R Dataset / Package COUNT / rwm1984

On this R-data 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 with the library() command. 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))

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

Attachments: csv, json

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