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.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(COUNT)
Loading required package: msme
Loading required package: MASS
Loading required package: lattice
Loading required package: sandwich
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/COUNT/rwm5yr.Rd_%03d_medium.png", width=480, height=480)
> ### Name: rwm5yr
> ### Title: rwm5yr
> ### Aliases: rwm5yr
> ### Keywords: datasets
>
> ### ** Examples
>
> library(MASS)
> data(rwm5yr)
>
> glmrp <- glm(docvis ~ outwork + female + age + factor(edlevel), family=poisson, data=rwm5yr)
> summary(glmrp)
Call:
glm(formula = docvis ~ outwork + female + age + factor(edlevel),
family = poisson, data = rwm5yr)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.4490 -2.2007 -1.1294 0.3583 25.7314
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.1339120 0.0183103 7.313 2.60e-13 ***
outwork 0.2191264 0.0093205 23.510 < 2e-16 ***
female 0.1984907 0.0090486 21.936 < 2e-16 ***
age 0.0192424 0.0003767 51.086 < 2e-16 ***
factor(edlevel)2 -0.0799467 0.0178411 -4.481 7.43e-06 ***
factor(edlevel)3 -0.2086855 0.0163618 -12.754 < 2e-16 ***
factor(edlevel)4 -0.3781903 0.0207183 -18.254 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 122270 on 19608 degrees of freedom
Residual deviance: 115248 on 19602 degrees of freedom
AIC: 152529
Number of Fisher Scoring iterations: 6
> exp(coef(glmrp))
(Intercept) outwork female age
1.1432922 1.2449886 1.2195607 1.0194287
factor(edlevel)2 factor(edlevel)3 factor(edlevel)4
0.9231655 0.8116504 0.6851001
>
> ## Not run:
> ##D library(msme)
> ##D nb2 <- nbinomial(docvis ~ outwork + female + age + factor(edlevel), data=rwm5yr)
> ##D summary(nb2)
> ##D exp(coef(nb2))
> ##D
> ##D glmrnb <- glm.nb(docvis ~ outwork + female + age + factor(edlevel), data=rwm5yr)
> ##D summary(glmrnb)
> ##D exp(coef(glmrnb))
> ## End(Not run)
>
>
>
>
>
> dev.off()
null device
1
>