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simple_randomization(20, c("Control", "Treatment"), seed = 123)
#> # A tibble: 20 × 2
#> Subject Group
#> <int> <chr>
#> 1 1 Treatment
#> 2 2 Control
#> 3 3 Treatment
#> 4 4 Control
#> 5 5 Control
#> 6 6 Treatment
#> 7 7 Control
#> 8 8 Control
#> 9 9 Control
#> 10 10 Treatment
#> 11 11 Control
#> 12 12 Treatment
#> 13 13 Control
#> 14 14 Control
#> 15 15 Treatment
#> 16 16 Control
#> 17 17 Treatment
#> 18 18 Treatment
#> 19 19 Treatment
#> 20 20 Control
block_randomization(24, c("Control", "Treatment"), block_size = 4, seed = 123)
#> # A tibble: 24 × 3
#> Subject Block Group
#> <int> <int> <chr>
#> 1 1 1 Treatment
#> 2 2 1 Treatment
#> 3 3 1 Control
#> 4 4 1 Control
#> 5 5 2 Treatment
#> 6 6 2 Control
#> 7 7 2 Treatment
#> 8 8 2 Control
#> 9 9 3 Treatment
#> 10 10 3 Control
#> # ℹ 14 more rows
variable_block_randomization(30, c("Control", "Treatment"), c(4, 6, 8), seed = 123)
#> # A tibble: 30 × 4
#> Subject Block BlockSize Group
#> <int> <int> <int> <chr>
#> 1 1 1 8 Control
#> 2 2 1 8 Control
#> 3 3 1 8 Treatment
#> 4 4 1 8 Treatment
#> 5 5 1 8 Control
#> 6 6 1 8 Treatment
#> 7 7 1 8 Treatment
#> 8 8 1 8 Control
#> 9 9 2 8 Control
#> 10 10 2 8 Control
#> # ℹ 20 more rowsdat <- data.frame(
ID = 1:40,
Sex = rep(c("Male", "Female"), 20),
Site = rep(c("A", "B"), each = 20)
)
stratified_randomization(dat, c("Sex", "Site"), c("Control", "Treatment"), seed = 123)
#> # A tibble: 40 × 5
#> ID Sex Site Treatment Stratum
#> <int> <chr> <chr> <chr> <chr>
#> 1 1 Male A Control Male::A
#> 2 2 Female A Treatment Female::A
#> 3 3 Male A Control Male::A
#> 4 4 Female A Control Female::A
#> 5 5 Male A Control Male::A
#> 6 6 Female A Treatment Female::A
#> 7 7 Male A Control Male::A
#> 8 8 Female A Control Female::A
#> 9 9 Male A Control Male::A
#> 10 10 Female A Control Female::A
#> # ℹ 30 more rows
stratified_block_randomization(dat, c("Sex", "Site"), c("Control", "Treatment"), 4, seed = 123)
#> # A tibble: 40 × 6
#> ID Sex Site Treatment Stratum Block
#> <int> <chr> <chr> <chr> <chr> <int>
#> 1 1 Male A Control Male::A 7
#> 2 2 Female A Treatment Female::A 1
#> 3 3 Male A Treatment Male::A 7
#> 4 4 Female A Treatment Female::A 1
#> 5 5 Male A Treatment Male::A 7
#> 6 6 Female A Control Female::A 1
#> 7 7 Male A Control Male::A 7
#> 8 8 Female A Control Female::A 1
#> 9 9 Male A Treatment Male::A 8
#> 10 10 Female A Treatment Female::A 2
#> # ℹ 30 more rows
minimization_randomization(dat, c("Sex", "Site"), seed = 123)
#> # A tibble: 40 × 4
#> ID Sex Site Treatment
#> <int> <chr> <chr> <chr>
#> 1 1 Male A Control
#> 2 2 Female A Treatment
#> 3 3 Male A Treatment
#> 4 4 Female A Control
#> 5 5 Male A Control
#> 6 6 Female A Treatment
#> 7 7 Male A Control
#> 8 8 Female A Control
#> 9 9 Male A Control
#> 10 10 Female A Control
#> # ℹ 30 more rowscompletely_randomized_design(20, c("A", "B"), seed = 123)
#> # A tibble: 20 × 2
#> Unit Treatment
#> <int> <chr>
#> 1 1 B
#> 2 2 A
#> 3 3 B
#> 4 4 A
#> 5 5 A
#> 6 6 B
#> 7 7 A
#> 8 8 A
#> 9 9 A
#> 10 10 B
#> 11 11 A
#> 12 12 B
#> 13 13 A
#> 14 14 A
#> 15 15 B
#> 16 16 A
#> 17 17 B
#> 18 18 B
#> 19 19 B
#> 20 20 A
randomized_block_design(24, c("A", "B"), block_size = 4, seed = 123)
#> # A tibble: 24 × 3
#> Unit Block Treatment
#> <int> <int> <chr>
#> 1 1 1 B
#> 2 2 1 B
#> 3 3 1 A
#> 4 4 1 A
#> 5 5 2 B
#> 6 6 2 A
#> 7 7 2 B
#> 8 8 2 A
#> 9 9 3 B
#> 10 10 3 A
#> # ℹ 14 more rows
factorial_design(list(Dose = c("Low", "High"), Diet = c("A", "B")), replicates = 2, seed = 123)
#> Unit Dose Diet
#> 4 1 High B
#> 4.1 2 High B
#> 2 3 High A
#> 3.1 4 Low B
#> 1.1 5 Low A
#> 2.1 6 High A
#> 3 7 Low B
#> 1 8 Low A
latin_square(LETTERS[1:4], seed = 123)
#> Col_1 Col_2 Col_3 Col_4
#> Row_1 "C" "D" "A" "B"
#> Row_2 "A" "C" "B" "D"
#> Row_3 "B" "A" "D" "C"
#> Row_4 "D" "B" "C" "A"
crossover_design(c("A", "B"), subjects = 8, periods = 2, seed = 123)
#> # A tibble: 8 × 3
#> Subject Period_1 Period_2
#> <int> <chr> <chr>
#> 1 1 A B
#> 2 2 A B
#> 3 3 A B
#> 4 4 B A
#> 5 5 A B
#> 6 6 B A
#> 7 7 B A
#> 8 8 B Asch <- simple_randomization(40, c("Control", "Treatment"), seed = 123)
allocation_summary(sch)
#> # A tibble: 2 × 3
#> Group Count Percentage
#> <chr> <int> <dbl>
#> 1 Control 24 60
#> 2 Treatment 16 40
plot_randomization(sch)
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