Simulate choices for a given survey

Simulate choices for a given survey data frame, either randomly or according to a utility model defined by user-provided parameters.

simulateChoices(survey, obsID = "obsID", pars = NULL, numDraws = 100)

Arguments

survey	A survey data frame exported from the makeSurvey() function.
obsID	The name of the column that identifies each choice observation. Defaults to "obsID".
pars	A list of one or more parameters separated by commas that define the "true" utility model used to simulate choices for the survey data frame. If no parameters are included, choices will be randomly assigned. Defaults to NULL.
numDraws	The number of Halton draws to use for simulated choices based on mixed logit models. Defaults to 100.

Value

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

Examples

library(conjointTools)

# Define the attributes and levels
levels <- list(
  price     = seq(1, 4, 0.5), # $ per pound
  type      = c('Fuji', 'Gala', 'Honeycrisp', 'Pink Lady', 'Red Delicious'),
  freshness = c('Excellent', 'Average', 'Poor')
)

# Make a full-factorial design of experiment
doe <- makeDoe(levels)

# Re-code levels
doe <- recodeDoe(doe, levels)

# Make the conjoint survey by randomly sampling from the doe
survey <- makeSurvey(
  doe       = doe,  # Design of experiment
  nResp     = 2000, # Total number of respondents (upper bound)
  nAltsPerQ = 3,    # Number of alternatives per question
  nQPerResp = 6     # Number of questions per respondent
)

# Simulate random choices for the survey
data_random <- simulateChoices(
    survey = survey,
    obsID  = "obsID"
)

# Simulate choices based on a utility model with the following parameters:
#   - 1 continuous "price" parameter
#   - 4 discrete parameters for "type"
#   - 2 discrete parameters for "freshness"
data_mnl <- simulateChoices(
    survey = survey,
    obsID  = "obsID",
    pars = list(
        price     = 0.1,
        type      = c(0.1, 0.2, 0.3, 0.4),
        freshness = c(0.1, -0.1))
)

# Simulate choices based on a utility model with the following parameters:
#   - 1 continuous "price" parameter
#   - 4 discrete parameters for "type"
#   - 2 random normal discrete parameters for "freshness"
#   - 2 interaction parameters between "price" and "freshness"
data_mxl <- simulateChoices(
    survey = survey,
    obsID  = "obsID",
    pars = list(
        price     = 0.1,
        type      = c(0.1, 0.2, 0.3, 0.4),
        freshness = randN(mu = c(0.1, -0.1), sigma = c(1, 2)),
        `price*freshness` = c(1, 2))
)