# wine: Bitterness of wine In ordinal: Regression Models for Ordinal Data

## 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

 `1` ```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

 ``` 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) ```

ordinal documentation built on May 2, 2019, 5:47 p.m.