blindSortingWines: Wine experts and panelists (blind) sorted red, rosé, and...

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

Wine experts and panelists (blind) sorted red, rosé, and white wines. These data can be used to illustrate multidimensional scaling methods (e.g., MDS or DISTATIS).

Description

blindSortingWines 26 wine Experts and 19 Panelists (i.e., novices) blind sorted 18 wines (6 red, 6 rosé, and 6 white).

The 26 Experts were told to sort the wines into three groups whereas the Panelists just told to make as many groups as they wished but less the 18 and more than 1.

Usage

data("blindSortingWines")

Format

A list containing five data frames

• wineInformation: a df describing the 18 wines. The colors are coded P, R, W (Rosé, Red, and White).

• distance.Experts: the 18 by 18 distance matrix derived from the sorting of the 26 wine Experts.

• distance.Panelists: the 18 by 18 distance matrix derived from the sorting of the 26 wine Panelists.

• df.Experts: The 18 wines (rows) by 26 wine Experts (columns) data frame storing the results of the sorting task by the wine Experts (from S1 to S26). Here two wines (rows) with the same number were placed in the same group by the Expert (columns).

• df.Experts: The 18 wines (rows) by 19 wine Panelists (columns) data frame storing the results of the sorting task by the wine Panelists (from J1 to J19). Here two wines (rows) with the same number were placed in the same group by the Panelist (columns).

Author(s)

Jordi Ballester, Dominique Valentin, & Hervé Abdi

References

These data (and the story around) are described in more details in Ballester, J., Abdi, H., Langlois, J., Peyron, D., & Valentin, D. (2009). The odor of colors: Can wine experts and novices distinguish the odors of white, red, and rosé wines? Chemosensory Perception, 2, 203-213.

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