R Dataset / Package COUNT / affairs

On this R-data statistics page, you will find information about the affairs data set which pertains to affairs. The affairs data set is found in the COUNT R package. You can load the affairs data set in R by issuing the following command at the console data("affairs"). This will load the data into a variable called affairs. If R says the affairs data set is not found, you can try installing the package by issuing this command install.packages("COUNT") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the affairs R data set. The size of this file is about 26,473 bytes.

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))

Dataset imported from https://www.r-project.org.

Attachments: csv, json

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