sixteenGums4Descriptors: the average data from ten tasters who evaluated the intensity...

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

the average data from ten tasters who evaluated the intensity of four descriptors for sixteen chewing-gums. These data can be used to illustrate: normed Principal Component Analysis (PCA) or Multiple Correspondence Analysis (MCA).

Description

sixteenGums4Descriptors: the average data obtained from ten tasters who evaluated sixteen chewing gums on the intensity of four descriptors. These data can be used to illustrate: un-normed (or normed) Principal Component Analysis (PCA) and Correspondence Analysis (MCA). The data are the averages (computed over the 10 judges) of the intensity ratings of the 4 descriptors for the 16 gums.

Usage

data("sixteenGums4Descriptors", 
       package = 'data4PCCAR')

Format

A list (of class: sixteenGums) containing one data frame, and several vectors:

• ratingsIntensity: A data frame of dimension I = 16 rows (Beers) by J = 4 columns (descriptors) with the average intensity ratings performed using different scales per variables (respectively: 10, 5, 5, 100 points).

• longNames: a 16 element vector with the long names of the gums. color4Products a 16 element vector with the color names of the gums. color4Descriptors a 4 element vector with the color names of the descriptors. scale a 4 element vector with the number of points of the scales used to describe the gums.

Author(s)

Hervé Abdi & Carlos Gomez.

References

These data are used in Abdi H, Gomez, C., & Delmas, M. (2022). Méthodes Statistiques Multivariées pour l'Analyse Sensorielle et les Etudes Consommateurs.

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