Generate survey designs for choice experiments (Updated Implementation)

This function creates experimental designs for choice-based conjoint experiments using multiple design approaches including optimization and frequency-based methods.

Usage

cbc_design(
  profiles,
  method = "random",
  priors = NULL,
  n_alts,
  n_q,
  n_resp = 100,
  n_blocks = 1,
  n_cores = NULL,
  no_choice = FALSE,
  label = NULL,
  randomize_questions = TRUE,
  randomize_alts = TRUE,
  remove_dominant = FALSE,
  dominance_types = c("total", "partial"),
  dominance_threshold = 0.8,
  max_dominance_attempts = 50,
  max_iter = 50,
  n_start = 5,
  include_probs = FALSE,
  use_idefix = TRUE
)

Arguments

profiles

A data frame of class cbc_profiles created using cbc_profiles()

method

Choose the design method: "random", "shortcut", "minoverlap", "balanced", "stochastic", "modfed", or "cea". Defaults to "random"

priors

A cbc_priors object created by cbc_priors(), or NULL for random/shortcut designs

n_alts

Number of alternatives per choice question

n_q

Number of questions per respondent (or per block)

n_resp

Number of respondents (for random/shortcut designs) or 1 (for optimized designs that get repeated)

n_blocks

Number of blocks in the design. Defaults to 1

n_cores

Number of cores to use for parallel processing in the design search. Defaults to NULL, in which case it is set to the number of available cores minus 1.

no_choice

Include a "no choice" option? Defaults to FALSE

label

The name of the variable to use in a "labeled" design. Defaults to NULL

randomize_questions

Randomize question order for each respondent? Defaults to TRUE (optimized methods only)

randomize_alts

Randomize alternative order within questions? Defaults to TRUE (optimized methods only)

remove_dominant

Remove choice sets with dominant alternatives? Defaults to FALSE

dominance_types

Types of dominance to check: "total" and/or "partial"

dominance_threshold

Threshold for total dominance detection. Defaults to 0.8

max_dominance_attempts

Maximum attempts to replace dominant choice sets. Defaults to 50.

max_iter

Maximum iterations for optimized designs. Defaults to 50

n_start

Number of random starts for optimized designs. Defaults to 5

include_probs

Include predicted probabilities in resulting design? Requires priors. Defaults to FALSE

use_idefix

If TRUE (the default), the idefix package will be used to find optimal designs, which is faster. Only valid with "cea" and "modfed" methods.

Value

A cbc_design object containing the experimental design

Details

Design Methods

The method argument determines the design approach used:

• "random": Creates designs by randomly sampling profiles for each respondent independently

• "shortcut": Frequency-based greedy algorithm that balances attribute level usage

• "minoverlap": Greedy algorithm that minimizes attribute overlap within choice sets

• "balanced": Greedy algorithm that maximizes overall attribute balance across the design

• "stochastic": Stochastic profile swapping with D-error optimization (first improvement found)

• "modfed": Modified Fedorov algorithm with exhaustive profile swapping for D-error optimization

• "cea": Coordinate Exchange Algorithm with attribute-by-attribute D-error optimization

Method Compatibility

The table below summarizes method compatibility with design features:

Method	No choice?	Labeled designs?	Restricted profiles?	Blocking?	Interactions?	Dominance removal?
"random"	Yes	Yes	Yes	No	Yes	Yes
"shortcut"	Yes	Yes	Yes	No	No	Yes
"minoverlap"	Yes	Yes	Yes	No	No	Yes
"balanced"	Yes	Yes	Yes	No	No	Yes
"stochastic"	Yes	Yes	Yes	Yes	Yes	Yes
"modfed"	Yes	Yes	Yes	Yes	Yes	Yes
"cea"	Yes	Yes	No	Yes	Yes	Yes

Design Quality Assurance

All methods ensure the following criteria are met:

1. No duplicate profiles within any choice set

2. No duplicate choice sets within any respondent

3. If remove_dominant = TRUE, choice sets with dominant alternatives are eliminated (optimization methods only)

Method Details

Random Method

Creates designs where each respondent sees completely independent, randomly generated choice sets.

Greedy Methods (shortcut, minoverlap, balanced)

These methods use frequency-based algorithms that make locally optimal choices:

• Shortcut: Balances attribute level usage within questions and across the overall design

• Minoverlap: Minimizes attribute overlap within choice sets while allowing some overlap for balance

• Balanced: Maximizes overall attribute balance, prioritizing level distribution over overlap reduction

These methods provide good level balance without requiring priors or D-error calculations and offer fast execution suitable for large designs.

D-Error Optimization Methods (stochastic, modfed, cea)

These methods minimize D-error to create statistically efficient designs:

• Stochastic: Random profile sampling with first improvement acceptance

• Modfed: Exhaustive profile testing for best improvement (slower but thorough)

• CEA: Coordinate exchange testing attribute levels individually (requires full factorial profiles)

idefix Integration

When use_idefix = TRUE (the default), the function leverages the highly optimized algorithms from the idefix package for 'cea' and 'modfed' design generation methods. This can provide significant speed improvements, especially for larger problems.

Key benefits of idefix integration:

• Faster optimization algorithms with C++ implementation

• Better handling of large candidate sets

• Optimized parallel processing

• Advanced blocking capabilities for multi-block designs