library("betareg")
data("ImpreciseTask", package = "betareg")
library("flexmix")
wt_betamix < betamix(location ~ difference * task, data = ImpreciseTask, k = 2,
extra_components = extraComponent(type = "betareg", coef =
list(mean = 0, precision = 8)),
FLXconcomitant = FLXPmultinom(~ task))
Imprecise Probabilities for Sunday Weather and Boeing Stock Task
Description
In this study participants were asked to estimate upper and lower probabilities for event to occur and not to occur.
Usage
data("ImpreciseTask", package = "betareg")
Format
A data frame with 242 observations on the following 3 variables.

task

a factor with levels
Boeing stock
andSunday weather
. 
location
 a numeric vector of the average of the lower estimate for the event not to occur and the upper estimate for the event to occur.

difference
 a numeric vector of the differences of the lower and upper estimate for the event to occur.
Details
All participants in the study were either first or secondyear undergraduate students in psychology, none of whom had a strong background in probability or were familiar with imprecise probability theories.
For the sunday weather task see WeatherTask
. For the Boeing stock task participants were asked to estimate the probability that Boeing’s stock would rise more than those in a list of 30 companies.
For each task participants were asked to provide lower and upper estimates for the event to occur and not to occur.
Source
Taken from Smithson et al. (2011) supplements.
References
Smithson, M., Merkle, E.C., and Verkuilen, J. (2011). Beta Regression Finite Mixture Models of Polarization and Priming. Journal of Educational and Behavioral Statistics, 36(6), 804–831. doi:10.3102/1076998610396893
Smithson, M., and Segale, C. (2009). Partition Priming in Judgments of Imprecise Probabilities. Journal of Statistical Theory and Practice, 3(1), 169–181.