cbc_design {cbcTools} | R Documentation |
Make a choice-based conjoint survey design
Description
This function creates a data frame containing a choice-based conjoint survey design where each row is an alternative. Generate a variety of survey designs, including full factorial designs, orthogonal designs, and Bayesian D-efficient designs as well as designs with "no choice" options and "labeled" (also known as "alternative specific") designs.
Usage
cbc_design(
profiles,
n_resp,
n_alts,
n_q,
n_blocks = 1,
n_draws = 50,
n_start = 5,
no_choice = FALSE,
label = NULL,
method = "random",
priors = NULL,
prior_no_choice = NULL,
probs = FALSE,
keep_d_eff = FALSE,
keep_db_error = FALSE,
max_iter = 50,
parallel = FALSE
)
Arguments
profiles |
A data frame in which each row is a possible profile. This
can be generated using the |
n_resp |
Number of survey respondents. |
n_alts |
Number of alternatives per choice question. |
n_q |
Number of questions per respondent. |
n_blocks |
Number of blocks used in Orthogonal or Bayesian D-efficient
designs. Max allowable is one block per respondent. Defaults to |
n_draws |
Number of draws used in simulating the prior distribution used
in Bayesian D-efficient designs. Defaults to |
n_start |
A numeric value indicating the number of random start designs
to use in obtaining a Bayesian D-efficient design. The default is |
no_choice |
Include a "no choice" option in the choice sets? Defaults to
|
label |
The name of the variable to use in a "labeled" design (also
called an "alternative-specific design") such that each set of alternatives
contains one of each of the levels in the |
method |
Choose the design method to use: |
priors |
A list of one or more assumed prior parameters used to generate
a Bayesian D-efficient design. Defaults to |
prior_no_choice |
Prior utility value for the "no choice" alternative.
Only required if |
probs |
If |
keep_d_eff |
If |
keep_db_error |
If |
max_iter |
A numeric value indicating the maximum number allowed iterations when searching for a Bayesian D-efficient design. The default is 50. |
parallel |
Logical value indicating whether computations should be done
over multiple cores. The default is |
Details
The method
argument determines the design method used. Options
are:
-
"random"
-
"full"
-
"orthogonal"
-
"dopt"
-
"CEA"
-
"Modfed"
All methods ensure that the two following criteria are met:
No two profiles are the same within any one choice set.
No two choice sets are the same within any one respondent.
The table below summarizes method compatibility with other design options, including the ability to include a "no choice" option, the creation of a "labeled" design (also called a "alternative-specific" design), the use of restricted profile, and the use of blocking.
Method Include "no choice"? Labeled designs? Restricted profiles? Blocking? "random"
Yes Yes Yes No "full"
Yes Yes Yes Yes "orthogonal"
Yes No No Yes "dopt"
Yes No Yes Yes "CEA"
Yes No No Yes "Modfed"
Yes No Yes Yes The
"random"
method (the default) creates a design where choice sets are created by randomly sampling from the full set ofprofiles
*with *replacement. This means that few (if any) respondents will see the same sets of choice sets. This method is less efficient than other approaches and may lead to a deficient experiment in smaller sample sizes, though it guarantees equal ability to estimate main and interaction effects.The
"full"
method for ("full factorial") creates a design where choice sets are created by randomly sampling from the full set ofprofiles
without replacement. The choice sets are then repeated to meet the desired number of survey respondents (determined byn_resp
). If blocking is used, choice set blocks are created using mutually exclusive subsets ofprofiles
within each block. This method produces a design with similar performance with that of the"random"
method, except the choice sets are repeated and thus there will be many more opportunities for different respondents to see the same choice sets. This method is less efficient than other approaches and may lead to a deficient experiment in smaller sample sizes, though it guarantees equal ability to estimate main and interaction effects. For more information about blocking with full factorial designs, see?DoE.base::fac.design
as well as the JSS article on the DoE.base package (Grömping, 2018).The
"orthogonal"
method creates a design where an orthogonal array from the full set ofprofiles
is found and then choice sets are created by randomly sampling from this orthogonal array without replacement. The choice sets are then repeated to meet the desired number of survey respondents (determined byn_resp
). If blocking is used, choice set blocks are created using mutually exclusive subsets of the orthogonal array within each block. For cases where an orthogonal array cannot be found, a full factorial design is used. This approach is also sometimes called a "main effects" design since orthogonal arrays focus the information on the main effects at the expense of information about interaction effects. For more information about orthogonal designs, see?DoE.base::oa.design
as well as the JSS article on the DoE.base package (Grömping, 2018).The
"dopt"
method creates a "D-optimal" design where an array fromprofiles
is found that maximizes the D-efficiency of a linear model using the Federov algorithm, with the total number of unique choice sets determined byn_q*n_blocks
. Choice sets are then created by randomly sampling from this array without replacement. The choice sets are then repeated to meet the desired number of survey respondents (determined byn_resp
). If blocking is used, choice set blocks are created from the D-optimal array. For more information about the underlying algorithm for this method, see?AlgDesign::optFederov
.The
"CEA"
and"Modfed"
methods use the specifiedpriors
to create a Bayesian D-efficient design for the choice sets, with the total number of unique choice sets determined byn_q*n_blocks
. The choice sets are then repeated to meet the desired number of survey respondents (determined byn_resp
). If"CEA"
or"Modfed"
is used without specifyingpriors
, a prior of all0
s will be used and a warning message stating this will be shown. In the opposite case, ifpriors
are specified but neither Bayesian method is used, the"CEA"
method will be used and a warning stating this will be shown. Restricted sets ofprofiles
can only be used with"Modfed"
. For more details on Bayesian D-efficient designs, see?idefix::CEA
and?idefix::Modfed
as well as the JSS article on the idefix package (Traets et al, 2020).
Value
The returned design
data frame contains a choice-based conjoint
survey design where each row is an alternative. It includes the following
columns:
-
profileID
: Identifies the profile inprofiles
. -
respID
: Identifies each survey respondent. -
qID
: Identifies the choice question answered by the respondent. -
altID
:Identifies the alternative in any one choice observation. -
obsID
: Identifies each unique choice observation across all respondents. -
blockID
: If blocking is used, identifies each unique block.
References
Grömping, U. (2018). R Package DoE.base for Factorial Experiments. Journal of Statistical Software, 85(5), 1–41 doi:10.18637/jss.v085.i05
Traets, F., Sanchez, D. G., & Vandebroek, M. (2020). Generating Optimal Designs for Discrete Choice Experiments in R: The idefix Package. Journal of Statistical Software, 96(3), 1–41, doi:10.18637/jss.v096.i03
Wheeler B (2022)._AlgDesign: Algorithmic Experimental Design. R package version 1.2.1, https://CRAN.R-project.org/package=AlgDesign.
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 by randomly sampling from all possible profiles
# (This is the default setting where method = 'random')
design_random <- cbc_design(
profiles = profiles,
n_resp = 100, # Number of respondents
n_alts = 3, # Number of alternatives per question
n_q = 6 # Number of questions per respondent
)
# Make a survey using a full factorial design and include a "no choice" option
design_full <- cbc_design(
profiles = profiles,
n_resp = 100, # Number of respondents
n_alts = 3, # Number of alternatives per question
n_q = 6, # Number of questions per respondent
method = 'full', # Change this to use a different method, e.g. 'orthogonal', or 'dopt'
no_choice = TRUE
)
# Make a survey by randomly sampling from all possible profiles
# with each level of the "type" attribute appearing as an alternative
design_random_labeled <- cbc_design(
profiles = profiles,
n_resp = 100, # Number of respondents
n_alts = 3, # Number of alternatives per question
n_q = 6, # Number of questions per respondent
label = "type"
)
# Make a Bayesian D-efficient design with a prior model specified
# Note that by speed can be improved by setting parallel = TRUE
design_bayesian <- cbc_design(
profiles = profiles,
n_resp = 100, # Number of respondents
n_alts = 3, # Number of alternatives per question
n_q = 6, # Number of questions per respondent
n_start = 1, # Defaults to 5, set to 1 here for a quick example
priors = list(
price = -0.1,
type = c(0.1, 0.2),
freshness = c(0.1, 0.2)
),
method = "CEA",
parallel = FALSE
)