Lma.design(candidate.array = NULL, attribute.names, nalternatives, nblocks, row.renames = TRUE, seed = NULL)
"print"(x, ...)TRUE (default), integer values are assigned to the row names starting from 1. When its value is FALSE, the row names are the same as those of an array created by the function oa.design (included in the DoE.base package) via the function Lma.design, or those of an array assigned to the argument candidate.array by the user.
print.
alt.j: the jth alternative in each choice set created by this function. Each of alt.j includes a variable BLOCK describing the serial number of blocks, a variable QES describing the serial number of choice experiment questions for each value of the variable BLOCK, a variable ALT describing the serial number of alternatives for each value of the QES variable, and attribute variables corresponding to the argument attribute.names.oa.design in the package DoE.base or which the user sets for the argument candidate.array. When nblocks >= 2, the last column in this value (candidate) shows a factor that is used for blocking.questionnaire and make.design.matrix. This list includes objects such as the number of blocks into which the choice experiment design is divided (nblocks), the number of questions per block (nquestions), the number of alternatives per choice set excluding an opt-out or common base option (nalternatives), and the number of attributes per alternative (nattributes).oa.design and may be valuable to a user who wishes to define the original array and assign it the argument candidate.array.
M times A columns of L level factors is used to create each choice set that contains M alternatives of A attributes with L levels. Each row of the array corresponds to the alternatives of a choice set.This method creates a labeled type choice experiment design that can contain both generic attributes and alternative-specific attributes: the generic attribute refers to that which is included in all the alternatives; the alternative-specific attribute is that which is included in only one alternative. The reader is referred to chapters 3 and 5 of Louviere et al. (2000) for details about the types of attribute---generic or alternative-specific---and the types of choice experiment design---labeled or unlabeled.
When this function is used, the combination of attributes and attribute levels, the number of alternatives per choice set excluding an opt-out or common base option, and the number of blocks are respectively assigned to the arguments.
The combination of attributes and attribute levels are assigned to the argument attribute.names in list format. For example, let's assume that the alternative has three attributes, each of which has three levels: an attribute X with the three levels of x1, x2, and x3; an attribute Y with the three levels of y1, y2, and y3; and an attribute Z with the three levels of 10, 20, and 30. In this case, the argument is set as follows:
attribute.names = list(X = c("x1", "x2", "x3"),
Y = c("y1", "y2", "y3"), Z = c("10", "20", "30"))
The number of alternatives per choice set is defined by the argument nalternatives: the number of alternatives does not include an opt-out option such as a "none of these" or a common base option.
When a large choice experiment design is created (that is, there are numerous choice experiment questions), the respondent may carry a heavy psychological burden in terms of answering the questions: in these cases, the choice experiment design is frequently divided into two or more blocks (subsets) of choice sets (questions), and each respondent is asked to answer one block of questions. The argument nblocks assigns the number of blocks. For example, when the argument nblocks is set to be 3 and the choice experiment design contains 27 individual choice sets (that is, there are 27 choice experiment questions), the choice experiment design is divided into 3 blocks, each of which has 9 individual choice sets (9 choice experiment questions). "Blocking" is performed on the basis of a factor with nblocks levels.
Under default settings, this function uses an orthogonal main-effect array that is automatically produced by the function oa.design in the package DoE.base based on the argument attribute.names to create a choice experiment design. However, when there is no array corresponding to the argument attribute.names, the function oa.design returns a full factorial based on the argument attribute.names (See help for the function oa.design in the packge DoE.base). On the other hand, when this function does not create a choice experiment design matching the user's requirements, the user might achieve it by assigning an arbitrary (user-defined) array to the argument candidate.array: this function uses the array to create a choice experiment design. When the user-defined array is used, the last column of the array must contain a column for dividing the design based on the argument nblocks. The arguments attribute.names and nblocks must also be assigned according to the array.
The function Lma.design can also be used for creating a binary choice experiment design on the basis of an orthogonal main-effect array by setting the argument nalternatives as 1 for a binary choice experiment with an opt-out or common base option, and 2 for a forced-choice format binary choice experiment.
Louviere, J. J., Hensher, D. A. and Swait, J. D. (2000) Stated Choice Methods: Analysis and Application. Cambridge University Press.
Aizaki, H. (2012) Basic Functions for Supporting an Implementation of Choice Experiments in R. Journal of Statistical Software, Code Snippets, 50(2), 1--24. http://www.jstatsoft.org/v50/c02/
rotation.design, syn.res2, oa.design
# See the second and third cases in "Example"
# for the function make.dataset.
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