| wide_conjoint {cregg} | R Documentation |
Example of a raw, “wide” conjoint dataset to demonstrate functionality of cj_tidy
Description
A simulated dataset containing 100 respondents' responses to four decision tasks (a,b,c,d) involving a forced choice between two alternative profiles, described by three features (1,2,3), as well as a secondary rating-scale outcome and a response time measure, along with two respondent-varying covariates. This is used in testing and examples within the package.
Usage
data(wide_conjoint)
Format
A data frame with 100 observations on the following variables:
- ‘respondent’
a numeric vector indicating the respondent identifier
- ‘feature1a1’
Feature 1 for task A left profile, a factor
- ‘feature1b1’
Feature 1 for task B left profile, a factor
- ‘feature1c1’
Feature 1 for task C left profile, a factor
- ‘feature1d1’
Feature 1 for task D left profile, a factor
- ‘feature1a2’
Feature 1 for task A right profile, a factor
- ‘feature1b2’
Feature 1 for task B right profile, a factor
- ‘feature1c2’
Feature 1 for task C right profile, a factor
- ‘feature1d2’
Feature 1 for task D right profile, a factor
- ‘feature2a1’
Feature 2 for task A left profile, a factor
- ‘feature2b1’
Feature 2 for task B left profile, a factor
- ‘feature2c1’
Feature 2 for task C left profile, a factor
- ‘feature2d1’
Feature 2 for task D left profile, a factor
- ‘feature2a2’
Feature 2 for task A right profile, a factor
- ‘feature2b2’
Feature 2 for task B right profile, a factor
- ‘feature2c2’
Feature 2 for task C right profile, a factor
- ‘feature2d2’
Feature 2 for task D right profile, a factor
- ‘feature3a1’
Feature 3 for task A left profile, a factor
- ‘feature3b1’
Feature 3 for task B left profile, a factor
- ‘feature3c1’
Feature 3 for task C left profile, a factor
- ‘feature3d1’
Feature 3 for task D left profile, a factor
- ‘feature3a2’
Feature 3 for task A right profile, a factor
- ‘feature3b2’
Feature 3 for task B right profile, a factor
- ‘feature3c2’
Feature 3 for task C right profile, a factor
- ‘feature3d2’
Feature 3 for task D right profile, a factor
- ‘choice_a’
outcome for task A indicating which profile was chosen, randomly 1 or 2, each equally probable
- ‘choice_b’
outcome for task B indicating which profile was chosen, randomly 1 or 2, each equally probable
- ‘choice_c’
outcome for task C indicating which profile was chosen, randomly 1 or 2, each equally probable
- ‘choice_d’
outcome for task D indicating which profile was chosen, randomly 1 or 2, each equally probable
- ‘rating_a1’
rating for task A left profile, random variable between 1 and 7, uniformly distributed
- ‘rating_a2’
rating for task A right profile, random variable between 1 and 7, uniformly distributed
- ‘rating_b1’
rating for task B left profile, random variable between 1 and 7, uniformly distributed
- ‘rating_b2’
rating for task B right profile, random variable between 1 and 7, uniformly distributed
- ‘rating_c1’
rating for task C left profile, random variable between 1 and 7, uniformly distributed
- ‘rating_c2’
rating for task C right profile, random variable between 1 and 7, uniformly distributed
- ‘rating_d1’
rating for task D left profile, random variable between 1 and 7, uniformly distributed
- ‘rating_d2’
rating for task D right profile, random variable between 1 and 7, uniformly distributed
- ‘timing_a’
timing for task A in seconds, random draws from a beta distribution (2,5) times 10
- ‘timing_b’
timing for task A in seconds, random draws from a beta distribution (2,5) times 10
- ‘timing_c’
timing for task A in seconds, random draws from a beta distribution (2,5) times 10
- ‘timing_d’
timing for task A in seconds, random draws from a beta distribution (2,5) times 10
- ‘covariate1’
random draws from a uniform distribution between -1 and 1
- ‘covariate2’
random draws from the set of 1 and 2
See Also
Examples
## Not run:
data("wide_conjoint")
# feature_variables
list1 <- list(
feature1 = list(
names(wide_conjoint)[grep("^feature1.{1}1", names(wide_conjoint))],
names(wide_conjoint)[grep("^feature1.{1}2", names(wide_conjoint))]
),
feature2 = list(
names(wide_conjoint)[grep("^feature2.{1}1", names(wide_conjoint))],
names(wide_conjoint)[grep("^feature2.{1}2", names(wide_conjoint))]
),
feature3 = list(
names(wide_conjoint)[grep("^feature3.{1}1", names(wide_conjoint))],
names(wide_conjoint)[grep("^feature3.{1}2", names(wide_conjoint))]
),
rating = list(
names(wide_conjoint)[grep("^rating.+1", names(wide_conjoint))],
names(wide_conjoint)[grep("^rating.+2", names(wide_conjoint))]
)
)
# task variables
list2 <- list(choice = paste0("choice_", letters[1:4]),
timing = paste0("timing_", letters[1:4]))
str(cj_tidy(wide_conjoint, profile_variables = list1, task_variables = list2, id = ~ respondent))
## End(Not run)