| Title: | Efficient Designs for Discrete Choice Experiments |
|---|---|
| Description: | Generates efficient designs for discrete choice experiments based on the multinomial logit model, and individually adapted designs for the mixed multinomial logit model. The generated designs can be presented on screen and choice data can be gathered using a shiny application. Traets F, Sanchez G, and Vandebroek M (2020) <doi:10.18637/jss.v096.i03>. |
| Authors: | Frits Traets [aut, cre], Daniel Gil [ctb] |
| Maintainer: | Frits Traets <[email protected]> |
| License: | GPL-3 |
| Version: | 1.0.3 |
| Built: | 2024-11-28 06:38:23 UTC |
| Source: | CRAN |
Generates efficient designs for discrete choice experiments based on the Multinomial Logit (MNL) model, and individually adapted designs for the Mixed Multinomial Logit model. The (adaptive) designs can be presented on screen and choice data can be gathered using a shiny application.
Maintainer: Frits Traets [email protected]
Other contributors:
Daniel Gil [email protected] [contributor]
Traets F, Sanchez G, Vandebroek M (2020). “Generating Optimal Designs for Discrete Choice Experiments in R: The idefix Package.” Journal of Statistical Software, 96(3).
To generate efficient designs using the Modified Federov algorithm, please consult the Modfed documentation.
To generate efficient designs using the Coordinate Exchange algorithm, please consult the CEA documentation.
To generate adaptive designs using the Modified Fedorov algorithm, please consult the SeqMOD documentation.
To generate adaptive designs using the Coordinate Exchange algorithm, please consult the SeqCEA documentation.
To generate a discrete choice survey on screen, please consult the SurveyApp documentation.
Useful links:
The dataset contains fictional data for seven participants, each responding to eight choice sets with two alternatives. Each alternative consists of three attributes, and each attribute contains three levels, which are dummy coded.
data(aggregate_design)data(aggregate_design)
A matrix with 112 rows and 9 variables
The algorithm improves an initial start design by considering changes on an attribute-by-attribute basis. By doing this, it tries to minimize the D(B)-error based on a multinomial logit model. This routine is repeated for multiple starting designs.
CEA( lvls, coding, c.lvls = NULL, n.sets, n.alts, par.draws, alt.cte = NULL, no.choice = FALSE, start.des = NULL, parallel = TRUE, max.iter = Inf, n.start = 12, best = TRUE )CEA( lvls, coding, c.lvls = NULL, n.sets, n.alts, par.draws, alt.cte = NULL, no.choice = FALSE, start.des = NULL, parallel = TRUE, max.iter = Inf, n.start = 12, best = TRUE )
lvls |
A numeric vector which contains for each attribute the number of levels. |
coding |
Type of coding that needs to be used for each attribute. |
c.lvls |
A list containing numeric vectors with the attribute levels for
each continuous attribute. The default is |
n.sets |
Numeric value indicating the number of choice sets. |
n.alts |
Numeric value indicating the number of alternatives per choice set. |
par.draws |
A matrix or a list, depending on |
alt.cte |
A binary vector indicating for each alternative whether an
alternative specific constant is desired. The default is |
no.choice |
A logical value indicating whether a no choice alternative
should be added to each choice set. The default is |
start.des |
A list containing one or more matrices corresponding to
initial start design(s). The default is |
parallel |
Logical value indicating whether computations should be done
over multiple cores. The default is |
max.iter |
A numeric value indicating the maximum number allowed
iterations. The default is |
n.start |
A numeric value indicating the number of random start designs to use. The default is 12. |
best |
A logical value indicating whether only the best design should be
returned. The default is |
Each iteration will loop through all profiles from the initial design,
evaluating the change in D(B)-error for every level in each attribute.
The algorithm stops when an iteration occured without replacing a profile or
when max.iter is reached.
By specifying a numeric vector in par.draws, the D-error will be
calculated and the design will be optimised locally. By specifying a matrix,
in which each row is a draw from a multivariate distribution, the DB-error
will be calculated, and the design will be optimised globally. Whenever there
are alternative specific constants, par.draws should be a list
containing two matrices: The first matrix containing the parameter draws for
the alternative specific constant parameters. The second matrix containing
the draws for the rest of the parameters.
The DB-error is calculated by taking the mean over D-errors. It could be that
for some draws the design results in an infinite D-error. The percentage of
draws for which this was true for the final design can be found in the output
inf.error.
Alternative specific constants can be specified in alt.cte. The length
of this binary vector should equal n.alts, were 0 indicates the
absence of an alternative specific constant and 1 the opposite.
start.des is a list with one or several matrices corresponding to
initial start design(s). In each matrix each row is a profile. The number of rows
equals n.sets * n.alts, and the
number of columns equals the number of columns of the design matrix + the
number of non-zero elements in alt.cte. Consider that for a
categorical attribute with p levels, there are p - 1 columns in the design
matrix, whereas for a continuous attribute there is only one column. If
start.des = NULL, n.start random initial designs will be
generated. If start designs are provided, n.start is ignored.
If no.choice is TRUE, in each choice set an alternative with
one alternative specific constant is added. The return value of the
D(B)-error is however based on the design without the no choice option.
When parallel is TRUE, detectCores will
be used to decide upon the number of available cores. That number minus 1
cores will be used to search for efficient designs. The computation time will
decrease significantly when parallel = TRUE.
If best = TRUE the design with the lowest D(B)-error is returned.
If best = FALSE, the results of all (provided) start designs are
returned.
design |
A numeric matrix wich contains an efficient design. |
error |
Numeric value indicating the D(B)-error of the design. |
inf.error |
Numeric
value indicating the percentage of draws for which the D-error was
|
probs |
Numeric matrix containing the probabilities of
each alternative in each choice set. If a sample matrix was provided in
|
# DB-efficient designs # 3 Attributes, all dummy coded. 1 alternative specific constant = 7 parameters mu <- c(1.2, 0.8, 0.2, -0.3, -1.2, 1.6, 2.2) # Prior parameter vector v <- diag(length(mu)) # Prior variance. set.seed(123) pd <- MASS::mvrnorm(n = 10, mu = mu, Sigma = v) # 10 draws. p.d <- list(matrix(pd[,1], ncol = 1), pd[,2:7]) CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), par.draws = p.d, n.alts = 2, n.sets = 8, parallel = FALSE, alt.cte = c(0, 1)) # DB-efficient design with categorical and continuous factors # 2 categorical attributes with 4 and 2 levels (effect coded) and 1 # continuous attribute (= 5 parameters) mu <- c(0.5, 0.8, 0.2, 0.4, 0.3) v <- diag(length(mu)) # Prior variance. set.seed(123) pd <- MASS::mvrnorm(n = 3, mu = mu, Sigma = v) # 10 draws. CEA(lvls = c(4, 2, 3), coding = c("E", "E", "C"), par.draws = pd, c.lvls = list(c(2, 4, 6)), n.alts = 2, n.sets = 6, parallel = FALSE) # DB-efficient design with start design provided. # 3 Attributes with 3 levels, all dummy coded (= 6 parameters). mu <- c(0.8, 0.2, -0.3, -0.2, 0.7, 0.4) v <- diag(length(mu)) # Prior variance. sd <- list(example_design) set.seed(123) ps <- MASS::mvrnorm(n = 10, mu = mu, Sigma = v) # 10 draws. CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), par.draws = ps, n.alts = 2, n.sets = 8, parallel = FALSE, start.des = sd)# DB-efficient designs # 3 Attributes, all dummy coded. 1 alternative specific constant = 7 parameters mu <- c(1.2, 0.8, 0.2, -0.3, -1.2, 1.6, 2.2) # Prior parameter vector v <- diag(length(mu)) # Prior variance. set.seed(123) pd <- MASS::mvrnorm(n = 10, mu = mu, Sigma = v) # 10 draws. p.d <- list(matrix(pd[,1], ncol = 1), pd[,2:7]) CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), par.draws = p.d, n.alts = 2, n.sets = 8, parallel = FALSE, alt.cte = c(0, 1)) # DB-efficient design with categorical and continuous factors # 2 categorical attributes with 4 and 2 levels (effect coded) and 1 # continuous attribute (= 5 parameters) mu <- c(0.5, 0.8, 0.2, 0.4, 0.3) v <- diag(length(mu)) # Prior variance. set.seed(123) pd <- MASS::mvrnorm(n = 3, mu = mu, Sigma = v) # 10 draws. CEA(lvls = c(4, 2, 3), coding = c("E", "E", "C"), par.draws = pd, c.lvls = list(c(2, 4, 6)), n.alts = 2, n.sets = 6, parallel = FALSE) # DB-efficient design with start design provided. # 3 Attributes with 3 levels, all dummy coded (= 6 parameters). mu <- c(0.8, 0.2, -0.3, -0.2, 0.7, 0.4) v <- diag(length(mu)) # Prior variance. sd <- list(example_design) set.seed(123) ps <- MASS::mvrnorm(n = 10, mu = mu, Sigma = v) # 10 draws. CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), par.draws = ps, n.alts = 2, n.sets = 8, parallel = FALSE, start.des = sd)
Transforms the data into the desired data format required by different estimation packages.
Datatrans(pkg, des, y, n.alts, n.sets, n.resp, bin, alt.names = NULL)Datatrans(pkg, des, y, n.alts, n.sets, n.resp, bin, alt.names = NULL)
pkg |
Indicates the desired estimation package. Options are
|
des |
A design matrix in which each row is a profile. |
y |
A numeric vector containing binary or discrete responses. See |
n.alts |
Numeric value indicating the number of alternatives per choice set. |
n.sets |
Numeric value indicating the number of choice sets. |
n.resp |
Numeric value indicating the number of respondents. |
bin |
Logical value indicating whether the reponse vector contains
binary data ( |
alt.names |
A character vector containing the names of the alternatives.
The default = |
The design (des) specified should be the full aggregated design.
Thus, if all participants responded to the same design, des will be a repetition of
that design matrix.
The responses in y should be successive when there are multiple
respondents. There can be n.sets elements for each respondent with
discrete values indicating the chosen alternative for each set. Or there can
be n.sets * n.alts elements for each respondent with binary values
indicating for each alternative whether it was chosen or not. In the latter
case the bin argument should be TRUE.
n.sets indicates the number of sets each respondent responded to. It is
assumed that every responded responded to the same number of choice sets.
The data ready to be used by the specified package.
idefix.data <- aggregate_design des <- as.matrix(idefix.data[, 3:8], ncol = 6) y <- idefix.data[, 9] bayesm.data <- Datatrans(pkg = "bayesm", des = des, y = y, n.alts = 2, n.sets = 8, n.resp = 7, bin = TRUE) Mix.pro.data <- Datatrans(pkg = "Mixed.Probit", des = des, y = y, n.alts = 2, n.sets = 8, n.resp = 7, bin = TRUE) mlogit.data <- Datatrans(pkg = "mlogit", des = des, y = y, n.alts = 2, n.sets = 8, n.resp = 7, bin = TRUE)idefix.data <- aggregate_design des <- as.matrix(idefix.data[, 3:8], ncol = 6) y <- idefix.data[, 9] bayesm.data <- Datatrans(pkg = "bayesm", des = des, y = y, n.alts = 2, n.sets = 8, n.resp = 7, bin = TRUE) Mix.pro.data <- Datatrans(pkg = "Mixed.Probit", des = des, y = y, n.alts = 2, n.sets = 8, n.resp = 7, bin = TRUE) mlogit.data <- Datatrans(pkg = "mlogit", des = des, y = y, n.alts = 2, n.sets = 8, n.resp = 7, bin = TRUE)
Function to calculate the DB-error given a design, and parameter values.
DBerr(par.draws, des, n.alts, weights = NULL, mean = TRUE)DBerr(par.draws, des, n.alts, weights = NULL, mean = TRUE)
par.draws |
Numeric matrix in which each row is a draw from a multivariate parameter distribution. |
des |
A design matrix in which each row is an alternative. |
n.alts |
Numeric value indicating the number of alternatives per choice set. |
weights |
A numeric vector containing weights of |
mean |
A logical value indicating whether the mean (DB) error should be returned or not. Default = TRUE. |
Numeric value indicating the DB-error of the design given the parameter draws.
des <- example_design mu = c(-1, -1.5, -1, -1.5, 0.5, 1) Sigma = diag(length(mu)) par.draws <- MASS::mvrnorm(100, mu = mu, Sigma = Sigma) n.alts = 2 DBerr(par.draws = par.draws, des = des, n.alts = n.alts) mu = c(-0.5, -1, -0.5, -1, 0.5, 1) Sigma = diag(length(mu)) par.draws <- MASS::mvrnorm(100, mu = mu, Sigma = Sigma) DBerr(par.draws = par.draws, des = des, n.alts = n.alts)des <- example_design mu = c(-1, -1.5, -1, -1.5, 0.5, 1) Sigma = diag(length(mu)) par.draws <- MASS::mvrnorm(100, mu = mu, Sigma = Sigma) n.alts = 2 DBerr(par.draws = par.draws, des = des, n.alts = n.alts) mu = c(-0.5, -1, -0.5, -1, 0.5, 1) Sigma = diag(length(mu)) par.draws <- MASS::mvrnorm(100, mu = mu, Sigma = Sigma) DBerr(par.draws = par.draws, des = des, n.alts = n.alts)
Transforms a coded design matrix into a design containing character attribute levels, ready to be used in a survey. The frequency of each attribute level in the design is also included in the output.
Decode( des, n.alts, lvl.names, coding, alt.cte = NULL, c.lvls = NULL, no.choice = NULL )Decode( des, n.alts, lvl.names, coding, alt.cte = NULL, c.lvls = NULL, no.choice = NULL )
des |
A numeric matrix which represents the design matrix. Each row is a profile. |
n.alts |
Numeric value indicating the number of alternatives per choice set. |
lvl.names |
A list containing character vectors with the values of each level of each attribute. |
coding |
A character vector denoting the type of coding used for each
attribute. See also |
alt.cte |
A binary vector indicating for each alternative if an
alternative specific constant is present. The default is |
c.lvls |
A list containing numeric vectors with the attribute levels for
each continuous attribute. The default is |
no.choice |
An integer indicating the no choice alternative. The default
is |
des A design matrix, this can also be a single choice set. See for
example the output of Modfed or CEA.
In lvl.names, the number of character vectors in the list should equal
the number of attributes in de choice set. The number of elements in each
character vector should equal the number of levels for that attribute.
Valid arguments for coding are C, D and E. When
using C the attribute will be treated as continuous and no coding will
be applied. All possible levels of that attribute should then be specified in
c.lvls. If D (dummy coding) is used
contr.treatment will be applied to that attribute. The first
attribute wil be used as reference level. For E (effect coding)
contr.sum is applied, in this case the last attribute level is
used as reference level.
If des contains columns for alternative specific constants,
alt.cte should be specified. In this case, the first column(s) (equal
to the number of nonzero elements in alt.cte) will be removed from
des before decoding the alternatives.
design |
A character matrix which represents the design. |
lvl.balance |
A list containing the frequency of appearance of each attribute level in the design. |
## Not run: # Example without continuous attributes. design <- example_design coded <- c("D", "D", "D") # Coding. # Levels as they should appear in survey. al <- list( c("$50", "$75", "$100"), # Levels attribute 1. c("2 min", "15 min", "30 min"), # Levels attribute 2. c("bad", "moderate", "good") # Levels attribute 3. ) # Decode Decode(des = design, n.alts = 2, lvl.names = al, coding = coded) # Example with alternative specific constants design <- example_design2 coded <- c("D", "D", "D") # Coding. # Levels as they should appear in survey. al <- list( c("$50", "$75", "$100"), # Levels attribute 1. c("2 min", "15 min", "30 min"), # Levels attribute 2. c("bad", "moderate", "good") # Levels attribute 3. ) # Decode Decode(des = design, n.alts = 3, lvl.names = al, coding = coded, alt.cte = c(1, 1, 0)) ## End(Not run)## Not run: # Example without continuous attributes. design <- example_design coded <- c("D", "D", "D") # Coding. # Levels as they should appear in survey. al <- list( c("$50", "$75", "$100"), # Levels attribute 1. c("2 min", "15 min", "30 min"), # Levels attribute 2. c("bad", "moderate", "good") # Levels attribute 3. ) # Decode Decode(des = design, n.alts = 2, lvl.names = al, coding = coded) # Example with alternative specific constants design <- example_design2 coded <- c("D", "D", "D") # Coding. # Levels as they should appear in survey. al <- list( c("$50", "$75", "$100"), # Levels attribute 1. c("2 min", "15 min", "30 min"), # Levels attribute 2. c("bad", "moderate", "good") # Levels attribute 3. ) # Decode Decode(des = design, n.alts = 3, lvl.names = al, coding = coded, alt.cte = c(1, 1, 0)) ## End(Not run)
This discrete choice design is generated using the Modfed
function. There are 8 choice sets, each containig 2 alternatives (rows). The
alternatives consist of 3 attributes (time, price, comfort) with 3
levels each, all of which are dummy coded (columns).
data(example_design)data(example_design)
A matrix with 16 rows and 6 columns.
This discrete choice design is generated using the Modfed
function. There are 8 choice sets, each containig 3 alternatives (rows). The
alternatives consist of 3 attributes (time, price, comfort) with 3
levels each, all of which are dummy coded (columns). The first two colums are
alternative specific constants for alternative 1 and 2.
data(example_design2)data(example_design2)
A matrix with 24 rows and 8 columns.
This function samples from the posterior distribution using importance sampling, assuming a multivariate (truncated) normal prior distribution and a MNL likelihood.
ImpsampMNL( n.draws, prior.mean, prior.covar, des, n.alts, y, alt.cte = NULL, lower = NULL, upper = NULL )ImpsampMNL( n.draws, prior.mean, prior.covar, des, n.alts, y, alt.cte = NULL, lower = NULL, upper = NULL )
n.draws |
Numeric value indicating the number of draws. |
prior.mean |
Numeric vector indicating the mean of the multivariate normal distribution (prior). |
prior.covar |
Covariance matrix of the prior distribution. |
des |
A design matrix in which each row is a profile. If alternative
specific constants are present, those should be included as the first
column(s) of the design. Can be generated with |
n.alts |
Numeric value indicating the number of alternatives per choice set. |
y |
A binary response vector. |
alt.cte |
A binary vector indicating for each alternative whether an
alternative specific constant is desired. The default is |
lower |
Numeric vector of lower truncation points, the default
is |
upper |
Numeric vector of upper truncation points, the default
is |
For the proposal distribution a t-distribution with degrees of freedom equal
to the number of parameters is used. The posterior mode is estimated using
optim, and the covariance matrix is calculated as the negative
inverse of the generalized Fisher information matrix. See reference for more
information.
From this distribution a lattice grid of draws is generated.
If truncation is present, incorrect draws are rejected and new ones are
generated untill n.draws is reached. The covariance matrix is in this case
still calculated as if no truncation was present.
sample |
Numeric vector with the (unweigthted) draws from the posterior distribution. |
weights |
Numeric vector with the associated weights of the draws. |
max |
Numeric vector with the estimated mode of the posterior distribution. |
covar |
Matrix representing the estimated variance covariance matrix. |
Yu J, Goos P, Vandebroek M (2011). “Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity.” https://www.sciencedirect.com/science/article/pii/S0167811611000668.
## Example 1: sample from posterior, no constraints, no alternative specific constants # choice design design <- example_design # Respons. truePar <- c(0.7, 0.6, 0.5, -0.5, -0.7, 1.7) # some values set.seed(123) resp <- RespondMNL(par = truePar, des = design, n.alts = 2) #prior pm <- c(1, 1, 1, -1, -1, 1) # mean vector pc <- diag(1, ncol(design)) # covariance matrix # draws from posterior. ImpsampMNL(n.draws = 100, prior.mean = pm, prior.covar = pc, des = design, n.alts = 2, y = resp) ## example 2: sample from posterior with constraints # and alternative specific constants # choice design. design <- example_design2 # Respons. truePar <- c(0.2, 0.8, 0.7, 0.6, 0.5, -0.5, -0.7, 1.7) # some values set.seed(123) resp <- RespondMNL(par = truePar, des = design, n.alts = 3) # prior pm <- c(1, 1, 1, 1, 1, -1, -1, 1) # mean vector pc <- diag(1, ncol(design)) # covariance matrix low = c(-Inf, -Inf, 0, 0, 0, -Inf, -Inf, 0) up = c(Inf, Inf, Inf, Inf, Inf, 0, 0, Inf) # draws from posterior. ImpsampMNL(n.draws = 100, prior.mean = pm, prior.covar = pc, des = design, n.alts = 3, y = resp, lower = low, upper = up, alt.cte = c(1, 1, 0))## Example 1: sample from posterior, no constraints, no alternative specific constants # choice design design <- example_design # Respons. truePar <- c(0.7, 0.6, 0.5, -0.5, -0.7, 1.7) # some values set.seed(123) resp <- RespondMNL(par = truePar, des = design, n.alts = 2) #prior pm <- c(1, 1, 1, -1, -1, 1) # mean vector pc <- diag(1, ncol(design)) # covariance matrix # draws from posterior. ImpsampMNL(n.draws = 100, prior.mean = pm, prior.covar = pc, des = design, n.alts = 2, y = resp) ## example 2: sample from posterior with constraints # and alternative specific constants # choice design. design <- example_design2 # Respons. truePar <- c(0.2, 0.8, 0.7, 0.6, 0.5, -0.5, -0.7, 1.7) # some values set.seed(123) resp <- RespondMNL(par = truePar, des = design, n.alts = 3) # prior pm <- c(1, 1, 1, 1, 1, -1, -1, 1) # mean vector pc <- diag(1, ncol(design)) # covariance matrix low = c(-Inf, -Inf, 0, 0, 0, -Inf, -Inf, 0) up = c(Inf, Inf, Inf, Inf, Inf, 0, 0, Inf) # draws from posterior. ImpsampMNL(n.draws = 100, prior.mean = pm, prior.covar = pc, des = design, n.alts = 3, y = resp, lower = low, upper = up, alt.cte = c(1, 1, 0))
Reads all individual choice data files, created by SurveyApp
function, from a directory and concatenates
those files into a single data file. Files containing either "num" or "char"
will be read, with num indicating numeric data and char indicating character
data. For more information, see output of SurveyApp.
LoadData(data.dir, type)LoadData(data.dir, type)
data.dir |
A character string containing the directory to read from. |
type |
Character vector containing either num or char. |
A data frame containg the full design and all the responses of the combined data files that were found. Different files are indicated by an ID variable.
The algorithm swaps every profile of an initial start design with candidate profiles. By doing this, it tries to minimize the D(B)-error, based on a multinomial logit model. This routine is repeated for multiple starting designs.
Modfed( cand.set, n.sets, n.alts, par.draws, alt.cte = NULL, no.choice = FALSE, start.des = NULL, parallel = TRUE, max.iter = Inf, n.start = 12, best = TRUE )Modfed( cand.set, n.sets, n.alts, par.draws, alt.cte = NULL, no.choice = FALSE, start.des = NULL, parallel = TRUE, max.iter = Inf, n.start = 12, best = TRUE )
cand.set |
A numeric matrix in which each row is a possible profile. The
|
n.sets |
Numeric value indicating the number of choice sets. |
n.alts |
Numeric value indicating the number of alternatives per choice set. |
par.draws |
A matrix or a list, depending on |
alt.cte |
A binary vector indicating for each alternative whether an
alternative specific constant is desired. The default is |
no.choice |
A logical value indicating whether a no choice alternative
should be added to each choice set. The default is |
start.des |
A list containing one or more matrices corresponding to initial start design(s). The default is |
parallel |
Logical value indicating whether computations should be done
over multiple cores. The default is |
max.iter |
A numeric value indicating the maximum number allowed
iterations. The default is |
n.start |
A numeric value indicating the number of random start designs to use. The default is 12. |
best |
A logical value indicating whether only the best design should be
returned. The default is |
Each iteration will loop through all profiles from the initial design,
evaluating the change in D(B)-error for every profile from cand.set.
The algorithm stops when an iteration occured without replacing a profile or
when max.iter is reached.
By specifying a numeric vector in par.draws, the D-error will be
calculated and the design will be optimised locally. By specifying a matrix,
in which each row is a draw from a multivariate distribution, the DB-error
will be calculated, and the design will be optimised globally. Whenever there
are alternative specific constants, par.draws should be a list
containing two matrices: The first matrix containing the parameter draws for
the alternative specific constant parameters. The second matrix containing
the draws for the rest of the parameters.
The DB-error is calculated by taking the mean over D-errors. It could be that
for some draws the design results in an infinite D-error. The percentage of
draws for which this was true for the final design can be found in the output
inf.error.
Alternative specific constants can be specified in alt.cte. The length
of this binary vector should equal n.alts, were 0 indicates the
absence of an alternative specific constant and 1 the opposite.
start.des is a list with one or several matrices corresponding to
initial start design(s). In each matrix each
row is a profile. The number of rows equals n.sets * n.alts, and the
number of columns equals the number of columns of cand.set + the
number of non-zero elements in alt.cte. If start.des
= NULL, n.start random initial designs will be
generated. If start designs are provided, n.start is ignored.
If no.choice is TRUE, in each choice set an alternative with
one alternative specific constant is added. The return value of the
D(B)-error is however based on the design without the no choice option.
When parallel is TRUE, detectCores will
be used to decide upon the number of available cores. That number minus 1
cores will be used to search for efficient designs. The computation time will
decrease significantly when parallel = TRUE.
If best = TRUE the design with the lowest D(B)-error is returned.
If best = FALSE, the results of all (provided) start designs are
returned.
design |
A numeric matrix wich contains an efficient design. |
error |
Numeric value indicating the D(B)-error of the design. |
inf.error |
Numeric
value indicating the percentage of draws for which the D-error was
|
probs |
Numeric matrix containing the probabilities of
each alternative in each choice set. If a sample matrix was provided in
|
Traets F, Sanchez G, Vandebroek M (2020). “Generating Optimal Designs for Discrete Choice Experiments in R: The idefix Package.” Journal of Statistical Software, 96(3).
## Not run: # DB-efficient designs # 3 Attributes, all dummy coded. 1 alternative specific constant = 7 parameters cand.set <- Profiles(lvls = c(3, 3, 3), coding = c("D", "D", "D")) mu <- c(0.5, 0.8, 0.2, -0.3, -1.2, 1.6, 2.2) # Prior parameter vector v <- diag(length(mu)) # Prior variance. set.seed(123) pd <- MASS::mvrnorm(n = 10, mu = mu, Sigma = v) # 10 draws. p.d <- list(matrix(pd[,1], ncol = 1), pd[,2:7]) Modfed(cand.set = cand.set, n.sets = 8, n.alts = 2, alt.cte = c(1, 0), parallel = FALSE, par.draws = p.d, best = FALSE) # DB-efficient design with start design provided. # 3 Attributes with 3 levels, all dummy coded (= 6 parameters). cand.set <- Profiles(lvls = c(3, 3, 3), coding = c("D", "D", "D")) mu <- c(0.8, 0.2, -0.3, -0.2, 0.7, 0.4) # Prior mean (total = 5 parameters). v <- diag(length(mu)) # Prior variance. sd <- list(example_design) set.seed(123) ps <- MASS::mvrnorm(n = 10, mu = mu, Sigma = v) # 10 draws. Modfed(cand.set = cand.set, n.sets = 8, n.alts = 2, alt.cte = c(0, 0), parallel = FALSE, par.draws = ps, start.des = sd) ## End(Not run)## Not run: # DB-efficient designs # 3 Attributes, all dummy coded. 1 alternative specific constant = 7 parameters cand.set <- Profiles(lvls = c(3, 3, 3), coding = c("D", "D", "D")) mu <- c(0.5, 0.8, 0.2, -0.3, -1.2, 1.6, 2.2) # Prior parameter vector v <- diag(length(mu)) # Prior variance. set.seed(123) pd <- MASS::mvrnorm(n = 10, mu = mu, Sigma = v) # 10 draws. p.d <- list(matrix(pd[,1], ncol = 1), pd[,2:7]) Modfed(cand.set = cand.set, n.sets = 8, n.alts = 2, alt.cte = c(1, 0), parallel = FALSE, par.draws = p.d, best = FALSE) # DB-efficient design with start design provided. # 3 Attributes with 3 levels, all dummy coded (= 6 parameters). cand.set <- Profiles(lvls = c(3, 3, 3), coding = c("D", "D", "D")) mu <- c(0.8, 0.2, -0.3, -0.2, 0.7, 0.4) # Prior mean (total = 5 parameters). v <- diag(length(mu)) # Prior variance. sd <- list(example_design) set.seed(123) ps <- MASS::mvrnorm(n = 10, mu = mu, Sigma = v) # 10 draws. Modfed(cand.set = cand.set, n.sets = 8, n.alts = 2, alt.cte = c(0, 0), parallel = FALSE, par.draws = ps, start.des = sd) ## End(Not run)
This discrete choice design is generated using the Modfed
function. There are 8 choice sets, each containig 3 alternatives (rows), of
which one is a no choice option. The no choice option consist of an
alternative specific constant and zero's for all other attribute levels. There
are three attributes (time, price, comfort) with 3 levels each, all of which
are dummy coded (columns).
data(nochoice_design)data(nochoice_design)
A matrix with 24 rows and 7 variables
Function to generate all possible combinations of attribute levels (i.e. all possible profiles).
Profiles(lvls, coding, c.lvls = NULL)Profiles(lvls, coding, c.lvls = NULL)
lvls |
A numeric vector which contains for each attribute the number of levels. |
coding |
Type of coding that needs to be used for each attribute. |
c.lvls |
A list containing numeric vectors with the attribute levels for
each continuous attribute. The default is |
Valid arguments for coding are C, D and E. When
using C the attribute will be treated as continuous and no coding will
be applied. All possible levels should then be specified in c.lvls. If
D (dummy coding) is used contr.treatment will be applied
to that attribute. For E (effect coding) contr.sum will
be applied.
A numeric matrix which contains all possible profiles.
# Without continuous attributes at.lvls <- c(3, 4, 2) # 3 Attributes with respectively 3, 4 and 2 levels. c.type <- c("E", "E", "E") # All Effect coded. Profiles(lvls = at.lvls, coding = c.type) # Generate profiles. # With continuous attributes at.lvls <- c(3, 4, 2) # 3 attributes with respectively 3, 4 and 2 levels. # First attribute is dummy coded, second and third are continuous. c.type <- c("D", "C", "C") # Levels for continuous attributes, in the same order. con.lvls <- list(c(4, 6, 8, 10), c(7, 9)) Profiles(lvls = at.lvls, coding = c.type, c.lvls = con.lvls)# Without continuous attributes at.lvls <- c(3, 4, 2) # 3 Attributes with respectively 3, 4 and 2 levels. c.type <- c("E", "E", "E") # All Effect coded. Profiles(lvls = at.lvls, coding = c.type) # Generate profiles. # With continuous attributes at.lvls <- c(3, 4, 2) # 3 attributes with respectively 3, 4 and 2 levels. # First attribute is dummy coded, second and third are continuous. c.type <- c("D", "C", "C") # Levels for continuous attributes, in the same order. con.lvls <- list(c(4, 6, 8, 10), c(7, 9)) Profiles(lvls = at.lvls, coding = c.type, c.lvls = con.lvls)
Function to generate random responses given parameter values and a design matrix, assuming a MNL model.
RespondMNL(par, des, n.alts, bin = TRUE)RespondMNL(par, des, n.alts, bin = TRUE)
par |
Numeric vector containing parameter values. |
des |
A design matrix in which each row is a profile. If alternative
specific constants are present, those should be included as the first
column(s) of the design. Can be generated with |
n.alts |
Numeric value indicating the number of alternatives per choice set. |
bin |
A logical value indicating whether the returned value should be a binary vector or a discrete value which denotes the chosen alternative. |
Numeric vector indicating the chosen alternatives.
# design: 3 dummy coded attributes, each 3 levels. There are 8 choice sets. des <- example_design set.seed(123) true_par <- rnorm(6) RespondMNL(par = true_par, des = des, n.alts = 2)# design: 3 dummy coded attributes, each 3 levels. There are 8 choice sets. des <- example_design set.seed(123) true_par <- rnorm(6) RespondMNL(par = true_par, des = des, n.alts = 2)
Selects the choice set that minimizes the DB-error when added to an initial design, given (updated) parameter values.
SeqCEA( des = NULL, lvls, coding, c.lvls = NULL, n.alts, par.draws, prior.covar, alt.cte = NULL, no.choice = NULL, weights = NULL, parallel = TRUE, reduce = TRUE, n.cs = NULL )SeqCEA( des = NULL, lvls, coding, c.lvls = NULL, n.alts, par.draws, prior.covar, alt.cte = NULL, no.choice = NULL, weights = NULL, parallel = TRUE, reduce = TRUE, n.cs = NULL )
des |
A design matrix in which each row is a profile. If alternative
specific constants are present, those should be included as the first
column(s) of the design. Can be generated with |
lvls |
A numeric vector which contains for each attribute the number of levels. |
coding |
Type of coding that needs to be used for each attribute. |
c.lvls |
A list containing numeric vectors with the attribute levels for
each continuous attribute. The default is |
n.alts |
Numeric value indicating the number of alternatives per choice set. |
par.draws |
A matrix or a list, depending on |
prior.covar |
Covariance matrix of the prior distribution. |
alt.cte |
A binary vector indicating for each alternative whether an
alternative specific constant is desired. The default is |
no.choice |
An integer indicating the no choice alternative. The default
is |
weights |
A vector containing the weights of the draws. Default is
|
parallel |
Logical value indicating whether computations should be done over multiple cores. |
reduce |
Logical value indicating whether the candidate set should be reduced or not. |
n.cs |
An integer indicating the number of possible random choice sets to
consider in the search for the next best choice set possible. The default is
|
This algorithm is ideally used in an adaptive context. The algorithm will select the next DB-efficient choice set given parameter values and possible previously generated choice sets. In an adaptive context these parameter values are updated after each observed response.
Previously generated choice sets, which together form an initial design, can
be provided in des. When no design is provided, the algorithm will
select the most efficient choice set based on the fisher information of the
prior covariance matrix prior.covar.
If alt.cte = NULL, par.draws should be a matrix in which each
row is a sample from the multivariate parameter distribution. In case that
alt.cte is not NULL, a list containing two matrices should be
provided to par.draws. The first matrix containing the parameter draws
for the alternative specific parameters. The second matrix containing the
draws for the rest of the parameters.
The list of potential choice sets is created by selecting randomly a level for
each attribute in an alternative/profile. n.cs controls the number of
potential choice sets to consider. The default is
NULL, which means that the number of possible choice sets is the product of
attribute levels considered in the experiment. For instance, an experiment
with 3 attribute and 3 levels each will consider 3^3 = 27 possible choice sets.
The weights argument can be used when the par.draws have
weights. This is for example the case when parameter values are updated using
ImpsampMNL.
When parallel is TRUE, detectCores will
be used to decide upon the number of available cores. That number minus 1
cores will be used to search for the optimal choice set. For small problems
(6 parameters), parallel = TRUE can be slower. For larger problems the
computation time will decrease significantly.
Note: this function is faster than SeqMOD, but
the output is not as stable. This happens because this function
makes a random search to get the choice set, whereas
SeqMOD makes an exhaustive search.
set |
A matrix representing a DB efficient choice set. |
error |
A numeric value indicating the DB-error of the whole design. |
Traets F, Sanchez G, Vandebroek M (2020). “Generating Optimal Designs for Discrete Choice Experiments in R: The idefix Package.” Journal of Statistical Software, 96(3).
Yu J, Goos P, Vandebroek M (2011). “Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity.” https://www.sciencedirect.com/science/article/pii/S0167811611000668.
Meyer RK, Nachtsheim CJ (1995). “The Coordinate-Exchange Algorithm for Constructing Exact Optimal Experimental Designs.” Technometrics, 37(1), 60–69. ISSN 00401706, https://www.jstor.org/stable/1269153.
Kessels R, Jones B, Goos P, Vandebroek M (2009). “An Efficient Algorithm for Constructing Bayesian Optimal Choice Designs.” Journal of Business & Economic Statistics, 27(2), 279–291. ISSN 07350015.
# DB efficient choice set, given a design and parameter draws. # 3 attributes with 3 levels each m <- c(0.3, 0.2, -0.3, -0.2, 1.1, 2.4) # mean (total = 6 parameters). pc <- diag(length(m)) # covariance matrix set.seed(123) sample <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # Initial design. des <- example_design # Efficient choice set to add. SeqCEA(des = des, lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.alts = 2, par.draws = sample, prior.covar = pc, parallel = FALSE) # DB efficient choice set, given parameter draws. # with alternative specific constants des <- example_design2 ac <- c(1, 1, 0) # Alternative specific constants. m <- c(0.3, 0.2, -0.3, -0.2, 1.1, 2.4, 1.8, 1.2) # mean pc <- diag(length(m)) # covariance matrix pos <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) sample <- list(pos[ , 1:2], pos[ , 3:8]) # Efficient choice set. SeqCEA(des = des, lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.alts = 3, par.draws = sample, alt.cte = ac, prior.covar = pc, parallel = FALSE)# DB efficient choice set, given a design and parameter draws. # 3 attributes with 3 levels each m <- c(0.3, 0.2, -0.3, -0.2, 1.1, 2.4) # mean (total = 6 parameters). pc <- diag(length(m)) # covariance matrix set.seed(123) sample <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # Initial design. des <- example_design # Efficient choice set to add. SeqCEA(des = des, lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.alts = 2, par.draws = sample, prior.covar = pc, parallel = FALSE) # DB efficient choice set, given parameter draws. # with alternative specific constants des <- example_design2 ac <- c(1, 1, 0) # Alternative specific constants. m <- c(0.3, 0.2, -0.3, -0.2, 1.1, 2.4, 1.8, 1.2) # mean pc <- diag(length(m)) # covariance matrix pos <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) sample <- list(pos[ , 1:2], pos[ , 3:8]) # Efficient choice set. SeqCEA(des = des, lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.alts = 3, par.draws = sample, alt.cte = ac, prior.covar = pc, parallel = FALSE)
Selects the choice set that maximizes the Kullback-Leibler divergence between the prior parameter values and the expected posterior, assuming a MNL model.
SeqKL( des = NULL, cand.set, n.alts, par.draws, alt.cte = NULL, no.choice = NULL, weights = NULL, allow.rep = FALSE )SeqKL( des = NULL, cand.set, n.alts, par.draws, alt.cte = NULL, no.choice = NULL, weights = NULL, allow.rep = FALSE )
des |
A design matrix in which each row is a profile. If alternative
specific constants are present, those should be included as the first
column(s) of the design. Can be generated with |
cand.set |
A numeric matrix in which each row is a possible profile. The
|
n.alts |
Numeric value indicating the number of alternatives per choice set. |
par.draws |
A matrix or a list, depending on |
alt.cte |
A binary vector indicating for each alternative if an alternative specific constant is desired. |
no.choice |
An integer indicating the no choice alternative. The default
is |
weights |
A vector containing the weights of the draws. Default is
|
allow.rep |
Logical value indicating whether repeated choice sets are allowed in the design. |
This algorithm is ideally used in an adaptive context. The algorithm selects the choice set that maximizes the Kullback-Leibler divergence between prior and expected posterior. Otherwisely framed the algorithm selects the choice set that maximizes the expected information gain.
If alt.cte = NULL, par.draws should be a matrix in which each
row is a sample from the multivariate parameter distribution. In case that
alt.cte is not NULL, a list containing two matrices should be
provided to par.draws. The first matrix containing the parameter draws
for the alternative specific parameters. The second matrix containing the
draws for the rest of the parameters.
The list of potential choice sets are created using
combn. The weights argument can be used when the
par.draws have
weights. This is for example the case when parameter values are updated using
ImpsampMNL.
set |
Numeric matrix containing the choice set that maximizes the expected KL divergence. |
kl |
Numeric value which is the Kullback leibler divergence. |
Crabbe M, Akinc D, Vandebroek M (2014). “Fast algorithms to generate individualized designs for the mixed logit choice model.” https://www.sciencedirect.com/science/article/pii/S0191261513002178.
# KL efficient choice set, given parameter draws. # Candidate profiles cs <- Profiles(lvls = c(3, 3), coding = c("E", "E")) m <- c(0.3, 0.2, -0.3, -0.2) # Prior mean (4 parameters). pc <- diag(length(m)) # Prior variance set.seed(123) ps <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # 10 draws. # Efficient choice set to add. SeqKL(cand.set = cs, n.alts = 2, alt.cte = NULL, par.draws = ps, weights = NULL) # KL efficient choice set, given parameter draws. # Candidate profiles cs <- Profiles(lvls = c(3, 3), coding = c("C", "E"), c.lvls = list(c(5,3,1))) m <- c(0.7, 0.3, -0.3, -0.2) # Prior mean (4 parameters). pc <- diag(length(m)) # Prior variance set.seed(123) ps <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # 10 draws. sample <- list(ps[ , 1], ps[ , 2:4]) ac <- c(1, 0) # Alternative specific constant. # Efficient choice set to add. SeqKL(cand.set = cs, n.alts = 2, alt.cte = ac, par.draws = sample, weights = NULL)# KL efficient choice set, given parameter draws. # Candidate profiles cs <- Profiles(lvls = c(3, 3), coding = c("E", "E")) m <- c(0.3, 0.2, -0.3, -0.2) # Prior mean (4 parameters). pc <- diag(length(m)) # Prior variance set.seed(123) ps <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # 10 draws. # Efficient choice set to add. SeqKL(cand.set = cs, n.alts = 2, alt.cte = NULL, par.draws = ps, weights = NULL) # KL efficient choice set, given parameter draws. # Candidate profiles cs <- Profiles(lvls = c(3, 3), coding = c("C", "E"), c.lvls = list(c(5,3,1))) m <- c(0.7, 0.3, -0.3, -0.2) # Prior mean (4 parameters). pc <- diag(length(m)) # Prior variance set.seed(123) ps <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # 10 draws. sample <- list(ps[ , 1], ps[ , 2:4]) ac <- c(1, 0) # Alternative specific constant. # Efficient choice set to add. SeqKL(cand.set = cs, n.alts = 2, alt.cte = ac, par.draws = sample, weights = NULL)
Selects the choice set that minimizes the DB-error when added to an initial design, given (updated) parameter values.
SeqMOD( des = NULL, cand.set, n.alts, par.draws, prior.covar, alt.cte = NULL, no.choice = NULL, weights = NULL, parallel = TRUE, reduce = TRUE, allow.rep = FALSE )SeqMOD( des = NULL, cand.set, n.alts, par.draws, prior.covar, alt.cte = NULL, no.choice = NULL, weights = NULL, parallel = TRUE, reduce = TRUE, allow.rep = FALSE )
des |
A design matrix in which each row is a profile. If alternative
specific constants are present, those should be included as the first
column(s) of the design. Can be generated with |
cand.set |
A numeric matrix in which each row is a possible profile. The
|
n.alts |
Numeric value indicating the number of alternatives per choice set. |
par.draws |
A matrix or a list, depending on |
prior.covar |
Covariance matrix of the prior distribution. |
alt.cte |
A binary vector indicating for each alternative whether an
alternative specific constant is desired. The default is |
no.choice |
An integer indicating the no choice alternative. The default
is |
weights |
A vector containing the weights of the draws. Default is
|
parallel |
Logical value indicating whether computations should be done over multiple cores. |
reduce |
Logical value indicating whether the candidate set should be reduced or not. |
allow.rep |
Logical value indicating whether repeated choice sets are allowed in the design. |
This algorithm is ideally used in an adaptive context. The algorithm will select the next DB-efficient choice set given parameter values and possible previously generated choice sets. In an adaptive context these parameter values are updated after each observed response.
Previously generated choice sets, which together form an initial design, can
be provided in des. When no design is provided, the algorithm will
select te most efficient choice set based on the fisher information of the
prior covariance matrix prior.covar.
If alt.cte = NULL, par.draws should be a matrix in which each
row is a sample from the multivariate parameter distribution. In case that
alt.cte is not NULL, a list containing two matrices should be
provided to par.draws. The first matrix containing the parameter draws
for the alternative specific parameters. The second matrix containing the
draws for the rest of the parameters.
The list of potential choice sets are created using
combn. If reduce is TRUE,
allow.rep = FALSE and vice versa. Furthermore, the list of
potential choice sets will be screaned in order to select only those choice
sets with a unique information matrix. If no alternative specific constants are used,
reduce should always be TRUE. When alternative specific
constants are used reduce can be TRUE so that the algorithm
will be faster, but the combinations of constants and profiles will not be
evaluated exhaustively.
The weights argument can be used when the par.draws have
weights. This is for example the case when parameter values are updated using
ImpsampMNL.
When parallel is TRUE, detectCores will
be used to decide upon the number of available cores. That number minus 1
cores will be used to search for the optimal choice set. For small problems
(6 parameters), parallel = TRUE can be slower. For larger problems the
computation time will decrease significantly.
Note: this function is more stable than SeqCEA, but
it takes more time to get the output. This happens because this function
makes an exhaustive search to get the choice set, whereas
SeqCEA makes a random search.
set |
A matrix representing a DB efficient choice set. |
error |
A numeric value indicating the DB-error of the whole design. |
Traets F, Sanchez G, Vandebroek M (2020). “Generating Optimal Designs for Discrete Choice Experiments in R: The idefix Package.” Journal of Statistical Software, 96(3).
Yu J, Goos P, Vandebroek M (2011). “Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity.” https://www.sciencedirect.com/science/article/pii/S0167811611000668.
# DB efficient choice set, given a design and parameter draws. # Candidate profiles cs <- Profiles(lvls = c(3, 3, 3), coding = c("D", "D", "D")) m <- c(0.3, 0.2, -0.3, -0.2, 1.1, 2.4) # mean (total = 6 parameters). pc <- diag(length(m)) # covariance matrix set.seed(123) sample <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # Initial design. des <- example_design # Efficient choice set to add. SeqMOD(des = des, cand.set = cs, n.alts = 2, par.draws = sample, prior.covar = pc, parallel = FALSE) # DB efficient choice set, given parameter draws. # with alternative specific constants des <- example_design2 cs <- Profiles(lvls = c(3, 3, 3), coding = c("D", "D", "D")) ac <- c(1, 1, 0) # Alternative specific constants. m <- c(0.3, 0.2, -0.3, -0.2, 1.1, 2.4, 1.8, 1.2) # mean pc <- diag(length(m)) # covariance matrix pos <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) sample <- list(pos[ , 1:2], pos[ , 3:8]) # Efficient choice set. SeqMOD(des = des, cand.set = cs, n.alts = 3, par.draws = sample, alt.cte = ac, prior.covar = pc, parallel = FALSE)# DB efficient choice set, given a design and parameter draws. # Candidate profiles cs <- Profiles(lvls = c(3, 3, 3), coding = c("D", "D", "D")) m <- c(0.3, 0.2, -0.3, -0.2, 1.1, 2.4) # mean (total = 6 parameters). pc <- diag(length(m)) # covariance matrix set.seed(123) sample <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) # Initial design. des <- example_design # Efficient choice set to add. SeqMOD(des = des, cand.set = cs, n.alts = 2, par.draws = sample, prior.covar = pc, parallel = FALSE) # DB efficient choice set, given parameter draws. # with alternative specific constants des <- example_design2 cs <- Profiles(lvls = c(3, 3, 3), coding = c("D", "D", "D")) ac <- c(1, 1, 0) # Alternative specific constants. m <- c(0.3, 0.2, -0.3, -0.2, 1.1, 2.4, 1.8, 1.2) # mean pc <- diag(length(m)) # covariance matrix pos <- MASS::mvrnorm(n = 10, mu = m, Sigma = pc) sample <- list(pos[ , 1:2], pos[ , 3:8]) # Efficient choice set. SeqMOD(des = des, cand.set = cs, n.alts = 3, par.draws = sample, alt.cte = ac, prior.covar = pc, parallel = FALSE)
This function starts a shiny application which puts choice sets on screen and saves the responses. The complete choice design can be provided in advance, or can be generated sequentially adaptively, or can be a combination of both.
SurveyApp( des = NULL, n.total, alts, atts, lvl.names, coding, alt.cte = NULL, no.choice = NULL, buttons.text, intro.text, end.text, data.dir = NULL, c.lvls = NULL, prior.mean = NULL, prior.covar = NULL, cand.set = NULL, n.draws = NULL, lower = NULL, upper = NULL, parallel = TRUE, reduce = TRUE )SurveyApp( des = NULL, n.total, alts, atts, lvl.names, coding, alt.cte = NULL, no.choice = NULL, buttons.text, intro.text, end.text, data.dir = NULL, c.lvls = NULL, prior.mean = NULL, prior.covar = NULL, cand.set = NULL, n.draws = NULL, lower = NULL, upper = NULL, parallel = TRUE, reduce = TRUE )
des |
A numeric matrix which represents the design matrix. Each row is a profile. |
n.total |
A numeric value indicating the total number of choice sets. |
alts |
A character vector containing the names of the alternatives. |
atts |
A character vector containing the names of the attributes. |
lvl.names |
A list containing character vectors with the values of each level of each attribute. |
coding |
A character vector denoting the type of coding used for each
attribute. See also |
alt.cte |
A binary vector indicating for each alternative if an
alternative specific constant is present. The default is |
no.choice |
An integer indicating which alternative should be a no choice
alternative. The default is |
buttons.text |
A string containing the text presented together with the option buttons. |
intro.text |
A string containing the text presented before the choice survey. |
end.text |
A string containing the text presented after the choice survey. |
data.dir |
A character string with the directory denoting where the data needs to be written. The default is NULL |
c.lvls |
A list containing numeric vectors with the attribute levels for
each continuous attribute. The default is |
prior.mean |
Numeric vector indicating the mean of the multivariate normal distribution (prior). |
prior.covar |
Covariance matrix of the prior distribution. |
cand.set |
A numeric matrix in which each row is a possible profile. The
|
n.draws |
Numeric value indicating the number of draws. |
lower |
Numeric vector of lower truncation points, the default
is |
upper |
Numeric vector of upper truncation points, the default
is |
parallel |
Logical value indicating whether computations should be done
over multiple cores. The default is |
reduce |
Logical value indicating whether the candidate set should be reduced or not. |
A pregenerated design can be specified in des. This should be a matrix
in which each row is a profile. This can be generated with Modfed
or CEA, but it is not necessary.
If n.total = nrow(des) / length(alts), the specified
design will be put on screen, one set after the other, and the responses will
be saved. If n.total > (nrow(des) / length(alts)), first
the specified design will be shown and afterwards the remaining sets will be
generated adaptively. If des = NULL, n.total sets will be
generated adaptively. See SeqMOD for more information on adaptive
choice sets.
Whenever adaptive sets will be generated, prior.mean,
prior.covar, cand.set and n.draws, should be specified.
These arguments are necessary for the underlying importance sampling algorithm
to update the prior preference distribution. lower and upper can
be used to specify lower and upper truncation points. See
ImpsampMNL for more details.
The names specified in alts will be used to label the choice
alternatives. The names specified in atts will be used to name the
attributes in the choice sets. The values of lvl.names will be used to
create the values in the choice sets. See Decode for more
details.
The text specified in buttons.text will be displayed above the buttons
to indicate the preferred choice (for example: "indicate your preferred
choice"). The text specified in intro.text will be displayed before the
choice sets. This will generally be a description of the survey and some
instructions. The text specified in end.text will be displayed after
the survey. This will generally be a thanking note and some further
instructions.
A no choice alternative is coded as an alternative with 1 alternative specific
constant and zero's for all other attribute levels. If a no choice alternative
is present in des, or is desired when generating adaptive choice sets,
no.choice should be specified. This should be done with an integer,
indicating which alternative is the no choice option. This alternative will
not be presented on screen, but the option to select "no choice" will be. The
alt.cte argument should be specified accordingly, namely with a
1 on the location of the no.choice option. See examples for
illustration.
When parallel is TRUE, detectCores will
be used to decide upon the number of available cores. That number minus 1
cores will be used to search for the optimal adaptive choice set. For small problems
(6 parameters), parallel = TRUE can be slower. For larger problems the
computation time will decrease significantly.
When reduce = TRUE, the set of all potential choice sets will be
reduced to choice sets that have a unique information matrix. If no
alternative specific constants are used, reduce should always be
TRUE. When alternative specific constants are used reduce can be
TRUE so that the algorithm will be faster, but the combinations of
constants and profiles will not be evaluated exhaustively.
After completing the survey, two text files can be found in
data.dir. The file with "num" in the filename is a matrix with the
numeric choice data. The coded design matrix ("par"), presented during the
survey, together with the observed responses ("resp") can be found here.
Rownames indicate the setnumbers. The file with "char" in the filename is a
matrix with character choice data. The labeled design matrix ("par"),
presented during the survey, together with the observed responses ("resp")
can be found here. See LoadData to load the data.
Yu J, Goos P, Vandebroek M (2011). “Individually adapted sequential Bayesian conjoint-choice designs in the presence of consumer heterogeneity.” https://www.sciencedirect.com/science/article/pii/S0167811611000668.
## Not run: #### Present choice design without adaptive sets (n.total = sets in des) # example design data("example_design") # pregenerated design xdes <- example_design ### settings of the design code <- c("D", "D", "D") n.sets <- 8 # settings of the survey alternatives <- c("Alternative A", "Alternative B") attributes <- c("Price", "Time", "Comfort") labels <- vector(mode="list", length(attributes)) labels[[1]] <- c("$10", "$5", "$1") labels[[2]] <- c("20 min", "12 min", "3 min") labels[[3]] <- c("bad", "average", "good") i.text <- "Welcome, here are some instructions ... good luck!" b.text <- "Please choose the alternative you prefer" e.text <- "Thanks for taking the survey" dataDir <- getwd() # Display the survey SurveyApp (des = xdes, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text) #### Present choice design with partly adaptive sets (n.total > sets in des) # example design data("example_design") # pregenerated design xdes <- example_design ### settings of the design code <- c("D", "D", "D") n.sets <- 12 # settings of the survey alternatives <- c("Alternative A", "Alternative B") attributes <- c("Price", "Time", "Comfort") labels <- vector(mode="list", length(attributes)) labels[[1]] <- c("$10", "$5", "$1") labels[[2]] <- c("20 min", "12 min", "3 min") labels[[3]] <- c("bad", "average", "good") i.text <- "Welcome, here are some instructions ... good luck!" b.text <- "Please choose the alternative you prefer" e.text <- "Thanks for taking the survey" # setting for adaptive sets levels <- c(3, 3, 3) cand <- Profiles(lvls = levels, coding = code) p.mean <- c(0.3, 0.7, 0.3, 0.7, 0.3, 0.7) p.var <- diag(length(p.mean)) dataDir <- getwd() # Display the survey SurveyApp(des = xdes, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text, prior.mean = p.mean, prior.covar = p.var, cand.set = cand, n.draws = 50) #### Choice design with only adaptive sets (des=NULL) # setting for adaptive sets levels <- c(3, 3, 3) p.mean <- c(0.3, 0.7, 0.3, 0.7, 0.3, 0.7) low = c(-Inf, -Inf, -Inf, 0, 0, -Inf) up = rep(Inf, length(p.mean)) p.var <- diag(length(p.mean)) code <- c("D", "D", "D") cand <- Profiles(lvls = levels, coding = code) n.sets <- 12 # settings of the survey alternatives <- c("Alternative A", "Alternative B") attributes <- c("Price", "Time", "Comfort") labels <- vector(mode="list", length(attributes)) labels[[1]] <- c("$10", "$5", "$1") labels[[2]] <- c("20 min", "12 min", "3 min") labels[[3]] <- c("bad", "average", "good") i.text <- "Welcome, here are some instructions ... good luck!" b.text <- "Please choose the alternative you prefer" e.text <- "Thanks for taking the survey" dataDir <- getwd() # Display the survey SurveyApp(des = NULL, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text, prior.mean = p.mean, prior.covar = p.var, cand.set = cand, lower = low, upper = up, n.draws = 50) # If CEA algorithm is desired, cand.set argument is not needed SurveyApp(des = NULL, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text, prior.mean = p.mean, prior.covar = p.var, lower = low, upper = up, n.draws = 50) #### Present choice design with a no choice alternative. # example design data("nochoice_design") # pregenerated design xdes <- nochoice_design ### settings of the design code <- c("D", "D", "D") n.sets <- 8 # settings of the survey alternatives <- c("Alternative A", "Alternative B", "None") attributes <- c("Price", "Time", "Comfort") labels <- vector(mode = "list", length(attributes)) labels[[1]] <- c("$10", "$5", "$1") labels[[2]] <- c("20 min", "12 min", "3 min") labels[[3]] <- c("bad", "average", "good") i.text <- "Welcome, here are some instructions ... good luck!" b.text <- "Please choose the alternative you prefer" e.text <- "Thanks for taking the survey" # Display the survey SurveyApp(des = xdes, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text, no.choice = 3, alt.cte = c(0, 0, 1)) ## End(Not run)## Not run: #### Present choice design without adaptive sets (n.total = sets in des) # example design data("example_design") # pregenerated design xdes <- example_design ### settings of the design code <- c("D", "D", "D") n.sets <- 8 # settings of the survey alternatives <- c("Alternative A", "Alternative B") attributes <- c("Price", "Time", "Comfort") labels <- vector(mode="list", length(attributes)) labels[[1]] <- c("$10", "$5", "$1") labels[[2]] <- c("20 min", "12 min", "3 min") labels[[3]] <- c("bad", "average", "good") i.text <- "Welcome, here are some instructions ... good luck!" b.text <- "Please choose the alternative you prefer" e.text <- "Thanks for taking the survey" dataDir <- getwd() # Display the survey SurveyApp (des = xdes, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text) #### Present choice design with partly adaptive sets (n.total > sets in des) # example design data("example_design") # pregenerated design xdes <- example_design ### settings of the design code <- c("D", "D", "D") n.sets <- 12 # settings of the survey alternatives <- c("Alternative A", "Alternative B") attributes <- c("Price", "Time", "Comfort") labels <- vector(mode="list", length(attributes)) labels[[1]] <- c("$10", "$5", "$1") labels[[2]] <- c("20 min", "12 min", "3 min") labels[[3]] <- c("bad", "average", "good") i.text <- "Welcome, here are some instructions ... good luck!" b.text <- "Please choose the alternative you prefer" e.text <- "Thanks for taking the survey" # setting for adaptive sets levels <- c(3, 3, 3) cand <- Profiles(lvls = levels, coding = code) p.mean <- c(0.3, 0.7, 0.3, 0.7, 0.3, 0.7) p.var <- diag(length(p.mean)) dataDir <- getwd() # Display the survey SurveyApp(des = xdes, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text, prior.mean = p.mean, prior.covar = p.var, cand.set = cand, n.draws = 50) #### Choice design with only adaptive sets (des=NULL) # setting for adaptive sets levels <- c(3, 3, 3) p.mean <- c(0.3, 0.7, 0.3, 0.7, 0.3, 0.7) low = c(-Inf, -Inf, -Inf, 0, 0, -Inf) up = rep(Inf, length(p.mean)) p.var <- diag(length(p.mean)) code <- c("D", "D", "D") cand <- Profiles(lvls = levels, coding = code) n.sets <- 12 # settings of the survey alternatives <- c("Alternative A", "Alternative B") attributes <- c("Price", "Time", "Comfort") labels <- vector(mode="list", length(attributes)) labels[[1]] <- c("$10", "$5", "$1") labels[[2]] <- c("20 min", "12 min", "3 min") labels[[3]] <- c("bad", "average", "good") i.text <- "Welcome, here are some instructions ... good luck!" b.text <- "Please choose the alternative you prefer" e.text <- "Thanks for taking the survey" dataDir <- getwd() # Display the survey SurveyApp(des = NULL, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text, prior.mean = p.mean, prior.covar = p.var, cand.set = cand, lower = low, upper = up, n.draws = 50) # If CEA algorithm is desired, cand.set argument is not needed SurveyApp(des = NULL, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text, prior.mean = p.mean, prior.covar = p.var, lower = low, upper = up, n.draws = 50) #### Present choice design with a no choice alternative. # example design data("nochoice_design") # pregenerated design xdes <- nochoice_design ### settings of the design code <- c("D", "D", "D") n.sets <- 8 # settings of the survey alternatives <- c("Alternative A", "Alternative B", "None") attributes <- c("Price", "Time", "Comfort") labels <- vector(mode = "list", length(attributes)) labels[[1]] <- c("$10", "$5", "$1") labels[[2]] <- c("20 min", "12 min", "3 min") labels[[3]] <- c("bad", "average", "good") i.text <- "Welcome, here are some instructions ... good luck!" b.text <- "Please choose the alternative you prefer" e.text <- "Thanks for taking the survey" # Display the survey SurveyApp(des = xdes, n.total = n.sets, alts = alternatives, atts = attributes, lvl.names = labels, coding = code, buttons.text = b.text, intro.text = i.text, end.text = e.text, no.choice = 3, alt.cte = c(0, 0, 1)) ## End(Not run)