Affairs: Fair's Extramarital Affairs Data

Description

Infidelity data, known as Fair's Affairs. Cross-section data from a survey conducted by Psychology Today in 1969.

Usage

data("Affairs")

Arguments

Format

A data frame containing 601 observations on 9 variables.

affairs: numeric. How often engaged in extramarital sexual intercourse during the past year? 0 = none, 1 = once, 2 = twice, 3 = 3 times, 7 = 4--10 times, 12 = monthly, 12 = weekly, 12 = daily.
gender: factor indicating gender.
age: numeric variable coding age in years: 17.5 = under 20, 22 = 20--24, 27 = 25--29, 32 = 30--34, 37 = 35--39, 42 = 40--44, 47 = 45--49, 52 = 50--54, 57 = 55 or over.
yearsmarried: numeric variable coding number of years married: 0.125 = 3 months or less, 0.417 = 4--6 months, 0.75 = 6 months--1 year, 1.5 = 1--2 years, 4 = 3--5 years, 7 = 6--8 years, 10 = 9--11 years, 15 = 12 or more years.
children: factor. Are there children in the marriage?
religiousness: numeric variable coding religiousness: 1 = anti, 2 = not at all, 3 = slightly, 4 = somewhat, 5 = very.
education: numeric variable coding level of education: 9 = grade school, 12 = high school graduate, 14 = some college, 16 = college graduate, 17 = some graduate work, 18 = master's degree, 20 = Ph.D., M.D., or other advanced degree.
occupation: numeric variable coding occupation according to Hollingshead classification (reverse numbering).
rating: numeric variable coding self rating of marriage: 1 = very unhappy, 2 = somewhat unhappy, 3 = average, 4 = happier than average, 5 = very happy.

References

Greene, W.H. (2003). Econometric Analysis, 5th edition. Upper Saddle River, NJ: Prentice Hall.

Fair, R.C. (1978). A Theory of Extramarital Affairs. Journal of Political Economy, 86, 45--61.

Examples

Run this code

# NOT RUN {
data("Affairs")

## Greene (2003)
## Tab. 22.3 and 22.4
fm_ols <- lm(affairs ~ age + yearsmarried + religiousness + occupation + rating,
  data = Affairs)
fm_probit <- glm(I(affairs > 0) ~ age + yearsmarried + religiousness + occupation + rating,
  data = Affairs, family = binomial(link = "probit"))

fm_tobit <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating,
  data = Affairs)
fm_tobit2 <- tobit(affairs ~ age + yearsmarried + religiousness + occupation + rating,
  right = 4, data = Affairs)

fm_pois <- glm(affairs ~ age + yearsmarried + religiousness + occupation + rating,
  data = Affairs, family = poisson)

library("MASS")
fm_nb <- glm.nb(affairs ~ age + yearsmarried + religiousness + occupation + rating,
  data = Affairs)

## Tab. 22.6
library("pscl")
fm_zip <- zeroinfl(affairs ~ age + yearsmarried + religiousness + occupation + rating | age + 
  yearsmarried + religiousness + occupation + rating, data = Affairs)
# }