rwm5yr: German health registry for the years 1984-1988

Description Usage Format Details Source References Examples

Description

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

Usage

1

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,

References

Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press

Examples

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data(rwm5yr)

glmrp <- glm(docvis ~ outwork + female + age + factor(edlevel),
             family = poisson, data = rwm5yr)
summary(glmrp)
exp(coef(glmrp))

ml_p <- ml_glm(docvis ~ outwork + female + age + factor(edlevel),
               family = "poisson",
               link = "log",
               data = rwm5yr)
summary(ml_p)
exp(coef(ml_p))


library(MASS)
glmrnb <- glm.nb(docvis ~ outwork + female + age + factor(edlevel),
                 data = rwm5yr)
summary(glmrnb)
exp(coef(glmrnb))
## Not run: 
library(gee)
mygee <- gee(docvis ~ outwork + age + factor(edlevel), id=id, 
  corstr = "exchangeable", family=poisson, data=rwm5yr)
summary(mygee)
exp(coef(mygee))

## End(Not run)

Example output

Loading required package: MASS
Loading required package: lattice

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)HS grad     -0.0799467  0.0178411  -4.481 7.43e-06 ***
factor(edlevel)Coll/Univ   -0.2086855  0.0163618 -12.754  < 2e-16 ***
factor(edlevel)Grad School -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

               (Intercept)                    outwork 
                 1.1432922                  1.2449886 
                    female                        age 
                 1.2195607                  1.0194287 
    factor(edlevel)HS grad   factor(edlevel)Coll/Univ 
                 0.9231655                  0.8116504 
factor(edlevel)Grad School 
                 0.6851001 
Warning message:
In y - y.hat :
  Recycling array of length 1 in vector-array arithmetic is deprecated.
  Use c() or as.vector() instead.


Call:
ml_glm(formula = docvis ~ outwork + female + age + factor(edlevel), 
    data = rwm5yr, family = "poisson", link = "log")

Deviance Residuals:
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-3.4451 -2.2022 -1.1307 -0.5577  0.3578 25.7217 

Coefficients (all in linear predictor):
                           Estimate       SE      Z         p     LCL     UCL
(Intercept)                  0.1411 0.018290   7.72   1.2e-14  0.1053  0.1770
outwork                      0.2197 0.009321  23.58 6.91e-123  0.2015  0.2380
female                       0.1979 0.009049  21.87 4.54e-106  0.1802  0.2157
age                          0.0191 0.000376  50.75         0  0.0184  0.0198
factor(edlevel)HS grad      -0.0815 0.017849  -4.56  5.01e-06 -0.1165 -0.0465
factor(edlevel)Coll/Univ    -0.2094 0.016360 -12.80  1.68e-37 -0.2415 -0.1773
factor(edlevel)Grad School  -0.3803 0.020732 -18.35  3.57e-75 -0.4210 -0.3397

Null deviance: 122269.6  on  19608 d.f.
Residual deviance: 115248.6  on  19602 d.f.
AIC:  152529.2

Number of optimizer iterations:  82 

               X(Intercept)                    Xoutwork 
                  1.1515649                   1.2457506 
                    Xfemale                        Xage 
                  1.2188860                   1.0192761 
    Xfactor(edlevel)HS grad   Xfactor(edlevel)Coll/Univ 
                  0.9217600                   0.8110823 
Xfactor(edlevel)Grad School 
                  0.6836312 

Call:
glm.nb(formula = docvis ~ outwork + female + age + factor(edlevel), 
    data = rwm5yr, init.theta = 0.4900861749, link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5944  -1.3104  -0.4553   0.1323   5.7974  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 0.116906   0.047679   2.452   0.0142 *  
outwork                     0.214377   0.026218   8.177 2.92e-16 ***
female                      0.251202   0.024584  10.218  < 2e-16 ***
age                         0.019063   0.001023  18.634  < 2e-16 ***
factor(edlevel)HS grad     -0.091222   0.047534  -1.919   0.0550 .  
factor(edlevel)Coll/Univ   -0.202769   0.040354  -5.025 5.04e-07 ***
factor(edlevel)Grad School -0.390351   0.046969  -8.311  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for Negative Binomial(0.4901) family taken to be 1)

    Null deviance: 21200  on 19608  degrees of freedom
Residual deviance: 20231  on 19602  degrees of freedom
AIC: 85701

Number of Fisher Scoring iterations: 1


              Theta:  0.49009 
          Std. Err.:  0.00670 

 2 x log-likelihood:  -85685.32200 
               (Intercept)                    outwork 
                 1.1240135                  1.2390897 
                    female                        age 
                 1.2855693                  1.0192461 
    factor(edlevel)HS grad   factor(edlevel)Coll/Univ 
                 0.9128148                  0.8164669 
factor(edlevel)Grad School 
                 0.6768193 
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
               (Intercept)                    outwork 
                0.22506966                 0.30800723 
                       age     factor(edlevel)HS grad 
                0.01881195                -0.06420743 
  factor(edlevel)Coll/Univ factor(edlevel)Grad School 
               -0.23859642                -0.40280104 

 GEE:  GENERALIZED LINEAR MODELS FOR DEPENDENT DATA
 gee S-function, version 4.13 modified 98/01/27 (1998) 

Model:
 Link:                      Logarithm 
 Variance to Mean Relation: Poisson 
 Correlation Structure:     Exchangeable 

Call:
gee(formula = docvis ~ outwork + age + factor(edlevel), id = id, 
    data = rwm5yr, family = poisson, corstr = "exchangeable")

Summary of Residuals:
        Min          1Q      Median          3Q         Max 
 -5.5018838  -2.7300598  -1.7300598   0.6528813 117.6334056 


Coefficients:
                              Estimate  Naive S.E.    Naive z Robust S.E.
(Intercept)                 0.24864833 0.072023212  3.4523361  0.07224973
outwork                     0.24736193 0.031502752  7.8520738  0.03401158
age                         0.01889188 0.001512739 12.4885280  0.00154318
factor(edlevel)HS grad     -0.04704080 0.072729806 -0.6467885  0.06426464
factor(edlevel)Coll/Univ   -0.23476360 0.066317259 -3.5400077  0.05552501
factor(edlevel)Grad School -0.36057208 0.083291171 -4.3290552  0.07317554
                             Robust z
(Intercept)                 3.4415123
outwork                     7.2728734
age                        12.2421751
factor(edlevel)HS grad     -0.7319858
factor(edlevel)Coll/Univ   -4.2280695
factor(edlevel)Grad School -4.9274945

Estimated Scale Parameter:  9.928062
Number of Iterations:  3

Working Correlation
          [,1]      [,2]      [,3]      [,4]      [,5]
[1,] 1.0000000 0.3158285 0.3158285 0.3158285 0.3158285
[2,] 0.3158285 1.0000000 0.3158285 0.3158285 0.3158285
[3,] 0.3158285 0.3158285 1.0000000 0.3158285 0.3158285
[4,] 0.3158285 0.3158285 0.3158285 1.0000000 0.3158285
[5,] 0.3158285 0.3158285 0.3158285 0.3158285 1.0000000
               (Intercept)                    outwork 
                 1.2822910                  1.2806425 
                       age     factor(edlevel)HS grad 
                 1.0190715                  0.9540485 
  factor(edlevel)Coll/Univ factor(edlevel)Grad School 
                 0.7907578                  0.6972773 
Warning message:
system call failed: Cannot allocate memory 

msme documentation built on May 2, 2019, 5:07 a.m.

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