On this Picostat.com statistics page, you will find information about the rwm1984 data set which pertains to rwm1984. The rwm1984 data set is found in the COUNT R package. You can load the rwm1984 data set in R by issuing the following command at the console data("rwm1984"). This will load the data into a variable called rwm1984. If R says the rwm1984 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 rwm1984 R data set. The size of this file is about 164,149 bytes.

rwm1984

Description

German health registry for the year 1984.

Usage

data(rwm1984)

Format

A data frame with 3,874 observations on the following 17 variables.

docvis

number of visits to doctor during year (0-121)

hospvis

number of days in hospital during year (0-51)

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

rwm1984 is saved as a data frame. Count models typically use docvis as response variable. 0 counts are included

Source

German Health Reform Registry, year=1984, 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)
library(msme)
data(rwm1984)glmrp <- glm(docvis ~ outwork + female + age + factor(edlevel), family=poisson, data=rwm1984)
summary(glmrp)
exp(coef(glmrp))summary(nb2 <- nbinomial(docvis ~ outwork + female + age + factor(edlevel), data=rwm1984))
exp(coef(nb2))summary(glmrnb <- glm.nb(docvis ~ outwork + female + age + factor(edlevel), data=rwm1984))
exp(coef(glmrnb))

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Dataset imported from https://www.r-project.org.