rwm1984

Description

German health reform data for the year 1984. Subset of a multiyear registry evaluating differences in physician provider utilization prior to and after health reform legislation in the late 1980s.

Usage

data(rwm1984)

Format

A data frame with 3,874 observations on the following 15 variables.

outwork

out of work=1; 0=working

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

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. The data is typically used to model docvis, which is a count variable. It also may be used to model "outwork", a variable indicating if a patient is out-of-work. "outwork" is a binary variable which can be used as the response of a logistic or other binary response model.

Source

German Health Reform Registry for the year 1984, in Hilbe and Greene (2007)

References

Hardin & Hilbe (2013), Generalized Linear Models & Extensions, 3rd ed, Stata Press.

Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC.

Hilbe, Joseph M (2011), Negative Binomial Regression, 2nd ed., Cambridge University Press.

Hilbe, Joseph M (2014), Modeling Count Data, Cambridge University Press.

Hilbe, Joseph M (2015), Practical Guide to Logistic Regression, Chapman & Hall/CRC.

Examples

# library(MASS)   if not automatically loaded
library(LOGIT)
# library(COUNT)  rwm1984 also in COUNT pacakge, but not toOR or P_disp
data(rwm1984)
# center both docvis and age
rwm1984$cage <- rwm1984$age - mean(rwm1984$age)
rwm1984$cdoc <- rwm1984$docvis - mean(rwm1984$docvis)
glmrp <- glm(outwork ~ cdoc + female + kids + cage + factor(edlevel),
             family=binomial, data=rwm1984)
summary(glmrp)
exp(coef(glmrp))
toOR(glmrp)

Results

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(LOGIT)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/LOGIT/rwm1984.rd_%03d_medium.png", width=480, height=480)
> ### Name: rwm1984
> ### Title: rwm1984
> ### Aliases: rwm1984
> ### Keywords: datasets
> 
> ### ** Examples
> 
> # library(MASS)   if not automatically loaded
> library(LOGIT)
> # library(COUNT)  rwm1984 also in COUNT pacakge, but not toOR or P_disp
> data(rwm1984)
> # center both docvis and age
> rwm1984$cage <- rwm1984$age - mean(rwm1984$age)
> rwm1984$cdoc <- rwm1984$docvis - mean(rwm1984$docvis)
> glmrp <- glm(outwork ~ cdoc + female + kids + cage + factor(edlevel),
+              family=binomial, data=rwm1984)
> summary(glmrp)

Call:
glm(formula = outwork ~ cdoc + female + kids + cage + factor(edlevel), 
    family = binomial, data = rwm1984)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3615  -0.6807  -0.4320   0.8541   2.8220  

Coefficients:
                  Estimate Std. Error z value Pr(>|z|)    
(Intercept)      -2.022806   0.085790 -23.579  < 2e-16 ***
cdoc              0.024480   0.006255   3.914 9.08e-05 ***
female            2.268467   0.084039  26.993  < 2e-16 ***
kids              0.373053   0.090586   4.118 3.82e-05 ***
cage              0.054355   0.004207  12.921  < 2e-16 ***
factor(edlevel)2  0.009293   0.173172   0.054 0.957201    
factor(edlevel)3  0.478016   0.153786   3.108 0.001882 ** 
factor(edlevel)4 -0.830282   0.214336  -3.874 0.000107 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 5091.1  on 3873  degrees of freedom
Residual deviance: 3889.9  on 3866  degrees of freedom
AIC: 3905.9

Number of Fisher Scoring iterations: 4

> exp(coef(glmrp))
     (Intercept)             cdoc           female             kids 
       0.1322837        1.0247821        9.6645776        1.4521618 
            cage factor(edlevel)2 factor(edlevel)3 factor(edlevel)4 
       1.0558599        1.0093368        1.6128714        0.4359263 
> toOR(glmrp)
                     or  delta   zscore pvalue exp.loci. exp.upci.
(Intercept)      0.1323 0.0113 -23.5787 0.0000    0.1118    0.1565
cdoc             1.0248 0.0064   3.9138 0.0001    1.0123    1.0374
female           9.6646 0.8122  26.9929 0.0000    8.1969   11.3951
kids             1.4522 0.1315   4.1182 0.0000    1.2159    1.7343
cage             1.0559 0.0044  12.9205 0.0000    1.0472    1.0646
factor(edlevel)2 1.0093 0.1748   0.0537 0.9572    0.7188    1.4172
factor(edlevel)3 1.6129 0.2480   3.1083 0.0019    1.1932    2.1802
factor(edlevel)4 0.4359 0.0934  -3.8737 0.0001    0.2864    0.6635
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> dev.off()
null device 
          1 
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