dce_efficiency {ExpertChoice} R Documentation

## Efficiency Measures for Discrete Choice Experiments

### Description

Efficiency Measures for Discrete Choice Experiments

### Usage

dce_efficiency(augmented_full_factorial, choice_sets)


### Arguments

 augmented_full_factorial The level augmented full factorial. See tutorial step 2. choice_sets A list of choice sets generated by one of the methods used to convert from fractional factorial designs.

### Value

a list of named output.

### References

Street, D.J., Burgess, L. and Louviere, J.J., 2005. Quick and easy choice sets: constructing optimal and nearly optimal stated choice experiments. International Journal of Research in Marketing, 22(4), pp.459-470.

### Examples

# See Step 8 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 (skipped)

# Step 8! -- Inspect the D-efficiency using the Street et. al method of the DCE design.
# NOTE: the af is used at this stage not the ff.
dce_efficiency(af, dce_modulo)


[Package ExpertChoice version 0.2.0 Index]