support.CEs (version 0.4-1)

rural: Synthetic respondent data set: residents' valuation of rural environment conservation plan

Description

Data set artificially created for an example based on a BDCE design. This example illustrates residents' valuation of rural environment conservation plan.

Usage

data(rural)

Format

Data frames with 400 respondents on the following 7 variables.

ID

Identification number of respondents.

BLOCK

Serial number of blocks to which each respondent had been assigned.

q1

Response to choice experiment question 1.

q2

Response to choice experiment question 2.

q3

Response to choice experiment question 3.

q4

Response to choice experiment question 4.

Region

Region variable denoting whether the respondent was sampled from region 1 (Region = 1) or region 2 (Region = 2).

See Also

make.dataset, make.design.matrix, Lma.design, glm

Examples

library(stats)

d.rural <- Lma.design(
  attribute.names = list(
    Area = c("20", "40", "60", "80"),
    Facility = c("None", "Agr", "Env", "Rec"),
    Tax = c("1000", "3000", "5000", "7000")),
  nalternatives = 1,
  nblocks = 4,
  row.renames = FALSE,
  seed = 987)

common.alt <- c(Area = "0", Facility = "None", Tax = "0")

dm.rural <- make.design.matrix(
  choice.experiment.design = d.rural,
  optout = FALSE,
  categorical.attributes = c("Facility"),
  continuous.attributes = c("Area", "Tax"),
  unlabeled = TRUE,
  common = common.alt,
  binary = TRUE)

data(rural)
rural1 <- subset(rural, Region == 1)
rural2 <- subset(rural, Region == 2)

ds.rural1 <- make.dataset(
  respondent.dataset = rural1,
  choice.indicators =
    c("q1", "q2", "q3", "q4"),
  design.matrix = dm.rural,
  detail = FALSE)

ds.rural2 <- make.dataset(
  respondent.dataset = rural2,
  choice.indicators =
    c("q1", "q2", "q3", "q4"),
  design.matrix = dm.rural,
  detail = FALSE)

fm.rural <- RES ~ Agr + Env + Rec + Area + Tax

out.rural1 <- glm(fm.rural,
                  family = binomial(link = "logit"),
                  data = ds.rural1)
summary(out.rural1)

out.rural2 <- glm(fm.rural,
                  family = binomial(link = "logit"),
                  data = ds.rural2)
summary(out.rural2)