Simulate choices for a survey design

Simulate choices for a survey design, either randomly or according to a utility model defined by user-provided prior parameters. All choices are simulated using the 'logitr' package. For more details see the JSS article on the 'logitr' package (Helveston, 2023).

cbc_choices(design, obsID = "obsID", priors = NULL, n_draws = 100)

Arguments

design

A data frame of a survey design.

obsID

The name of the column in design that identifies each choice observation. Defaults to "obsID".

priors

A list of one or more prior parameters that define a prior (assumed) utility model used to simulate choices for the survey data frame. If NULL (the default), choices will be randomly assigned.

n_draws

The number of Halton draws to use for simulated choices for mixed logit models. Defaults to 100.

Value

Returns the design data frame with an additional choice column identifying the simulated choices.

References

Helveston, J. P. (2023). logitr: Fast Estimation of Multinomial and Mixed Logit Models with Preference Space and Willingness-to-Pay Space Utility Parameterizations. Journal of Statistical Software, 105(10), 1–37, doi:10.18637/jss.v105.i10

Examples

library(cbcTools)

# A simple conjoint experiment about apples

# Generate all possible profiles
profiles <- cbc_profiles(
  price     = c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5),
  type      = c("Fuji", "Gala", "Honeycrisp"),
  freshness = c('Poor', 'Average', 'Excellent')
)

# Make a survey design from all possible profiles
# (This is the default setting where method = 'full' for "full factorial")
design <- cbc_design(
  profiles = profiles,
  n_resp   = 300, # Number of respondents
  n_alts   = 3,   # Number of alternatives per question
  n_q      = 6    # Number of questions per respondent
)

# Simulate random choices
data <- cbc_choices(
  design = design,
  obsID  = "obsID"
)

# Simulate choices according to a prior utility model
data <- cbc_choices(
  design = design,
  obsID = "obsID",
  priors = list(
    price     = 0.1,
    type      = c(0.1, 0.2),
    freshness = c(0.1, 0.2)
  )
)

# Simulate choices according to a prior model with interactions
data <- cbc_choices(
  design = design,
  obsID = "obsID",
  priors = list(
    price     = 0.1,
    type      = c(0.1, 0.2),
    freshness = c(0.1, 0.2),
    `price*type` = c(0.1, 0.5)
  )
)

# Simulate choices according to a prior utility model with random parameters
data <- cbc_choices(
  design = design,
  obsID = "obsID",
  priors = list(
    price = 0.1,
    type = randN(mean = c(0.1, 0.2), sd = c(1, 2)),
    freshness = c(0.1, 0.2)
  )
)