Description Usage Arguments Details Value References Examples
Selects the choice set that minimizes the DBerror when added to an initial design, given (updated) parameter values.
1 2 3 
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 DBefficient 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 DBerror of the whole design. 
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  # 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)

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