winesOf3Colors: 36 plus 1 wines are evaluated on several dimensions organized...

In HerveAbdi/data4PCCAR: Some data sets and R-functions for PCA and CA to accompany Abdi & Beaton (to appear, 2023) Principal Component and Correspondence Analysis with R

36 plus 1 wines are evaluated on several dimensions organized in 4 blocks.

Description

winesOf3Colors: 36 wines are evaluated on several dimensions organized in 4 blocks. An additional mystery wine is provided (to be projected as supplementary observation).

Usage

data("winesOf3Colors")

Format

A list with one data frame with four blocks of data:

• 1 The descriptors (origin, color, varietal): columns 1 to 3.

• 2 The price (in Dollars): column 4.

• 3 The chemistry properties: columns 5 to 8

• 4 The sensory properties: columns 9 to 17.

Details

The (fictitious) wines come from 3 countries Argentina, Canada, and the USA. They also come in three different colors: Red, Rosé, and White. They are described by their characteristics, chemistry, and sensory properties. The mystery wine is a French red pinot noir that could be though as a "Bourgogne Rouge".

Author(s)

Hervé Abdi and Dominique Valentin

References

These data have been used to illustrate PLSC and CCA. See, e.g., (papers available from https://personal.utdallas.edu/~herve/)

Abdi H., Eslami, A., & Guillemot, V. (2018). Canonical correlation analysis (CCA). In R. Alhajj and J. Rokne (Eds.), Encyclopedia of Social Networks and Mining (2nd Edition). New York: Springer Verlag.

Abdi, H., & Williams, L.J. (2013). Partial least squares methods: Partial least squares correlation and partial least square regression. In: B. Reisfeld & A. Mayeno (Eds.), Methods in Molecular Biology: Computational Toxicology. New York: Springer Verlag. pp. 549-579.

HerveAbdi/data4PCCAR documentation built on Sept. 11, 2022, 4:19 p.m.