# NOT RUN {
#See Step 7 of the Practical Introduction to ExpertChoice Vignette.
# Step 1
attrshort = list(condition = c("0", "1", "2"),
technical =c("0", "1", "2"),
provenance = c("0", "1"))
#Step 2
#' # ff stands for "full fatorial"
ff <- full_factorial(attrshort)
af <- augment_levels(ff)
# af stands for "augmented factorial"
# Step 3
# Choose a design type: Federov or Orthogonal. Here an Orthogonal one is used.
nlevels <- unlist(purrr::map(ff, function(x){length(levels(x))}))
fractional_factorial <- DoE.base::oa.design(nlevels = nlevels, columns = "min34")
# Step 4 & 5
# The functional draws out the rows from the original augmented full factorial design.
colnames(fractional_factorial) <- colnames(ff)
fractional <- search_design(ff, fractional_factorial)
# Step 5 - Skipped, but important, see vignette.
# Step 6
# Two modulators c(1,1,1) and c(0,1,1) are specified.
dce_modulo <- modulo_method(
fractional,
list(c(1,1,1),c(0,1,1))
)
# Step 7! -- Check for Pareto dominate solutions
check_overshadow(dce_modulo)
# }
Run the code above in your browser using DataLab