Bitterness of wine

Description

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.

Usage

wine

Format

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.

Source

Data are adopted from Randall (1989).

References

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.

Examples

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)