COUNT (version 1.3.4)

affairs: affairs

Description

Data from Fair (1978). Although Fair used a tobit model with the data, the outcome measure can be modeled as a count. In fact, Greene (2003) modeled it as Poisson, but given the amount of overdispersion in the data, employing a negative binomial model is an appropriate strategy. The data is stored in the affairs data set. Naffairs is the response variable, indicating the number of affairs reported by the participant in the past year.

Usage

data(affairs)

Format

A data frame with 601 observations on the following 18 variables.

Source

Fair, R. (1978). A Theory of Extramarital Affairs, Journal of Political Economy, 86: 45-61. Greene, W.H. (2003). Econometric Analysis, Fifth Edition, New York: Macmillan.

Details

rwm5yr is saved as a data frame. Count models use naffairs as response variable. 0 counts are included.

References

Hilbe, Joseph M (2011), Negative Binomial Regression, Cambridge University Press Hilbe, Joseph M (2009), Logistic regression Models, Chapman & Hall/CRC

Examples

data(affairs)
glmaffp <- glm(naffairs ~ kids + yrsmarr2 + yrsmarr3 + yrsmarr4 + yrsmarr5,
               family = poisson, data = affairs)
summary(glmaffp)
exp(coef(glmaffp))

require(MASS)
glmaffnb <- glm.nb(naffairs ~ kids + yrsmarr2 + yrsmarr3 + yrsmarr4 + yrsmarr5,
                   data=affairs)
summary(glmaffnb)
exp(coef(glmaffnb))