search_design: Search Full Factorial for Fractional Factorial Design

In JedStephens/ExpertChoice: Design of Discrete Choice and Conjoint Analysis

Description

Returns a consistent fractional factorial design from the input fractional factorial design. The key advantage of this function is that it ensures factors are coded and enchances the attributes of the output.

Usage

search_design(full_factorial, fractional_factorial_design)

Arguments

full_factorial	a 'data.table' generated by the 'full_factorial' function
fractional_factorial_design	a means of creating a fractional design using either orthogonal arrays or Federov. See the tutorial for examples.

Value

a 'data.frame' with only the rows of your chosen fractional factorial design.

Examples

# The use of this function depends on what the input to the argument fractional_factorial_design
# will be. See Step 4 of 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! - The search_design function.
# The functional draws out the rows from the original augmented full factorial design.
colnames(fractional_factorial) <- colnames(ff)
fractional <- search_design(ff, fractional_factorial)

JedStephens/ExpertChoice documentation built on April 8, 2020, 2:57 p.m.