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
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(bordeaux)
str(bordeaux)
#> 'data.frame': 34 obs. of 5 variables:
#> $ Temperature: int 3064 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 ...
#> $ Quality : Ord.factor w/ 3 levels "1"<"2"<"3": 2 3 2 3 1 1 3 3 3 2 ...