## 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.

`naffairs`

number of affairs within last year

`kids`

1=have children;0= no children

`vryunhap`

(1/0) very unhappily married

`unhap`

(1/0) unhappily married

`avgmarr`

(1/0) average married

`hapavg`

(1/0) happily married

`vryhap`

(1/0) very happily married

`antirel`

(1/0) anti religious

`notrel`

(1/0) not religious

`slghtrel`

(1/0) slightly religious

`smerel`

(1/0) somewhat religious

`vryrel`

(1/0) very religious

`yrsmarr1`

(1/0) >0.75 yrs

`yrsmarr2`

(1/0) >1.5 yrs

`yrsmarr3`

(1/0) >4.0 yrs

`yrsmarr4`

(1/0) >7.0 yrs

`yrsmarr5`

(1/0) >10.0 yrs

`yrsmarr6`

(1/0) >15.0 yrs

### Details

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

### 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.

### 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))