Flury (version 0.1-3)

wines: Chemical composition of wines

Description

These data have been collected on the chemical composition of Weisser Riesling wines from three countries; South Africa,Germany and Italy

Usage

data(wines)

Format

'wines' is a data frame with 26 observations, one factor denoting the country of origin and 15 quantitative variables denoting 15 free monoterpenes and C[13]-norisoprenoids. It is thought these influence the wine's aroma.

Country

a factor with levels South Africa Germany Italy

Y1

a numeric vector

Y2

a numeric vector

Y3

a numeric vector

Y4

a numeric vector

Y5

a numeric vector

Y6

a numeric vector

Y7

a numeric vector

Y8

a numeric vector

Y9

a numeric vector

Y10

a numeric vector

Y11

a numeric vector

Y12

a numeric vector

Y13

a numeric vector

Y14

a numeric vector

Y15

a numeric vector

Source

Marais, J., G. Versini, C.J. van Wyj and A. Rapp (1992) “Effect of region on free and bound monoterpene and C[13]-norisoprenoid concentration in Weisser Riesling wines” South African Journal of Enology and Viniculture 13:71-77

Details

There are a total of nine South African wines, seven German wines (all from Pfalz) and ten from Northern Italy (from both Trentino Alto Adige as Friuli)

References

Flury, B.D. (1997) A First Course in Multivariate Statistics, Springer NY

Examples

data(wines)
## Not run: pairs(wines[,-1],
#   lower.panel = function(x, y){ points(x, y,
#   pch = unclass(wines[,1]),
#   col = as.numeric(wines[,1]))},
#   main = "Pairwise scatter plots for Marais wine data")
# ## rather congested scatter plots!## End(Not run)