| rwm5yr | R Documentation |
German health registry for the years 1984-1988. Health information for years immediately prior to health reform.
data(rwm5yr)
A data frame with 19,609 observations on the following 17 variables.
idpatient ID (1=7028)
docvisnumber of visits to doctor during year (0-121)
hospvisnumber of days in hospital during year (0-51)
yearyear; (categorical: 1984, 1985, 1986, 1987, 1988)
edleveleducational level (categorical: 1-4)
ageage: 25-64
outworkout of work=1; 0=working
femalefemale=1; 0=male
marriedmarried=1; 0=not married
kidshave children=1; no children=0
hhninchousehold yearly income in marks (in Marks)
educyears of formal education (7-18)
selfself-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
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)
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