Returns one or more of underlying attributes used in design generation/evaluation
Arguments
- output
The output of either `gen_design()` or `eval_design()`/`eval_design_mc()``
- attr
Default `NULL`. Return just the specific value requested. Potential values are `model.matrix` for model used, `moments.matrix`, `variance.matrix`, `alias.matrix`, `correlation.matrix`, and `model` for the model used in the evaluation/generation of the design.
- round
Default `TRUE`. Rounds off values smaller than the magnitude `1e-15`` in the `correlation.matrix` and `alias.matrix` matrix attributes.
Examples
# We can extract the attributes of a design from either the output of `gen_design()`
# or the output of `eval_design()`
factorialcoffee = expand.grid(cost = c(1, 2),
type = as.factor(c("Kona", "Colombian", "Ethiopian", "Sumatra")),
size = as.factor(c("Short", "Grande", "Venti")))
designcoffee = gen_design(factorialcoffee, ~cost + size + type, trials = 29,
optimality = "D", repeats = 100)
#Extract a list of all attributes
get_attribute(designcoffee)
#> $model.matrix
#> (Intercept) cost size1 size2 type1 type2 type3
#> [1,] 1 -1 -0.7071068 1.224745 -0.5773503 1.6329932 0.000000
#> [2,] 1 1 1.4142136 0.000000 -0.5773503 -0.8164966 -1.414214
#> [3,] 1 1 -0.7071068 -1.224745 -0.5773503 -0.8164966 1.414214
#> [4,] 1 -1 -0.7071068 -1.224745 -0.5773503 1.6329932 0.000000
#> [5,] 1 -1 1.4142136 0.000000 1.7320508 0.0000000 0.000000
#> [6,] 1 -1 1.4142136 0.000000 -0.5773503 -0.8164966 -1.414214
#> [7,] 1 -1 -0.7071068 -1.224745 -0.5773503 -0.8164966 -1.414214
#> [8,] 1 1 -0.7071068 1.224745 -0.5773503 -0.8164966 -1.414214
#> [9,] 1 -1 -0.7071068 -1.224745 1.7320508 0.0000000 0.000000
#> [10,] 1 1 1.4142136 0.000000 -0.5773503 -0.8164966 -1.414214
#> [11,] 1 1 -0.7071068 -1.224745 1.7320508 0.0000000 0.000000
#> [12,] 1 -1 1.4142136 0.000000 -0.5773503 1.6329932 0.000000
#> [13,] 1 -1 -0.7071068 -1.224745 -0.5773503 -0.8164966 1.414214
#> [14,] 1 1 -0.7071068 -1.224745 1.7320508 0.0000000 0.000000
#> [15,] 1 1 1.4142136 0.000000 -0.5773503 1.6329932 0.000000
#> [16,] 1 1 1.4142136 0.000000 -0.5773503 -0.8164966 1.414214
#> [17,] 1 1 -0.7071068 1.224745 1.7320508 0.0000000 0.000000
#> [18,] 1 -1 -0.7071068 1.224745 -0.5773503 -0.8164966 -1.414214
#> [19,] 1 1 1.4142136 0.000000 -0.5773503 -0.8164966 1.414214
#> [20,] 1 -1 -0.7071068 1.224745 1.7320508 0.0000000 0.000000
#> [21,] 1 -1 1.4142136 0.000000 1.7320508 0.0000000 0.000000
#> [22,] 1 -1 -0.7071068 1.224745 -0.5773503 -0.8164966 1.414214
#> [23,] 1 1 1.4142136 0.000000 -0.5773503 1.6329932 0.000000
#> [24,] 1 1 -0.7071068 1.224745 -0.5773503 -0.8164966 1.414214
#> [25,] 1 1 -0.7071068 -1.224745 -0.5773503 1.6329932 0.000000
#> [26,] 1 -1 1.4142136 0.000000 -0.5773503 -0.8164966 1.414214
#> [27,] 1 1 -0.7071068 1.224745 1.7320508 0.0000000 0.000000
#> [28,] 1 1 -0.7071068 1.224745 -0.5773503 1.6329932 0.000000
#> [29,] 1 1 -0.7071068 -1.224745 -0.5773503 -0.8164966 -1.414214
#>
#> $moments.matrix
#> (Intercept) cost size1 size2 type1 type2 type3
#> (Intercept) 1 0.0000000 0 0 0 0 0
#> cost 0 0.3333333 0 0 0 0 0
#> size1 0 0.0000000 1 0 0 0 0
#> size2 0 0.0000000 0 1 0 0 0
#> type1 0 0.0000000 0 0 1 0 0
#> type2 0 0.0000000 0 0 0 1 0
#> type3 0 0.0000000 0 0 0 0 1
#>
#> $variance.matrix
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13]
#> [1,] 1 0 0 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 1 0 0 0 0 0 0 0 0 0 0 0
#> [3,] 0 0 1 0 0 0 0 0 0 0 0 0 0
#> [4,] 0 0 0 1 0 0 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 1 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 1 0 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0 1 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0 0 1 0 0 0 0 0
#> [9,] 0 0 0 0 0 0 0 0 1 0 0 0 0
#> [10,] 0 0 0 0 0 0 0 0 0 1 0 0 0
#> [11,] 0 0 0 0 0 0 0 0 0 0 1 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 0 0 1 0
#> [13,] 0 0 0 0 0 0 0 0 0 0 0 0 1
#> [14,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [15,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [16,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [17,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [18,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [20,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [21,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [22,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [23,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [24,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [25,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [26,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [28,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 0 0 0 0 0 0 0 0
#> [,14] [,15] [,16] [,17] [,18] [,19] [,20] [,21] [,22] [,23] [,24] [,25]
#> [1,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [2,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [3,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [4,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [5,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [6,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [7,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [8,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [9,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [10,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [11,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [12,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [13,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [14,] 1 0 0 0 0 0 0 0 0 0 0 0
#> [15,] 0 1 0 0 0 0 0 0 0 0 0 0
#> [16,] 0 0 1 0 0 0 0 0 0 0 0 0
#> [17,] 0 0 0 1 0 0 0 0 0 0 0 0
#> [18,] 0 0 0 0 1 0 0 0 0 0 0 0
#> [19,] 0 0 0 0 0 1 0 0 0 0 0 0
#> [20,] 0 0 0 0 0 0 1 0 0 0 0 0
#> [21,] 0 0 0 0 0 0 0 1 0 0 0 0
#> [22,] 0 0 0 0 0 0 0 0 1 0 0 0
#> [23,] 0 0 0 0 0 0 0 0 0 1 0 0
#> [24,] 0 0 0 0 0 0 0 0 0 0 1 0
#> [25,] 0 0 0 0 0 0 0 0 0 0 0 1
#> [26,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [27,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [28,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [29,] 0 0 0 0 0 0 0 0 0 0 0 0
#> [,26] [,27] [,28] [,29]
#> [1,] 0 0 0 0
#> [2,] 0 0 0 0
#> [3,] 0 0 0 0
#> [4,] 0 0 0 0
#> [5,] 0 0 0 0
#> [6,] 0 0 0 0
#> [7,] 0 0 0 0
#> [8,] 0 0 0 0
#> [9,] 0 0 0 0
#> [10,] 0 0 0 0
#> [11,] 0 0 0 0
#> [12,] 0 0 0 0
#> [13,] 0 0 0 0
#> [14,] 0 0 0 0
#> [15,] 0 0 0 0
#> [16,] 0 0 0 0
#> [17,] 0 0 0 0
#> [18,] 0 0 0 0
#> [19,] 0 0 0 0
#> [20,] 0 0 0 0
#> [21,] 0 0 0 0
#> [22,] 0 0 0 0
#> [23,] 0 0 0 0
#> [24,] 0 0 0 0
#> [25,] 0 0 0 0
#> [26,] 1 0 0 0
#> [27,] 0 1 0 0
#> [28,] 0 0 1 0
#> [29,] 0 0 0 1
#>
#> $alias.matrix
#> cost size1 size2 type1 type2 type3 cost:size1 cost:size2
#> (Intercept) 0 0 0 0 0 0 0.005324599 0.0000000
#> cost 1 0 0 0 0 0 0.078804069 0.0000000
#> size1 0 1 0 0 0 0 0.048192771 0.0000000
#> size2 0 0 1 0 0 0 0.000000000 0.1111111
#> type1 0 0 0 1 0 0 -0.304341723 0.0000000
#> type2 0 0 0 0 1 0 0.000000000 0.0000000
#> type3 0 0 0 0 0 1 0.000000000 0.0000000
#> cost:type1 cost:type2 cost:type3 size1:type1 size2:type1
#> (Intercept) -0.03478014 0.0000000 0.0000000 -0.12173669 0.0000000
#> cost 0.06260425 0.0000000 0.0000000 -0.33200915 0.0000000
#> size1 -0.31479386 0.0000000 0.0000000 -0.06260425 0.0000000
#> size2 0.00000000 0.0000000 0.0000000 0.00000000 0.1924501
#> type1 -0.01204819 0.0000000 0.0000000 -0.11288150 0.0000000
#> type2 0.00000000 0.1428571 0.0000000 0.00000000 0.0000000
#> type3 0.00000000 0.0000000 0.1428571 0.00000000 0.0000000
#> size1:type2 size2:type2 size1:type3 size2:type3
#> (Intercept) 0.0000000 0 0.0000000 0
#> cost 0.0000000 0 0.0000000 0
#> size1 0.0000000 0 0.0000000 0
#> size2 0.0000000 0 0.0000000 0
#> type1 0.0000000 0 0.0000000 0
#> type2 0.2020305 0 0.0000000 0
#> type3 0.0000000 0 0.2020305 0
#>
#> $correlation.matrix
#> cost size1 size2 type1 type2 type3
#> cost 1.00000000 0.0000000 0 0.05776713 0 0
#> size1 0.00000000 1.0000000 0 0.15803160 0 0
#> size2 0.00000000 0.0000000 1 0.00000000 0 0
#> type1 0.05776713 0.1580316 0 1.00000000 0 0
#> type2 0.00000000 0.0000000 0 0.00000000 1 0
#> type3 0.00000000 0.0000000 0 0.00000000 0 1
#>
#> $model
#> ~cost + size + type
#> <environment: 0x562049b4c410>
#>
#Get just one attribute
get_attribute(designcoffee,"model.matrix")
#> (Intercept) cost size1 size2 type1 type2 type3
#> [1,] 1 -1 -0.7071068 1.224745 -0.5773503 1.6329932 0.000000
#> [2,] 1 1 1.4142136 0.000000 -0.5773503 -0.8164966 -1.414214
#> [3,] 1 1 -0.7071068 -1.224745 -0.5773503 -0.8164966 1.414214
#> [4,] 1 -1 -0.7071068 -1.224745 -0.5773503 1.6329932 0.000000
#> [5,] 1 -1 1.4142136 0.000000 1.7320508 0.0000000 0.000000
#> [6,] 1 -1 1.4142136 0.000000 -0.5773503 -0.8164966 -1.414214
#> [7,] 1 -1 -0.7071068 -1.224745 -0.5773503 -0.8164966 -1.414214
#> [8,] 1 1 -0.7071068 1.224745 -0.5773503 -0.8164966 -1.414214
#> [9,] 1 -1 -0.7071068 -1.224745 1.7320508 0.0000000 0.000000
#> [10,] 1 1 1.4142136 0.000000 -0.5773503 -0.8164966 -1.414214
#> [11,] 1 1 -0.7071068 -1.224745 1.7320508 0.0000000 0.000000
#> [12,] 1 -1 1.4142136 0.000000 -0.5773503 1.6329932 0.000000
#> [13,] 1 -1 -0.7071068 -1.224745 -0.5773503 -0.8164966 1.414214
#> [14,] 1 1 -0.7071068 -1.224745 1.7320508 0.0000000 0.000000
#> [15,] 1 1 1.4142136 0.000000 -0.5773503 1.6329932 0.000000
#> [16,] 1 1 1.4142136 0.000000 -0.5773503 -0.8164966 1.414214
#> [17,] 1 1 -0.7071068 1.224745 1.7320508 0.0000000 0.000000
#> [18,] 1 -1 -0.7071068 1.224745 -0.5773503 -0.8164966 -1.414214
#> [19,] 1 1 1.4142136 0.000000 -0.5773503 -0.8164966 1.414214
#> [20,] 1 -1 -0.7071068 1.224745 1.7320508 0.0000000 0.000000
#> [21,] 1 -1 1.4142136 0.000000 1.7320508 0.0000000 0.000000
#> [22,] 1 -1 -0.7071068 1.224745 -0.5773503 -0.8164966 1.414214
#> [23,] 1 1 1.4142136 0.000000 -0.5773503 1.6329932 0.000000
#> [24,] 1 1 -0.7071068 1.224745 -0.5773503 -0.8164966 1.414214
#> [25,] 1 1 -0.7071068 -1.224745 -0.5773503 1.6329932 0.000000
#> [26,] 1 -1 1.4142136 0.000000 -0.5773503 -0.8164966 1.414214
#> [27,] 1 1 -0.7071068 1.224745 1.7320508 0.0000000 0.000000
#> [28,] 1 1 -0.7071068 1.224745 -0.5773503 1.6329932 0.000000
#> [29,] 1 1 -0.7071068 -1.224745 -0.5773503 -0.8164966 -1.414214
# Extract from `eval_design()` output
power_output = eval_design(designcoffee, model = ~cost + size + type,
alpha = 0.05, detailedoutput = TRUE)
get_attribute(power_output,"correlation.matrix")
#> cost size1 size2 type1 type2 type3
#> cost 1.00000000 0.00997807 0.01772574 0.02159185 0.03064609 0.05386824
#> size1 0.00997807 1.00000000 0.04386345 0.02576107 0.03656362 0.06426979
#> size2 0.01772574 0.04386345 1.00000000 0.04576376 0.06495416 0.11417333
#> type1 0.02159185 0.02576107 0.04576376 1.00000000 0.01022654 0.01797572
#> type2 0.03064609 0.03656362 0.06495416 0.01022654 1.00000000 0.02551359
#> type3 0.05386824 0.06426979 0.11417333 0.01797572 0.02551359 1.00000000