rwm5yr {COUNT} | R Documentation |
rwm5yr
Description
German health registry for the years 1984-1988. Health information for years immediately prior to health reform.
Usage
data(rwm5yr)
Format
A data frame with 19,609 observations on the following 17 variables.
id
patient ID (1=7028)
docvis
number of visits to doctor during year (0-121)
hospvis
number of days in hospital during year (0-51)
year
year; (categorical: 1984, 1985, 1986, 1987, 1988)
edlevel
educational level (categorical: 1-4)
age
age: 25-64
outwork
out of work=1; 0=working
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
Details
rwm5yr is saved as a data frame. Count models typically use docvis as response variable. 0 counts are included
Source
German Health Reform Registry, years pre-reform 1984-1988, in Hilbe and Greene (2007)
References
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.
Examples
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)