On this Picostat.com statistics page, you will find information about the rwm5yr data set which pertains to rwm5yr. The rwm5yr data set is found in the COUNT R package. You can load the rwm5yr data set in R by issuing the following command at the console data("rwm5yr"). This will load the data into a variable called rwm5yr. If R says the rwm5yr data set is not found, you can try installing the package by issuing this command install.packages("COUNT") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the rwm5yr R data set. The size of this file is about 1,027,860 bytes.
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.
data(rwm5yr)glmrp <- glm(docvis ~ outwork + female + age + factor(edlevel), family=poisson, data=rwm5yr)
exp(coef(glmrp))## Not run:
nb2 <- nbinomial(docvis ~ outwork + female + age + factor(edlevel), data=rwm5yr)
exp(coef(nb2)) glmrnb <- glm.nb(docvis ~ outwork + female + age + factor(edlevel), data=rwm5yr)
exp(coef(glmrnb))## End(Not run)
Dataset imported from https://www.r-project.org.