German health registry for the years 1984-1988. Health information for years immediately prior to health reform.
A data frame with 19,609 observations on the following 17 variables.
patient ID (1=7028)
number of visits to doctor during year (0-121)
number of days in hospital during year (0-51)
year; (categorical: 1984, 1985, 1986, 1987, 1988)
educational level (categorical: 1-4)
out of work=1; 0=working
married=1; 0=not married
have children=1; no children=0
household yearly income in marks (in Marks)
years of formal education (7-18)
self-employed=1; not self employed=0
(1/0) not high school graduate
(1/0) high school graduate
(1/0) graduate school
rwm5yr is saved as a data frame. Count models typically use docvis as response variable. 0 counts are included
German Health Reform Registry, years pre-reform 1984-1988, in Hilbe and Greene (2007)
Hilbe, Joseph M (2014), Modeling Count Data, Cambridge University Press Hilbe, Joseph M (2011), Negative Binomial Regression, Cambridge University Press Hilbe, J. and W. Greene (2008). Count Response Regression Models, in ed. C.R. Rao, J.P Miller, and D.C. Rao, Epidemiology and Medical Statistics, Elsevier Handbook of Statistics Series. London, UK: Elsevier.
library(MASS) data(rwm5yr) glmrp <- glm(docvis ~ outwork + female + age + factor(edlevel), family=poisson, data=rwm5yr) summary(glmrp) exp(coef(glmrp)) ## Not run: library(msme) nb2 <- nbinomial(docvis ~ outwork + female + age + factor(edlevel), data=rwm5yr) summary(nb2) exp(coef(nb2)) glmrnb <- glm.nb(docvis ~ outwork + female + age + factor(edlevel), data=rwm5yr) summary(glmrnb) exp(coef(glmrnb)) ## End(Not run)