCBD arrangement. This in for a single location.
SPDExample1 <- split_plot(wp = 4, sp = 2, reps = 5, l = 1, 
                          plotNumber = 101, 
                          seed = 14,
                          type = 2, 
                          locationNames = "FARGO")
SPDExample1$infoDesign
SPDExample1$layoutlocations
head(SPDExample1$fieldBook,12)

# Example 2: Generates a split plot design SPD with 5 whole plots 
# (4 types of fungicide + one control), 10 sub plots per whole plot (10 bean varieties), 
# and 6 reps in an RCBD arrangement. This in 3 locations or sites.
# In this case, we show how to use the option data.
wp <- c("NFung", paste("Fung", 1:4, sep = ""))  # Fungicides (5 Whole plots)
sp <- paste("Beans", 1:10, sep = "")            # Beans varieties (10 sub plots)
split_plot_Data <- data.frame(list(WHOLPLOT = c(wp, rep(NA, 5)), SUBPLOT = sp))
head(split_plot_Data, 12)
SPDExample2 <- split_plot(reps = 6, l = 3, 
                          plotNumber = c(101, 1001, 2001),
                          seed = 23, 
                          type = 2, 
                          locationNames = c("A", "B", "C"),
                          data = split_plot_Data)
SPDExample2$infoDesign
SPDExample2$layoutlocations
head(SPDExample2$fieldBook,12)

Generates a Split Split Plot Design

Description

It randomly generates a split split plot design (SSPD) across locations.

Usage

split_split_plot(
  wp = NULL,
  sp = NULL,
  ssp = NULL,
  reps = NULL,
  type = 2,
  l = 1,
  plotNumber = 101,
  seed = NULL,
  locationNames = NULL,
  factorLabels = TRUE,
  data = NULL
)

Arguments

wp

Number of whole plots, as an integer or a vector.
sp

Number of sub plots per whole plot, as an integer or a vector.
ssp

Number of sub-sub plots, as an integer or a vector.
reps

Number of blocks (full replicates).
type

Option for CRD or RCBD designs. Values are type = 1 (CRD) or type = 2 (RCBD). By default type = 2.
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.
locationNames

(optional) Names for each location.
factorLabels

(optional) If TRUE retain the levels labels from the original data set otherwise, numeric labels will be assigned. Default is factorLabels =TRUE.
data

(optional) Data frame with label list of treatments.

Value

A list with two elements.

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

  • fieldBook is a data frame with the split split plot field book.

Author(s)

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

References

Federer, W. T. (1955). Experimental Design. Theory and Application. New York, USA. The Macmillan Company.

Examples

# Example 1: Generates a split split plot design SSPD with 5 whole plots, 2 sub-plots,
# 3 sub-sub plots, and 3 reps in an RCBD arrangement. This is for one location.
SSPD1 <- split_split_plot(wp = 4, sp = 2, ssp = 3, reps = 5, l = 1, 
                          plotNumber = 101, 
                          seed = 23, 
                          type = 2, 
                          locationNames = "FARGO")
SSPD1$infoDesign
head(SSPD1$fieldBook,12)

# Example 2: Generates a split split plot design SSPD with 2 whole plost 
# (Irrigation, No irrigation), 5 sub plots (4 types of fungicide + one control), and 
# 10 sub-sub plots (Ten varieties of beans), and 4 reps in an RCBD arrangement.
# This is for 3 locations. In this case, we show how to use the option data.
wp <- paste("IRR_", c("NO", "Yes"), sep = "") #Irrigation (2 Whole plots)
sp <- c("NFung", paste("Fung", 1:4, sep = "")) #Fungicides (5 Sub plots)
ssp <- paste("Beans", 1:10, sep = "") #Beans varieties (10 Sub-sub plots)
split_split_plot_Data <- data.frame(list(WHOLPLOT = c(wp, rep(NA, 8)), 
                                         SUBPLOT = c(sp, rep(NA, 5)),
                                         SUB_SUBPLOTS = ssp))
head(split_split_plot_Data, 10)
SSPD2 <- split_split_plot(reps = 4, l = 3, 
                          plotNumber = c(101, 1001, 2001),
                          seed = 23, 
                          type = 2, 
                          locationNames = c("A", "B", "C"),
                          data = split_split_plot_Data)
SSPD2$infoDesign
head(SSPD2$fieldBook,12)

Generates a Square Lattice Design.

Description

It randomly generates a square lattice design across locations.

Usage

square_lattice(
  t = NULL,
  k = NULL,
  r = NULL,
  l = 1,
  plotNumber = 101,
  locationNames = NULL,
  seed = NULL,
  data = NULL
)

Arguments

t

Number of treatments.
k

Size of incomplete blocks (number of units per incomplete block).
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.
data

(optional) Data frame with label list of treatments.

Value

A list with two elements.

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

  • fieldBook is a data frame with the square lattice design field book.

Author(s)

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

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 square lattice design with 5 full blocks, 8 units per IBlock,
# 8 IBlocks for a square number of treatmens of 64 in two locations.
squareLattice1 <- square_lattice(t = 64, k = 8, r = 5, l = 2, 
                                 plotNumber = c(1001, 2001),
                                 locationNames = c("FARGO", "MINOT"), 
                                 seed = 1986)
squareLattice1$infoDesign
head(squareLattice1$fieldBook,12)

# Example 2: Generates a square lattice design with 3 full blocks, 7 units per IBlock,
# 7 IBlocks for a square number of treatmens of 49 in one location.
# In this case, we show how to use the option data.
treatments <- paste("G", 1:49, sep = "")
ENTRY <- 1:49
treatment_list <- data.frame(list(ENTRY = ENTRY, TREATMENT = treatments))
head(treatment_list) 
squareLattice2 <- square_lattice(t = 49, k = 7, r = 3, l = 1, 
                                 plotNumber = 1001,
                                 locationNames = "CASSELTON", 
                                 seed = 1986,
                                 data = treatment_list)
squareLattice2$infoDesign
head(squareLattice2$fieldBook,12)

Strip Plot Design

Description

It randomly generates a strip plot design across locations.

Usage

strip_plot(
  Hplots = NULL,
  Vplots = NULL,
  b = 1,
  l = 1,
  plotNumber = NULL,
  planter = "serpentine",
  locationNames = NULL,
  seed = NULL,
  factorLabels = TRUE,
  randomizeH = TRUE,
  randomizeV = FALSE,
  data = NULL
)

Arguments

Hplots

Number of horizontal factors, as an integer or a vector.
Vplots

Number of vertical factors, as an integer or a vector.
b

Number of blocks (full replicates).
l

Number of locations. By default l = 1.
plotNumber

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

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

(optional) Names for each location.
seed

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

(optional) If TRUE retain the levels labels from the original data set otherwise, numeric labels will be assigned. Default is factorLabels =TRUE.
randomizeH

Logical flag: TRUE to randomize horizontal strips separately in each replicate; FALSE to randomize once per location and reuse.
randomizeV

Logical flag: TRUE to randomize vertical strips separately in each replicate; FALSE to randomize once per location and reuse.
data

(optional) data frame with the labels of vertical and horizontal plots.

Value

A list with four elements.

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

  • stripsBlockLoc is a list with the strip blocks for each location.

  • plotLayouts is a list with the layout plot numbers for each location.

  • fieldBook is a data frame with the strip plot field book.

Author(s)

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

References

Federer, W. T. (1955). Experimental Design. Theory and Application. New York, USA. The Macmillan Company.

Examples

# Example 1: Generates a strip plot design with 5 vertical strips and 4 horizontal strips,
# with 3 reps in one location.
H <- paste("H", 1:4, sep = "")
V <- paste("V", 1:5, sep = "")
strip1 <- strip_plot(
  Hplots = H,
  Vplots = V,
  b = 3,
  l = 1,
  plotNumber = 101,
  planter = "serpentine",
  locationNames = "A",
  seed = 333
)
strip1$infoDesign
strip1$stripsBlockLoc
strip1$plotLayouts
head(strip1$fieldBook, 12)

# Example 2: Generates a strip plot design with 5 vertical strips and 5 horizontal strips,
# with 6 reps across to 3 locations. In this case, we show how to use the option data.
Hplots <- LETTERS[1:5]
Vplots <- LETTERS[1:4]
strip_data <- data.frame(list(HPLOTS = Hplots, VPLOTS = c(Vplots, NA)))
head(strip_data)
strip2 <- strip_plot(
  Hplots = 5,
  Vplots = 5,
  b = 6,
  l = 3,
  plotNumber = c(101, 1001, 2001),
  planter = "cartesian",
  locationNames = c("A", "B", "C"),
  seed = 222,
  data = strip_data
)
strip2$infoDesign
strip2$stripsBlockLoc
strip2$plotLayouts
head(strip2$fieldBook, 12)

Summary a FielDHub object

Description

Summarise information on the design parameters, and data frame structure

Usage

## S3 method for class 'FielDHub'
summary(object, ...)

Arguments

object

an object inheriting from class FielDHub
...

Unused, for extensibility

Value

an object inheriting from class summary.FielDHub

Author(s)

Thiago de Paula Oliveira, [email protected]

Examples

# Example 1: Generates a CRD design with 5 treatments and 5 reps each.
crd1 <- CRD(t = 5, reps = 5, plotNumber = 101,
seed = 1985, locationName = "Fargo")
crd1$infoDesign
summary(crd1)

Swap pairs in a matrix of integers

Description

Modifies the input matrix X to ensure that the distance between any two occurrences of the same integer is at least a distance d, by swapping one of the occurrences with a candidate cell of a different integer. The function starts with starting_dist = 3 and increases it by 1 until the algorithm no longer converges or stop_iter iterations have been performed. This version evaluates candidate swaps using both the mean pairwise distance and a centrality penalty, and it uses candidate sampling to reduce computation.

Usage

swap_pairs(
  X,
  starting_dist = 3,
  stop_iter = 10,
  lambda = 0.5,
  dist_method = "euclidean",
  candidate_sample_size = 4
)

Arguments

X

A matrix of integers.
starting_dist

The minimum starting distance to enforce between pairs of occurrences of the same integer. Default is 3.
stop_iter

The maximum number of iterations to perform. Default is 50.
lambda

A tuning parameter for the centrality penalty. Default is 0.1.
dist_method

The method used for distance calculation. Options are "euclidean" (default) and "manhattan".
candidate_sample_size

Maximum number of candidate cells to evaluate per swap. Default is 5.

Value

A list containing:

optim_design

The modified matrix.
designs

A list of all intermediate designs, starting from the input matrix.
distances

A list of all pair distances for each intermediate design.
min_distance

The minimum distance between pairs of occurrences of the same integer in the final design.
pairwise_distance

A data frame with the pairwise distances for the final design.
rows_incidence

A vector recording the number of rows with repeated integers for each iteration.

Examples

set.seed(123)
X <- matrix(sample(c(rep(1:10, 2), 11:50), replace = FALSE), ncol = 10)
B <- swap_pairs(
  X,
  starting_dist = 3,
  stop_iter = 50,
  lambda = 0.5,
  dist_method = "euclidean",
  candidate_sample_size = 3
)
B$optim_design