badhealth: badhealth
In LOGIT: Functions, Data and Code for Binary and Binomial Data

Description Usage Format Details Source References Examples

A study of 1127 patients in which only three variables have been selected. The data may be evaluated as a logistic or other binary response model with the binary variable "badh" as the response. It may also be modeled as a count model with "numvisit" (number of visits to a physician during the year) as the response. "age" is an adjustor, and should be centered or standardized when used in the model.

1	data(badhealth)

A data frame with 1127 observations with 3 variables.

numvisit: Number of visits to a physician during the year: 0 - 40
badh: 0=patient evaluates self as in good health; 1=patient in bad health
age: patient age: 20 - 60

badhealth is saved as a data frame.

Hilbe, Practical Guide to Logistic Regression, Chapman & Hall/CRC

Hilbe, Joseph M (2015), Practical Guide to Logistic Regression, Chapman & Hall/CRC

library(LOGIT)
data(badhealth)
age.std <- scale(badhealth$age)
summary(myhealth<- glm(badh ~  numvisit + age.std, family=binomial, data=badhealth))
toOR(myhealth)