Returns a list of optimality values (or one value in particular).
Note: The choice of contrast will effect the `G` efficiency value, and `gen_design()` and `eval_design()` by default set different contrasts (`contr.simplex` vs `contr.sum`).
Arguments
- output
The output of either gen_design or eval_design/eval_design_mc.
- optimality
Default `NULL`. Return just the specific optimality requested.
- calc_g
Default `FALSE`. Whether to calculate the g-efficiency.
Value
A dataframe of optimality conditions. `D`, `A`, and `G` are efficiencies (value is out of 100). `T` is the trace of the information matrix, `E` is the minimum eigenvalue of the information matrix, `I` is the average prediction variance, and `Alias` is the trace of the alias matrix.
Examples
# We can extract the optimality 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_optimality(designcoffee)
#> D I A G T E Alias
#> 1 99.09996 0.2224214 98.21557 Not Computed 203 22.79472 6.519097
#Get just one attribute
get_optimality(designcoffee,"D")
#> D
#> 1 99.09996
# Extract from `eval_design()` output
power_output = eval_design(designcoffee, model = ~cost + size + type,
alpha = 0.05, detailedoutput = TRUE)
get_optimality(power_output)
#> D I A G T E Alias
#> 1 99.09996 0.2224214 98.21557 98.09173 203 22.79472 19.72452