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 ...