XbordeauxNA: Incomplete dataset for the quality of wine dataset

In plsRglm: Partial Least Squares Regression for Generalized Linear Models

Incomplete dataset for the quality of wine dataset

Description

Quality of Bordeaux wines (Quality) and four potentially predictive variables (Temperature, Sunshine, Heat and Rain).
The value of Temperature for the first observation was remove from the matrix of predictors on purpose.

Format

A data frame with 34 observations on the following 4 variables.

Temperature

a numeric vector

Sunshine

a numeric vector

Heat

a numeric vector

Rain

a numeric vector

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(XbordeauxNA)
str(XbordeauxNA)

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