Skip to contents

Generates a Full Factorial Design

Source: R/fct_full_factorial.R
full_factorial.Rd

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.

References

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

Author

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

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
#> $factors
#> [1] "A" "B" "C"
#> 
#> $levels
#> [1] 0 1 0 1 0 1
#> 
#> $runs
#> [1] 8
#> 
#> $all_treatments
#>   A B C
#> 1 0 0 0
#> 2 1 0 0
#> 3 0 1 0
#> 4 1 1 0
#> 5 0 0 1
#> 6 1 0 1
#> 7 0 1 1
#> 8 1 1 1
#> 
#> $reps
#> [1] 3
#> 
#> $locations
#> [1] 1
#> 
#> $location_names
#> [1] "FARGO"
#> 
#> $kind
#> [1] "RCBD"
#> 
#> $levels_each_factor
#> [1] 2 2 2
#> 
#> $id_design
#> [1] 4
#> 
head(fullFact1$fieldBook,10)
#>    ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB
#> 1   1    FARGO  101   1        0        1        1    0*1*1
#> 2   2    FARGO  102   1        1        1        1    1*1*1
#> 3   3    FARGO  103   1        1        0        0    1*0*0
#> 4   4    FARGO  104   1        0        1        0    0*1*0
#> 5   5    FARGO  105   1        1        1        0    1*1*0
#> 6   6    FARGO  106   1        1        0        1    1*0*1
#> 7   7    FARGO  107   1        0        0        0    0*0*0
#> 8   8    FARGO  108   1        0        0        1    0*0*1
#> 16  9    FARGO  109   2        1        1        0    1*1*0
#> 15 10    FARGO  110   2        0        0        0    0*0*0

# 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)
#>   FACTOR LEVEL
#> 1      A    a0
#> 2      A    a1
#> 3      B    b0
#> 4      B    b1
#> 5      B    b2
#> 6      C    c0
#> 7      C    c1
# 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
#> $factors
#> [1] "A" "B" "C"
#> 
#> $levels
#> $levels[[1]]
#> [1] "a0" "a1"
#> 
#> $levels[[2]]
#> [1] "b0" "b1" "b2"
#> 
#> $levels[[3]]
#> [1] "c0" "c1"
#> 
#> 
#> $runs
#> [1] 12
#> 
#> $all_treatments
#>     A  B  C
#> 1  a0 b0 c0
#> 2  a1 b0 c0
#> 3  a0 b1 c0
#> 4  a1 b1 c0
#> 5  a0 b2 c0
#> 6  a1 b2 c0
#> 7  a0 b0 c1
#> 8  a1 b0 c1
#> 9  a0 b1 c1
#> 10 a1 b1 c1
#> 11 a0 b2 c1
#> 12 a1 b2 c1
#> 
#> $reps
#> [1] 5
#> 
#> $locations
#> [1] 3
#> 
#> $location_names
#> [1] "Loc1" "Loc2" "Loc3"
#> 
#> $kind
#> [1] "RCBD"
#> 
#> $levels_each_factor
#> [1] 2 3 2
#> 
#> $id_design
#> [1] 4
#> 
head(fullFact2$fieldBook,10)
#>    ID LOCATION PLOT REP FACTOR_A FACTOR_B FACTOR_C TRT_COMB
#> 1   1     Loc1  101   1       a0       b1       c0 a0*b1*c0
#> 2   2     Loc1  102   1       a1       b0       c1 a1*b0*c1
#> 3   3     Loc1  103   1       a1       b2       c1 a1*b2*c1
#> 4   4     Loc1  104   1       a0       b1       c1 a0*b1*c1
#> 5   5     Loc1  105   1       a1       b0       c0 a1*b0*c0
#> 6   6     Loc1  106   1       a0       b0       c1 a0*b0*c1
#> 7   7     Loc1  107   1       a1       b1       c0 a1*b1*c0
#> 8   8     Loc1  108   1       a0       b2       c1 a0*b2*c1
#> 9   9     Loc1  109   1       a1       b1       c1 a1*b1*c1
#> 10 10     Loc1  110   1       a0       b0       c0 a0*b0*c0