COUNT (version 1.3.2)

rwm5yr: 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

Source

German Health Reform Registry, years pre-reform 1984-1988, in Hilbe and Greene (2007)

Details

rwm5yr is saved as a data frame. Count models typically use docvis as response variable. 0 counts are included

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)