badhealth

Description

From German health survey data for the year 1998 only.

Usage

data(badhealth)

Format

A data frame with 1,127 observations on the following 3 variables.

numvisit

number of visits to doctor during 1998

badh

1=patient claims to be in bad health; 0=not in bad health

age

age of patient: 20-60

Details

badhealth is saved as a data frame. Count models use numvisit as the response variable, 0 counts are included.

Source

German Health Survey, amended in Hilbe and Greene (2008).

References

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

data(badhealth)
glmbadp <- glm(numvisit ~ badh + age, family=poisson, data=badhealth)
summary(glmbadp)
exp(coef(glmbadp))
library(MASS)
glmbadnb <- glm.nb(numvisit ~ badh + age, data=badhealth)
summary(glmbadnb)
exp(coef(glmbadnb))

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
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> 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/badhealth.Rd_%03d_medium.png", width=480, height=480)
> ### Name: badhealth
> ### Title: badhealth
> ### Aliases: badhealth
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(badhealth)
> glmbadp <- glm(numvisit ~ badh + age, family=poisson, data=badhealth)
> summary(glmbadp)

Call:
glm(formula = numvisit ~ badh + age, family = poisson, data = badhealth)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.6653  -1.9186  -0.6789   0.6292  10.0684  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) 0.447022   0.071428   6.258 3.89e-10 ***
badh        1.108331   0.046169  24.006  < 2e-16 ***
age         0.005822   0.001822   3.195   0.0014 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 4020.3  on 1126  degrees of freedom
Residual deviance: 3465.3  on 1124  degrees of freedom
AIC: 5638.6

Number of Fisher Scoring iterations: 5

> exp(coef(glmbadp))
(Intercept)        badh         age 
   1.563648    3.029299    1.005839 
> library(MASS)
> glmbadnb <- glm.nb(numvisit ~ badh + age, data=badhealth)
> summary(glmbadnb)

Call:
glm.nb(formula = numvisit ~ badh + age, data = badhealth, init.theta = 0.9974812528, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0304  -1.4361  -0.4152   0.3180   3.9516  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) 0.404116   0.130847   3.088  0.00201 ** 
badh        1.107342   0.111603   9.922  < 2e-16 ***
age         0.006952   0.003397   2.047  0.04070 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for Negative Binomial(0.9975) family taken to be 1)

    Null deviance: 1355.7  on 1126  degrees of freedom
Residual deviance: 1217.7  on 1124  degrees of freedom
AIC: 4475.3

Number of Fisher Scoring iterations: 1


              Theta:  0.9975 
          Std. Err.:  0.0693 

 2 x log-likelihood:  -4467.2850 
> exp(coef(glmbadnb))
(Intercept)        badh         age 
   1.497977    3.026304    1.006977 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>