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

## Description

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.

## Usage

 `1` ```data(Sake) ```

## Format

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

## Details

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

## Source

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

## References

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```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)) ```

heplots documentation built on Oct. 7, 2021, 1:07 a.m.