R Dataset / Package COUNT / rwm5yr

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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<iframe src="https://embed.picostat.com/r-dataset-package-count-rwm5yr.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-57527.csv 1003.77 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

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.

Documentation

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

Details

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

Source

German Health Reform Registry, years pre-reform 1984-1988, 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)
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)
--

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

R Output Date
Picostat Output - Boxplot Mar 9, 2019
Picostat Output - Boxplot Mar 9, 2019
Picostat Output - Boxplot Mar 9, 2019