fairml (version 0.8)

drug.consumption: Drug Consumption

Description

Predict drug consumption based on psychological scores and demographics.

Usage

data(drug.consumption)

Format

The data contains 1885 observations and 31 variables. See the UCI Machine Learning Repository for details.

References

UCI Machine Learning Repository.
https://archive-beta.ics.uci.edu/dataset/373/

Examples

data(drug.consumption)

# short-hand variable names.
r = drug.consumption[, "Meth"]
s = drug.consumption[, c("Age", "Gender", "Race")]
p = drug.consumption[, c("Education", "Nscore", "Escore", "Oscore", "Ascore",
                         "Cscore", "Impulsive", "SS")]

# collapse levels with low observed frequencies.
levels(p$Education) =
  c("at.most.18y", "at.most.18y", "at.most.18y", "at.most.18y", "university",
    "diploma", "bachelor", "master", "phd")

if (FALSE) {
m = fgrrm(response = r, sensitive = s, predictors = p, ,
      family = "multinomial", unfairness = 0.05)
summary(m)

HH = drug.consumption$Heroin
levels(HH) = c("Never Used", "Used", "Used", "Used", "Used Recently",
               "Used Recently", "Used Recently")

m = fgrrm(response = HH, sensitive = s, predictors = p, ,
      family = "multinomial", unfairness = 0.05)
summary(m)
}