Initialise a design data frame with or without blocking.
Usage
initialise_design_df(
items = NULL,
nrows = NULL,
ncols = NULL,
block_nrows = NULL,
block_ncols = NULL,
splits = NULL,
designs = NULL,
design_col = "site"
)
initialize_design_df(
items = NULL,
nrows = NULL,
ncols = NULL,
block_nrows = NULL,
block_ncols = NULL,
splits = NULL,
designs = NULL,
design_col = "site"
)Arguments
- items
Items to be placed in the design. Either a single numeric value (the number of equally replicated items), or a vector of items. (default:
NULL)- nrows
Number of rows in the design (default:
NULL)- ncols
Number of columns in the design (default:
NULL)- block_nrows
Number of rows in each block (default:
NULL)- block_ncols
Number of columns in each block (default:
NULL)- splits
Deprecated; use
initialise_split_design_df()instead. A named list of nested-unit specifications, ordered from the outermost level to the innermost. Each entry is itself a list withnrowsandncols(the dimensions of one unit at that level, in cells) and an optionalitems(treatments to allocate across the units at that level, one item per unit, ordered by parent then within-parent ID). For each level,<name>and<name>_treatmentcolumns are added (the latter only ifitemsis provided). Used to build hierarchical layouts such as split-plot, split-split-plot, and strip-plot designs. (default:NULL)- designs
A list of named arguments describing design specifications, required if
nrowsandncolsare absent. (default:NULL)- design_col
A column name to distinguish different designs (default:
"site")
Examples
initialise_design_df(
items = c(1, 2, 2, 1, 3, 3, 1, 3, 3),
nrows = 3,
ncols = 3
)
#> row col treatment
#> 1 1 1 1
#> 2 2 1 2
#> 3 3 1 2
#> 4 1 2 1
#> 5 2 2 3
#> 6 3 2 3
#> 7 1 3 1
#> 8 2 3 3
#> 9 3 3 3
# blocking
initialise_design_df(rep(1:8, 4), 8, 4, 2, 2)
#> row col treatment row_block col_block block
#> 1 1 1 1 1 1 1
#> 2 2 1 2 1 1 1
#> 3 3 1 5 2 1 2
#> 4 4 1 6 2 1 2
#> 5 5 1 1 3 1 3
#> 6 6 1 2 3 1 3
#> 7 7 1 5 4 1 4
#> 8 8 1 6 4 1 4
#> 9 1 2 3 1 1 1
#> 10 2 2 4 1 1 1
#> 11 3 2 7 2 1 2
#> 12 4 2 8 2 1 2
#> 13 5 2 3 3 1 3
#> 14 6 2 4 3 1 3
#> 15 7 2 7 4 1 4
#> 16 8 2 8 4 1 4
#> 17 1 3 1 1 2 5
#> 18 2 3 2 1 2 5
#> 19 3 3 5 2 2 6
#> 20 4 3 6 2 2 6
#> 21 5 3 1 3 2 7
#> 22 6 3 2 3 2 7
#> 23 7 3 5 4 2 8
#> 24 8 3 6 4 2 8
#> 25 1 4 3 1 2 5
#> 26 2 4 4 1 2 5
#> 27 3 4 7 2 2 6
#> 28 4 4 8 2 2 6
#> 29 5 4 3 3 2 7
#> 30 6 4 4 3 2 7
#> 31 7 4 7 4 2 8
#> 32 8 4 8 4 2 8
# another blocking example
initialise_design_df(
items = paste0("T", 1:6),
nrows = 4,
ncols = 6,
block_nrows = 2,
block_ncols = 3
)
#> row col treatment row_block col_block block
#> 1 1 1 T1 1 1 1
#> 2 2 1 T2 1 1 1
#> 3 3 1 T1 2 1 2
#> 4 4 1 T2 2 1 2
#> 5 1 2 T3 1 1 1
#> 6 2 2 T4 1 1 1
#> 7 3 2 T3 2 1 2
#> 8 4 2 T4 2 1 2
#> 9 1 3 T5 1 1 1
#> 10 2 3 T6 1 1 1
#> 11 3 3 T5 2 1 2
#> 12 4 3 T6 2 1 2
#> 13 1 4 T1 1 2 3
#> 14 2 4 T2 1 2 3
#> 15 3 4 T1 2 2 4
#> 16 4 4 T2 2 2 4
#> 17 1 5 T3 1 2 3
#> 18 2 5 T4 1 2 3
#> 19 3 5 T3 2 2 4
#> 20 4 5 T4 2 2 4
#> 21 1 6 T5 1 2 3
#> 22 2 6 T6 1 2 3
#> 23 3 6 T5 2 2 4
#> 24 4 6 T6 2 2 4
# MET
initialise_design_df(
items = c(rep(1:10, 6), rep(11:20, 8)),
designs = list(
a = list(nrows = 10, ncols = 3),
b = list(nrows = 10, ncols = 5),
c = list(nrows = 10, ncols = 6)
)
)
#> row col treatment site
#> 1 1 1 1 a
#> 2 2 1 2 a
#> 3 3 1 3 a
#> 4 4 1 4 a
#> 5 5 1 5 a
#> 6 6 1 6 a
#> 7 7 1 7 a
#> 8 8 1 8 a
#> 9 9 1 9 a
#> 10 10 1 10 a
#> 11 1 2 1 a
#> 12 2 2 2 a
#> 13 3 2 3 a
#> 14 4 2 4 a
#> 15 5 2 5 a
#> 16 6 2 6 a
#> 17 7 2 7 a
#> 18 8 2 8 a
#> 19 9 2 9 a
#> 20 10 2 10 a
#> 21 1 3 1 a
#> 22 2 3 2 a
#> 23 3 3 3 a
#> 24 4 3 4 a
#> 25 5 3 5 a
#> 26 6 3 6 a
#> 27 7 3 7 a
#> 28 8 3 8 a
#> 29 9 3 9 a
#> 30 10 3 10 a
#> 31 1 1 1 b
#> 32 2 1 2 b
#> 33 3 1 3 b
#> 34 4 1 4 b
#> 35 5 1 5 b
#> 36 6 1 6 b
#> 37 7 1 7 b
#> 38 8 1 8 b
#> 39 9 1 9 b
#> 40 10 1 10 b
#> 41 1 2 1 b
#> 42 2 2 2 b
#> 43 3 2 3 b
#> 44 4 2 4 b
#> 45 5 2 5 b
#> 46 6 2 6 b
#> 47 7 2 7 b
#> 48 8 2 8 b
#> 49 9 2 9 b
#> 50 10 2 10 b
#> 51 1 3 1 b
#> 52 2 3 2 b
#> 53 3 3 3 b
#> 54 4 3 4 b
#> 55 5 3 5 b
#> 56 6 3 6 b
#> 57 7 3 7 b
#> 58 8 3 8 b
#> 59 9 3 9 b
#> 60 10 3 10 b
#> 61 1 4 11 b
#> 62 2 4 12 b
#> 63 3 4 13 b
#> 64 4 4 14 b
#> 65 5 4 15 b
#> 66 6 4 16 b
#> 67 7 4 17 b
#> 68 8 4 18 b
#> 69 9 4 19 b
#> 70 10 4 20 b
#> 71 1 5 11 b
#> 72 2 5 12 b
#> 73 3 5 13 b
#> 74 4 5 14 b
#> 75 5 5 15 b
#> 76 6 5 16 b
#> 77 7 5 17 b
#> 78 8 5 18 b
#> 79 9 5 19 b
#> 80 10 5 20 b
#> 81 1 1 11 c
#> 82 2 1 12 c
#> 83 3 1 13 c
#> 84 4 1 14 c
#> 85 5 1 15 c
#> 86 6 1 16 c
#> 87 7 1 17 c
#> 88 8 1 18 c
#> 89 9 1 19 c
#> 90 10 1 20 c
#> 91 1 2 11 c
#> 92 2 2 12 c
#> 93 3 2 13 c
#> 94 4 2 14 c
#> 95 5 2 15 c
#> 96 6 2 16 c
#> 97 7 2 17 c
#> 98 8 2 18 c
#> 99 9 2 19 c
#> 100 10 2 20 c
#> 101 1 3 11 c
#> 102 2 3 12 c
#> 103 3 3 13 c
#> 104 4 3 14 c
#> 105 5 3 15 c
#> 106 6 3 16 c
#> 107 7 3 17 c
#> 108 8 3 18 c
#> 109 9 3 19 c
#> 110 10 3 20 c
#> 111 1 4 11 c
#> 112 2 4 12 c
#> 113 3 4 13 c
#> 114 4 4 14 c
#> 115 5 4 15 c
#> 116 6 4 16 c
#> 117 7 4 17 c
#> 118 8 4 18 c
#> 119 9 4 19 c
#> 120 10 4 20 c
#> 121 1 5 11 c
#> 122 2 5 12 c
#> 123 3 5 13 c
#> 124 4 5 14 c
#> 125 5 5 15 c
#> 126 6 5 16 c
#> 127 7 5 17 c
#> 128 8 5 18 c
#> 129 9 5 19 c
#> 130 10 5 20 c
#> 131 1 6 11 c
#> 132 2 6 12 c
#> 133 3 6 13 c
#> 134 4 6 14 c
#> 135 5 6 15 c
#> 136 6 6 16 c
#> 137 7 6 17 c
#> 138 8 6 18 c
#> 139 9 6 19 c
#> 140 10 6 20 c
# MET with different items for each site
initialise_design_df(
designs = list(
a = list(items = 1:30, nrows = 10, ncols = 6),
b = list(items = 1:25, nrows = 10, ncols = 5),
c = list(items = 16:30, nrows = 10, ncols = 3)
)
)
#> row col treatment site
#> 1 1 1 1 a
#> 2 2 1 2 a
#> 3 3 1 3 a
#> 4 4 1 4 a
#> 5 5 1 5 a
#> 6 6 1 6 a
#> 7 7 1 7 a
#> 8 8 1 8 a
#> 9 9 1 9 a
#> 10 10 1 10 a
#> 11 1 2 11 a
#> 12 2 2 12 a
#> 13 3 2 13 a
#> 14 4 2 14 a
#> 15 5 2 15 a
#> 16 6 2 16 a
#> 17 7 2 17 a
#> 18 8 2 18 a
#> 19 9 2 19 a
#> 20 10 2 20 a
#> 21 1 3 21 a
#> 22 2 3 22 a
#> 23 3 3 23 a
#> 24 4 3 24 a
#> 25 5 3 25 a
#> 26 6 3 26 a
#> 27 7 3 27 a
#> 28 8 3 28 a
#> 29 9 3 29 a
#> 30 10 3 30 a
#> 31 1 4 1 a
#> 32 2 4 2 a
#> 33 3 4 3 a
#> 34 4 4 4 a
#> 35 5 4 5 a
#> 36 6 4 6 a
#> 37 7 4 7 a
#> 38 8 4 8 a
#> 39 9 4 9 a
#> 40 10 4 10 a
#> 41 1 5 11 a
#> 42 2 5 12 a
#> 43 3 5 13 a
#> 44 4 5 14 a
#> 45 5 5 15 a
#> 46 6 5 16 a
#> 47 7 5 17 a
#> 48 8 5 18 a
#> 49 9 5 19 a
#> 50 10 5 20 a
#> 51 1 6 21 a
#> 52 2 6 22 a
#> 53 3 6 23 a
#> 54 4 6 24 a
#> 55 5 6 25 a
#> 56 6 6 26 a
#> 57 7 6 27 a
#> 58 8 6 28 a
#> 59 9 6 29 a
#> 60 10 6 30 a
#> 61 1 1 1 b
#> 62 2 1 2 b
#> 63 3 1 3 b
#> 64 4 1 4 b
#> 65 5 1 5 b
#> 66 6 1 6 b
#> 67 7 1 7 b
#> 68 8 1 8 b
#> 69 9 1 9 b
#> 70 10 1 10 b
#> 71 1 2 11 b
#> 72 2 2 12 b
#> 73 3 2 13 b
#> 74 4 2 14 b
#> 75 5 2 15 b
#> 76 6 2 16 b
#> 77 7 2 17 b
#> 78 8 2 18 b
#> 79 9 2 19 b
#> 80 10 2 20 b
#> 81 1 3 21 b
#> 82 2 3 22 b
#> 83 3 3 23 b
#> 84 4 3 24 b
#> 85 5 3 25 b
#> 86 6 3 1 b
#> 87 7 3 2 b
#> 88 8 3 3 b
#> 89 9 3 4 b
#> 90 10 3 5 b
#> 91 1 4 6 b
#> 92 2 4 7 b
#> 93 3 4 8 b
#> 94 4 4 9 b
#> 95 5 4 10 b
#> 96 6 4 11 b
#> 97 7 4 12 b
#> 98 8 4 13 b
#> 99 9 4 14 b
#> 100 10 4 15 b
#> 101 1 5 16 b
#> 102 2 5 17 b
#> 103 3 5 18 b
#> 104 4 5 19 b
#> 105 5 5 20 b
#> 106 6 5 21 b
#> 107 7 5 22 b
#> 108 8 5 23 b
#> 109 9 5 24 b
#> 110 10 5 25 b
#> 111 1 1 16 c
#> 112 2 1 17 c
#> 113 3 1 18 c
#> 114 4 1 19 c
#> 115 5 1 20 c
#> 116 6 1 21 c
#> 117 7 1 22 c
#> 118 8 1 23 c
#> 119 9 1 24 c
#> 120 10 1 25 c
#> 121 1 2 26 c
#> 122 2 2 27 c
#> 123 3 2 28 c
#> 124 4 2 29 c
#> 125 5 2 30 c
#> 126 6 2 16 c
#> 127 7 2 17 c
#> 128 8 2 18 c
#> 129 9 2 19 c
#> 130 10 2 20 c
#> 131 1 3 21 c
#> 132 2 3 22 c
#> 133 3 3 23 c
#> 134 4 3 24 c
#> 135 5 3 25 c
#> 136 6 3 26 c
#> 137 7 3 27 c
#> 138 8 3 28 c
#> 139 9 3 29 c
#> 140 10 3 30 c