Wine {candisc} | R Documentation |
Chemical composition of three cultivars of wine
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.
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, Alkalinity 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
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.
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)