Get optimality values — get_optimality • skpr

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).

Usage

get_optimality(output, optimality = NULL, calc_g = FALSE)

Arguments

output: The output of either gen_design(), eval_design(), or 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 NA 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 Not Computed 203 22.79472 19.72452