| Lma.design {support.CEs} | R Documentation |
Creating a choice experiment design using the L^MA method
Description
This function creates a choice experiment design according to the L^MA method.
Usage
Lma.design(candidate.array = NULL, attribute.names,
nalternatives, nblocks, row.renames = TRUE,
seed = NULL)
## S3 method for class 'cedes'
print(x, ...)
Arguments
candidate.array |
A data frame containing an array created by the user. Normally, when this function is used, this argument does not need to be set by the user. |
attribute.names |
A list of the names of attributes and levels. |
nalternatives |
An integer value describing the number of alternatives per choice set, excluding an opt-out alternative such as a "none of these" or a common base alternative. |
nblocks |
An integer value describing the number of blocks into which a choice experiment design is divided. |
row.renames |
A logical variable describing whether or not the row names of a choice experiment design created by this function are changed. When its value is |
seed |
Seed for a random number generator. |
x |
An object of S3 class "ceds." |
... |
Arguments passed to the function |
Details
The L^MA method directly creates a choice experiment design from an orthogonal main-effect array (Johnson et al. 2007). In this method, an orthogonal main-effect array with 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.
Value
This function returns an object of S3 class "cedes" that is a list with the following components.
alternatives |
A list of objects, |
candidate |
A candidate array used for creating a choice experiment design, which is generated using the function |
design.information |
Information related to the choice experiment design created by this function, which is used as arguments in post-processing functions, such as the functions |
Messages are frequently shown immediately after executing this function when it works properly. These messages are taken from the function oa.design and may be valuable to a user who wishes to define the original array and assign it the argument candidate.array.
Author(s)
Hideo Aizaki
See Also
rotation.design, syn.res2, oa.design
Examples
# See the second and third cases in "Example"
# for the function make.dataset.