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)
#> 'data.frame': 34 obs. of 4 variables:
#> $ Temperature: int NA 3000 3155 3085 3245 3267 3080 2974 3038 3318 ...
#> $ Sunshine : int 1201 1053 1133 970 1258 1386 966 1189 1103 1310 ...
#> $ Heat : int 10 11 19 4 36 35 13 12 14 29 ...
#> $ Rain : int 361 338 393 467 294 225 417 488 677 427 ...