Description Usage Format Source References Examples
The wine
data set is adopted from Randall(1989) and from a
factorial experiment on factors determining the bitterness of
wine. Two treatment factors (temperature and contact) each have two
levels. Temperature and contact between juice and skins can be
controlled when cruching grapes during wine production. Nine judges
each assessed wine from two bottles from each of the four treatment
conditions, hence there are 72 observations in all.
1 |
response
scorings of wine bitterness on a 0—100 continuous scale.
rating
ordered factor with 5 levels; a grouped version of response
.
temp
temperature: factor with two levels.
contact
factor with two levels ("no"
and "yes"
).
bottle
factor with eight levels.
judge
factor with nine levels.
Data are adopted from Randall (1989).
Randall, J (1989). The analysis of sensory data by generalised linear model. Biometrical journal 7, pp. 781–793.
Tutz, G. and W. Hennevogl (1996). Random effects in ordinal regression models. Computational Statistics & Data Analysis 22, pp. 537–557.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | head(wine)
str(wine)
## Variables 'rating' and 'response' are related in the following way:
(intervals <- seq(0,100, by = 20))
all(wine$rating == findInterval(wine$response, intervals)) ## ok
## A few illustrative tabulations:
## Table matching Table 5 in Randall (1989):
temp.contact.bottle <- with(wine, temp:contact:bottle)[drop=TRUE]
xtabs(response ~ temp.contact.bottle + judge, data = wine)
## Table matching Table 6 in Randall (1989):
with(wine, {
tcb <- temp:contact:bottle
tcb <- tcb[drop=TRUE]
table(tcb, rating)
})
## or simply: with(wine, table(bottle, rating))
## Table matching Table 1 in Tutz & Hennevogl (1996):
tab <- xtabs(as.numeric(rating) ~ judge + temp.contact.bottle,
data = wine)
colnames(tab) <-
paste(rep(c("c","w"), each = 4), rep(c("n", "n", "y", "y"), 2),
1:8, sep=".")
tab
## A simple model:
m1 <- clm(rating ~ temp * contact, data = wine)
summary(m1)
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