Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/make.design.matrix.R

This function converts a choice experiment design created by the function `Lma.design`

or `rotation.design`

into a design matrix suitable for a conditional logit model analysis with the function `clogit`

in the package survival, or for a binary choice model analysis with the function `glm`

in the package stats.

1 2 3 4 5 6 7 |

`choice.experiment.design` |
A data frame containing a choice experiment design created by the function |

`optout` |
A logical variable describing whether or not the opt-out alternative is included in the design matrix created by this function. If |

`categorical.attributes` |
A vector containing the names of attributes treated as categorical independent variables in the analysis. |

`continuous.attributes` |
A vector containing the names of attributes treated as continuous independent variables in the analysis. |

`unlabeled` |
A logical variable describing the types of a choice experiment design assigned by the argument |

`common` |
A vector containing a fixed combination of attribute-levels corresponding to a common base option in each question. If there is no common base option, the argument is set as |

`binary` |
When the function is applied to the conditional logit model, the argument is set as |

This function converts a choice experiment design created by the function `Lma.design`

or `rotation.design`

into a design matrix that is suitable for conditional logit model analysis with the function `clogit`

in the package survival or binary choice model analysis with the function `glm`

in the package stats.

A choice experiment design created by the function `Lma.design`

or `rotation.design`

is assigned to the argument `choice.experiment.design`

.

Attributes included in the choice experiment design assigned to the argument `choice.experiment.design`

are classified into categorical and continuous attributes that are assigned to the arguments `categorical.attributes`

and `continuous.attributes`

, respectively. For example, an alternative may have three attributes such as X, Y, and Z. In the conditional logit model analysis, when attributes X and Y are treated as categorical variables and attribute Z is treated as continuous variable, the arguments are set as follows:

`categorical.attributes = c("X", "Y")`

`continuous.attributes = c("Z")`

The categorical variables are created in dummy-variable format. In other words, the minimum value in a categorical attribute is normalized; as a result, each dummy variable is assigned a value of 1 when the categorical attribute takes on a value other than the minimum value. The dummy variables are referred to by their levels. For example, in a categorical attribute X with the three levels of "x1," "x2," and "x3," the dummy variables are created as follows: (1) When the choice experiment design is unlabeled, two dummy variables are created: a dummy variable x2 that assumes a value of 1 when attribute X takes "x2," and 0 otherwise; and a dummy variable x3 that assumes a value of 1 when attribute X takes "x3," and 0 otherwise. (2) When the choice experiment design is labeled and the design contains two alternatives ("alternative 1" and "alternative 2"), excluding an opt-out alternative, four dummy variables are created: a dummy variable x21 that assumes a value of 1 when attribute X in alternative 1 takes "x2," and 0 otherwise; a dummy variable x22 that assumes a value of 1 when attribute X in alternative 2 takes "x2," and 0 otherwise; a dummy variable x31 that assumes a value of 1 when attribute X in alternative 1 takes "x3," and 0 otherwise; and a dummy variable x32 that assumes a value of 1 when attribute X in alternative 2 takes "x3," and 0 otherwise.

Two points should be noted with regard to continuous and categorical variables in the function `make.design.matrix`

. First, the level of the argument `continuous.attributes`

must take on numerical values: that is, the level must not contain a unit of the attribute, such as "USD," "kg," or "km." For example, when the argument `continuous.attributes`

is set as `c("Z")`

and it shows the price attribute of a product alternative, the variable Z must not contain the levels `USD10`

, `USD20`

, and `USD30`

but must have the levels of `10`

, `20`

, and `30`

, respectively. Second, categorical variables created by the function are not in factor format. R usually treats categorical variables as factors. However, values of attribute variables in each row corresponding to an opt-out option must be set as zero (`0`

) because the systematic component of the utility for the opt-out option is normalized to zero. Therefore, the function `make.design.matrix`

converts categorical attributes into dummy variables (the same treatment is applied to the function `make.dataset`

).

The argument `unlabeled`

is set as `TRUE`

when the choice experiment design assigned to the argument `choice.experiment.design`

is unlabeled; it is `FALSE`

otherwise (when the choice experiment design is labeled).

The argument `optout`

is set as `TRUE`

when an opt-out alternative such as the option "none of these" is included in the choice experiment questions. It is set as `FALSE`

when the opt-out alternative is not included.

When a common base option, which is also known as the constant comparator, is included in each choice set, a combination of attribute-levels corresponding to the common base is assigned to the argument `common`

. For example, when the common base is an alternative in which attribute X takes "x1," attribute Y takes "y2" and attriute Z "10," the argument is set as follows:

`common = c(X = "x1", Y = "y2", Z = "10")`

The argument `common`

is set as `NULL`

when the common base option is not included. It is noted that levels of categorical attributes in the common base option are limited to those that are used in the design assigned to the argument `choice.experiment.design`

.

When this function is used for constructing a design matrix for the function `clogit`

, the argument `binary`

is set as `FALSE`

(default); the argument is set as `TRUE`

when the function is applied to binary choice models with the function `glm`

.

This function provides a design matrix that can be directly assigned to an argument `design.matrix`

in the function `make.dataset`

. The design matrix contains categorical and/or continuous variables created by this function as well as the following four kinds of variables.

`BLOCK` |
An integer variable describing the serial number of blocks. |

`QES` |
An integer variable describing the serial number of questions according to the value of the variable |

`ALT` |
An integer variable describing the serial number of alternatives according to the value of the variable |

`ASC` |
Alternative specific constant(s). When the choice experiment design is labeled, the serial number of alternatives ( |

Hideo Aizaki

`Lma.design`

, `rotation.design`

, `make.dataset`

, `syn.res1`

, `syn.res2`

, `syn.res3`

, `clogit`

, `glm`

1 | ```
# See "Examples" for the function make.dataset.
``` |

```
Loading required package: DoE.base
Loading required package: grid
Loading required package: conf.design
Attaching package: 'DoE.base'
The following objects are masked from 'package:stats':
aov, lm
The following object is masked from 'package:graphics':
plot.design
The following object is masked from 'package:base':
lengths
Loading required package: MASS
Loading required package: simex
Loading required package: RCurl
Loading required package: bitops
Loading required package: XML
```

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