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.

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.

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))