rwm1984: rwm1984
In LOGIT: Functions, Data and Code for Binary and Binomial Data

Description Usage Format Details Source References Examples

German health reform data for the year 1984. Subset of a multiyear registry evaluating differences in physician provider utilization prior to and after health reform legislation in the late 1980s.

1	data(rwm1984)

A data frame with 3,874 observations on the following 15 variables.

outwork: out of work=1; 0=working
docvis: number of visits to doctor during year (0-121)
hospvis: number of days in hospital during year (0-51)
edlevel: educational level (categorical: 1-4)
age: age: 25-64
female: female=1; 0=male
married: married=1; 0=not married
kids: have children=1; no children=0
hhninc: household yearly income in marks (in Marks)
educ: years of formal education (7-18)
self: self-employed=1; not self employed=0
edlevel1: (1/0) not high school graduate
edlevel2: (1/0) high school graduate
edlevel3: (1/0) university/college
edlevel4: (1/0) graduate school

rwm1984 is saved as a data frame. The data is typically used to model docvis, which is a count variable. It also may be used to model "outwork", a variable indicating if a patient is out-of-work. "outwork" is a binary variable which can be used as the response of a logistic or other binary response model.

German Health Reform Registry for the year 1984, in Hilbe and Greene (2007)

Hardin & Hilbe (2013), Generalized Linear Models & Extensions, 3rd ed, Stata Press.

Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC.

Hilbe, Joseph M (2011), Negative Binomial Regression, 2nd ed., Cambridge University Press.

Hilbe, Joseph M (2014), Modeling Count Data, Cambridge University Press.

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

# library(MASS)   if not automatically loaded
library(LOGIT)
# library(COUNT)  rwm1984 also in COUNT pacakge, but not toOR or P_disp
data(rwm1984)
# center both docvis and age
rwm1984$cage <- rwm1984$age - mean(rwm1984$age)
rwm1984$cdoc <- rwm1984$docvis - mean(rwm1984$docvis)
glmrp <- glm(outwork ~ cdoc + female + kids + cage + factor(edlevel),
             family=binomial, data=rwm1984)
summary(glmrp)
exp(coef(glmrp))
toOR(glmrp)