bordeauxNA: Quality of wine dataset

In plsRglm: Partial Least Squares Regression for Generalized Linear Models

Quality of wine dataset

Description

Quality of Bordeaux wines (Quality) and four potentially predictive variables (Temperature, Sunshine, Heat and Rain).

Format

A data frame with 34 observations on the following 5 variables.

Temperature

a numeric vector

Sunshine

a numeric vector

Heat

a numeric vector

Rain

a numeric vector

Quality

an ordered factor with levels 1 < 2 < 3

Details

The value of x1 for the first observation was removed from the matrix of predictors on purpose.

The bordeauxNA is a dataset with a missing value for testing purpose.

Source

P. Bastien, V. Esposito-Vinzi, and M. Tenenhaus. (2005). PLS generalised linear regression. Computational Statistics & Data Analysis, 48(1):17-46.

References

M. Tenenhaus. (2005). La regression logistique PLS. In J.-J. Droesbeke, M. Lejeune, and G. Saporta, editors, Modeles statistiques pour donnees qualitatives. Editions Technip, Paris.

Examples

data(bordeauxNA)
str(bordeauxNA)

plsRglm documentation built on March 31, 2023, 11:10 p.m.