ce.response: Generating artificial responses to choice experiment...
In support.CEs: Basic Functions for Supporting an Implementation of Choice Experiments

ce.response

R Documentation

Generating artificial responses to choice experiment questions

Description

This function synthesizes responses to choice experiment questions.

Usage

ce.response(design,
            categorical.attributes = NULL, 
            continuous.attributes = NULL,
            optout = FALSE,
            b,
            n,
            detail = FALSE,
            seed = NULL)

Arguments

`design`	An object of the S3 class "cedes".
`categorical.attributes`	A vector containing the names of attributes treated as categorical independent variables in the model. If there are no categorical variables, it is set as `NULL` (default).
`continuous.attributes`	A vector containing the names of attributes treated as continuous independent variables in the model. If there are no continuous variables, it is set as `NULL` (default).
`optout`	A logical variable describing whether or not the opt-out alternative is included in the choice experiment questions. If `TRUE` (default), the opt-out alternative is included; otherwise it is not.
`b`	A vector containing parameters of independent variables in the model. The vector is used to calculate utilities for alternatives.
`n`	An integer value showing the number of respondents in the resultant dataset.
`detail`	A logical variable: if `TRUE`, the dataset is returned in a detailed format; and if `FALSE` (default), the dataset is return in a simple format.
`seed`	Seed for a random number generator.

Details

This function synthesizes responses to choice experiment questions. Before using the function, see help for functions rotation.design, make.design.matrix, and make.dataset. For details on the usage, refer to section Examples below.

Value

This function returns a data frame that contains synthesized responses to choice experiment questions, in either a detailed or a simple format. The detailed format dataset contains the following variables, as well as independent variables according to the arguments categorical.attributes and/or continuous.attributes.

`ID`	An identification number of artificial respondents.
`BLOCK`	A serial number of blocks in the choice sets.
`QES`	A serial number of questions.
`ALT`	A serial number of alternatives.
`ASC`	Alternative specific constant.
`STR`	A stratification variable used to identify each combination of respondent and question.
`RES`	Responses to choice experiment questions, taking the value of 1 if an alternative is selected by the synthesized respondents and 0 otherwise.

Author(s)

Hideo Aizaki

Examples

# The following example is based on the dataset rice.
# The choice sets differ from those in the dataset rice.

library(survival)

dsgn.rice <- rotation.design(
  attribute.names = list(
    Region = c("RegA", "RegB", "RegC"),
    Cultivation = c("Conv", "NoChem", "Organic"),
    Price = c("1700", "2000", "2300")),
  nalternatives = 2,
  nblocks = 3,
  row.renames = FALSE,
  randomize = TRUE,
  seed = 987)

syn.rice1 <- ce.response(
  design = dsgn.rice, 
  categorical.attributes = c("Region", "Cultivation"),
  continuous.attributes = "Price",
  optout = TRUE,
  b = c(4.4, 0.47, 0.97, 0.66, 1.1, -0.0023),
  n = 99,
  detail = TRUE,
  seed = 1)
head(syn.rice1, 30)
mf <- RES ~ ASC + RegB + RegC + NoChem + Organic + Price + strata(STR)
(out1 <- clogit(mf, data = syn.rice1))

syn.rice.simple <- ce.response(
  design = dsgn.rice, 
  categorical.attributes = c("Region", "Cultivation"),
  continuous.attributes = "Price",
  optout = TRUE,
  b = c(4.4, 0.47, 0.97, 0.66, 1.1, -0.0023),
  n = 99,
  detail = FALSE,
  seed = 1)
head(syn.rice.simple)
dsgnmat.rice <- make.design.matrix(
  choice.experiment.design = dsgn.rice,
  optout = TRUE,
  categorical.attributes = c("Region", "Cultivation"),
  continuous.attributes = c("Price"),
  unlabeled = TRUE)
syn.rice2 <- make.dataset(
  respondent.dataset = syn.rice.simple,
  choice.indicators =
    c("q1", "q2", "q3"),
  design.matrix = dsgnmat.rice)
(out2 <- clogit(mf, data = syn.rice2))

all.equal(coef(out1), coef(out2))

support.CEs documentation built on Nov. 3, 2023, 9:07 a.m.