Wine: Chemical composition of three cultivars of wine

In candisc: Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis

Description

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

This data set is a classic in the machine learning literature as an easy high-D classification problem, but is also of interest for examples of MANOVA and discriminant analysis.

Usage

data("Wine")

Format

A data frame with 178 observations on the following 14 variables.

Cultivar

a factor with levels barolo grignolino barbera

Alcohol

a numeric vector

MalicAcid

a numeric vector

Ash

a numeric vector

AlcAsh

a numeric vector, Alcalinity of ash

Mg

a numeric vector, Magnesium

Phenols

a numeric vector, Total phenols

Flav

a numeric vector, Flavanoids

NonFlavPhenols

a numeric vector

Proa

a numeric vector, Proanthocyanins

Color

a numeric vector, color intensity

Hue

a numeric vector

OD

a numeric vector, OD280/OD315 of diluted wines

Proline

a numeric vector

Details

The precise definitions of these variables is unknown: units, how they were measured, etc.

Source

This data set was obtained from the UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/datasets/Wine. This page references a large number of papers that use this data set to compare different methods.

References

In R, a comparable data set is contained in the ggbiplot package.

Examples

data(Wine)
str(Wine)
#summary(Wine)

Wine.mlm <- lm(as.matrix(Wine[, -1]) ~ Cultivar, data=Wine)
Wine.can <- candisc(Wine.mlm)
Wine.can


plot(Wine.can, ellipse=TRUE)
plot(Wine.can, which=1)

candisc documentation built on May 2, 2019, 6:37 p.m.