library("betareg")
data("WeatherTask", package = "betareg")
library("flexmix")
wt_betamix < betamix(agreement ~ 1, data = WeatherTask, k = 2,
extra_components = extraComponent(type = "betareg", coef =
list(mean = 0, precision = 2)),
FLXconcomitant = FLXPmultinom(~ priming + eliciting))
Weather Task with Priming and Precise and Imprecise Probabilities
Description
In this study participants were asked to judge how likely Sunday is to be the hottest day of the week.
Usage
data("WeatherTask", package = "betareg")
Format
A data frame with 345 observations on the following 3 variables.

priming

a factor with levels
twofold
(case prime) andsevenfold
(class prime). 
eliciting

a factor with levels
precise
andimprecise
(lower and upper limit). 
agreement
 a numeric vector, probability indicated by participants or the average between minimum and maximum probability indicated.
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 priming
the questions were:
 twofold
 [What is the probability that] the temperature at Canberra airport on Sunday will be higher than every other day next week?
 sevenfold
 [What is the probability that] the highest temperature of the week at Canberra airport will occur on Sunday?
For eliciting
the instructions were if
 precise
 to assign a probability estimate,
 imprecise
 to assign a lower and upper probability estimate.
Source
Taken from Smithson et al. (2011) supplements.
References
Smithson M, Merkle EC, 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, Segale C (2009). Partition Priming in Judgments of Imprecise Probabilities. Journal of Statistical Theory and Practice, 3(1), 169–181.