Quality of wine dataset

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.

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.

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)
#> 'data.frame':	34 obs. of  5 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 ...
#>  $ Quality    : Ord.factor w/ 3 levels "1"<"2"<"3": 2 3 2 3 1 1 3 3 3 2 ...