support.CEs (version 0.7-0)

make.dataset: Making a data set

Description

This function makes a data set used 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.

Usage

make.dataset(respondent.dataset, design.matrix, 
             choice.indicators, detail = FALSE)

Value

In addition to some variables contained in the respondent and design matrix data sets, the data set also contains the following two variables that are used in the functions clogit and glm:

STR

A variable assigned to the argument strata in the function clogit in order to identify each combination of respondent and question.

RES

A logical variable taking on TRUE when the alternative is selected and FALSE when it is not.

Arguments

respondent.dataset

A data frame containing respondents' answers to choice experiment questions.

design.matrix

A data frame containing a design matrix created by the function make.design.matrix in this package.

choice.indicators

A vector of variables showing the alternative of which was selected in each choice experiment question.

detail

A logical variable describing whether or not some variables contained in the argument respondent.dataset and variables created in this function are stored in a data set produced by this function.

Author

Hideo Aizaki

Details

Conditional logit model analyses of responses to choice experiment questions in R can be conducted using the function clogit in the package survival. When the function is used to analyze the responses to the choice experiment questions, a data set in a special format is needed; each alternative should comprise one row of the data set (see the example in Figure 4 in Aizaki and Nishimura 2008). The function make.dataset is able to create such a data set by combining a data set containing information about responses to the choice experiment questions and a data set containing a design matrix related to these questions.

The respondent data set has to be created by the user and is assigned to the argument respondent.dataset. The data set, in which each row shows one respondent, has to contain a variable ID, corresponding to the respondent's identification number; a variable BLOCK, corresponding to the serial number of blocks to which each respondent had been assigned; and response variables, corresponding to the answers to each of the choice experiment questions. If necessary, covariates showing the respondent's individual characteristics such as age and gender are also included in the respondent.dataset. Although the names of the response variables and covariates are discretionary, the names of the respondent's identification number variable and block variable must be ID and BLOCK, respectively.

The names of the response variables are assigned to the argument choice.indicators. For example, when the names of the response variables in the respondent data set are q1, q2, q3, and q4, the argument is set as c("q1", "q2", "q3", "q4"). The response variables show the serial number of the alternative selected by the respondent for each choice experiment question. The method of assigning the serial number of the alternatives must be the same as that of assigning the ALT in the output from the make.design.matrix. In other words, each alternative must be assigned an integer value that starts from 1. In the respondent.dataset, all variables are automatically treated as covariates, except for the variable ID, the variable BLOCK, and the response variables assigned by the argument choice.indicators.

The design matrix data set created by the function make.design.matrix is assigned to the argument design.matrix.

It should be noted that the order of the questions in the respondent.dataset must be the same as that of the variable QES in the design matrix data set that was assigned to the argument design.matrix, if the order of choice experiment questions presented to respondents was randomized.

The function make.dataset can also be used for making a data set suitable for a binary choice model analysis using the function glm in the package stats.

See Also

make.design.matrix, syn.res1, syn.res2, syn.res3, clogit, glm

Examples

library(survival)
library(stats)

if(getRversion() >= "3.6.0") RNGkind(sample.kind = "Rounding")

# Case 1
# Choice experiments using the function rotaion.design.
# See "Details" for the data set syn.res1.

des1 <- rotation.design(
 attribute.names = list(
  Region = c("Reg_A", "Reg_B", "Reg_C"), 
  Eco = c("Conv.", "More", "Most"), 
  Price = c("1", "1.1", "1.2")), 
 nalternatives = 2,
 nblocks = 1,
 row.renames = FALSE, 
 randomize = TRUE,
 seed = 987)
des1
questionnaire(choice.experiment.design = des1)
desmat1 <- make.design.matrix(
 choice.experiment.design = des1, 
 optout = TRUE, 
 categorical.attributes = c("Region", "Eco"), 
 continuous.attributes = c("Price"),
 unlabeled = TRUE)
data(syn.res1)
dataset1 <- make.dataset(
 respondent.dataset = syn.res1, 
 choice.indicators = 
  c("q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "q9"), 
 design.matrix = desmat1)
clogout1 <- clogit(RES ~ ASC + Reg_B + Reg_C + More + Most + 
 More:F + Most:F + Price + strata(STR), data = dataset1)
clogout1
gofm(clogout1)
mwtp(
 output = clogout1,
 monetary.variables = c("Price"), 
 nonmonetary.variables = 
  c("Reg_B", "Reg_C", "More", "Most", "More:F", "Most:F"), 
 seed = 987)

# Case 2
# Choice experiments using the function Lma.design.
# See "Details" for the data set syn.res2.

des2 <- Lma.design(
 attribute.names = list(
  Eco = c("Conv.", "More", "Most"), 
  Price = c("1", "1.1", "1.2")), 
 nalternatives = 3,
 nblocks = 2,
 row.renames = FALSE,
 seed = 987)
des2
questionnaire(choice.experiment.design = des2, quote = FALSE)
desmat2 <- make.design.matrix(
 choice.experiment.design = des2, 
 optout = TRUE, 
 categorical.attributes = c("Eco"), 
 continuous.attributes = c("Price"), 
 unlabeled = FALSE)
data(syn.res2)
dataset2 <- make.dataset(
 respondent.dataset = syn.res2, 
 choice.indicators = 
  c("q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "q9"), 
 design.matrix = desmat2)
clogout2 <- clogit(RES ~ ASC1 + More1 + Most1 + Price1 + 
 ASC2 + More2 + Most2 + Price2 + ASC3 + More3 + Most3 + Price3 + 
 strata(STR), data = dataset2)
clogout2
gofm(clogout2)
mwtp(
 output = clogout2, 
 monetary.variables = c("Price1", "Price2", "Price3"), 
 nonmonetary.variables = list(
  c("More1", "Most1"), c("More2", "Most2"), c("More3", "Most3")), 
 seed = 987)

# Case 3
# Binary choice experiments using the function Lma.design.
# See "Details" for the data set syn.res3.

des3 <- Lma.design(
 attribute.names = list(
  Region = c("Reg_A", "Reg_B", "Reg_C"),
  Eco = c("Conv.", "More", "Most"),
  Price = c("1", "1.1", "1.2")),
 nalternatives = 1,
 nblocks = 1,
 row.renames = FALSE,
 seed = 987)
des3
questionnaire(choice.experiment.design = des3, quote = FALSE)
desmat3 <- make.design.matrix(
 choice.experiment.design = des3,
 optout = TRUE,
 categorical.attributes = c("Region", "Eco"),
 continuous.attributes = c("Price"),
 unlabeled = TRUE,
 common = NULL,
 binary = TRUE)
data(syn.res3)
dataset3 <- make.dataset(
 respondent.dataset = syn.res3,
 choice.indicators = 
  c("q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8", "q9"),
 design.matrix = desmat3)
blout <- glm(RES ~ Reg_B + Reg_C + More + Most + Price,
 family = binomial(link = logit), data = dataset3)
summary(blout)
gofm(blout)
mwtp(output = blout,
 monetary.variables = c("Price"),
 nonmonetary.variables = 
  c("Reg_B", "Reg_C", "More", "Most"),
 seed = 987)