msme (version 0.5.3)

rwm5yr: German health registry for the years 1984-1988

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

References

Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press

Examples

# NOT RUN {
data(rwm5yr)

glmrp <- glm(docvis ~ outwork + female + age + factor(edlevel),
             family = poisson, data = rwm5yr)
summary(glmrp)
exp(coef(glmrp))

ml_p <- ml_glm(docvis ~ outwork + female + age + factor(edlevel),
               family = "poisson",
               link = "log",
               data = rwm5yr)
summary(ml_p)
exp(coef(ml_p))


library(MASS)
glmrnb <- glm.nb(docvis ~ outwork + female + age + factor(edlevel),
                 data = rwm5yr)
summary(glmrnb)
exp(coef(glmrnb))
# }
# NOT RUN {
library(gee)
mygee <- gee(docvis ~ outwork + age + factor(edlevel), id=id, 
  corstr = "exchangeable", family=poisson, data=rwm5yr)
summary(mygee)
exp(coef(mygee))
# }