R Dataset / Package COUNT / rwm

How To Create a Barplot

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How To Create a Pie Chart

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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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Correlation Coefficient

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Numerical Summaries

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Plot

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Regression

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<iframe src="https://embed.picostat.com/r-dataset-package-count-rwm.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-80914.csv 544.71 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 rwm data set which pertains to rwm. The rwm data set is found in the COUNT R package. You can load the rwm data set in R by issuing the following command at the console data("rwm"). This will load the data into a variable called rwm. If R says the rwm 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 rwm R data set. The size of this file is about 557,780 bytes.

Documentation

rwm

Description

German health registry for the years 1984-1988. Health information for years prior to health reform.

Usage

data(rwm)

Format

A data frame with 27,326 observations on the following 4 variables.

docvis

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

age

age: 25-64

educ

years of formal education (7-18)

hhninc

household yearly income in DM/1000)

Details

rwm 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, From Hilbe and Greene (2008)

References

Hilbe, Joseph M (2011), Negative Binomial Regression, Cambridge University Press Hilbe, J.M. and W.H. Greene (2008), "Count Response Regression Models", in Rao, CR, JP Miller and DC Rao (eds), Handbook of Statistics 27: Epidemiology and Medical Statistics, Amsterdam: Elsevier. pp. 210-252.

Examples

data(rwm)
glmrwp <- glm(docvis ~ age + educ + hhninc, family=poisson, data=rwm)
summary(glmrwp)
exp(coef(glmrwp))
library(MASS)
glmrwnb <- glm.nb(docvis ~ age + educ + hhninc, data=rwm)
summary(glmrwnb)
exp(coef(glmrwnb))
--

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

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