Sake: Taste Ratings of Japanese Rice Wine (Sake)
In heplots: Visualizing Hypothesis Tests in Multivariate Linear Models

Description Usage Format Details Source References Examples

Siotani et al. (1985) describe a study of Japanese rice wine (sake) used to investigate the relationship between two subjective ratings (taste and smell) and a number of physical measurements on 30 brands of sake.

These data provide one example of a case where a multivariate regression doesn't benefit from having multiple outcome measures, using the standard tests. Barrett (2003) uses this data to illustrate influence measures for multivariate regression models.

1	data(Sake)

A data frame with 30 observations on the following 10 variables.

taste: mean taste rating
smell: mean smell rating
pH: pH measurement
acidity1: one measure of acidity
acidity2: another measure of acidity
sake: Sake-meter score
rsugar: direct reducing sugar content
tsugar: total sugar content
alcohol: alcohol content
nitrogen: formol-nitrogen content

The taste and smell values are the mean ratings of 10 experts on some unknown scale.

Siotani, M. Hayakawa, T. & Fujikoshi, Y. (1985). Modern Multivariate Statistical Analysis: A Graduate Course and Handbook. American Sciences Press, p. 217.

Barrett, B. E. (2003). Understanding Influence in Multivariate Regression. Communications in Statistics - Theory and Methods 32 (3), 667-680.

data(Sake)
# quick look at the data
boxplot(scale(Sake))

Sake.mod <- lm(cbind(taste,smell) ~ ., data=Sake)

library(car)
Anova(Sake.mod)

predictors <- colnames(Sake)[-(1:2)]                 
# overall multivariate regression test
linearHypothesis(Sake.mod, predictors)

heplot(Sake.mod, hypotheses=list("Regr" = predictors))