badhealth

Description

A study of 1127 patients in which only three variables have been selected. The data may be evaluated as a logistic or other binary response model with the binary variable "badh" as the response. It may also be modeled as a count model with "numvisit" (number of visits to a physician during the year) as the response. "age" is an adjustor, and should be centered or standardized when used in the model.

Usage

data(badhealth)

Format

A data frame with 1127 observations with 3 variables.

numvisit

Number of visits to a physician during the year: 0 - 40

badh

0=patient evaluates self as in good health; 1=patient in bad health

age

patient age: 20 - 60

Details

badhealth is saved as a data frame.

Source

Hilbe, Practical Guide to Logistic Regression, Chapman & Hall/CRC

References

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

Examples

library(LOGIT)
data(badhealth)
age.std <- scale(badhealth$age)
summary(myhealth<- glm(badh ~  numvisit + age.std, family=binomial, data=badhealth))
toOR(myhealth)

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.
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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/badhealth.rd_%03d_medium.png", width=480, height=480)
> ### Name: badhealth
> ### Title: badhealth
> ### Aliases: badhealth
> ### Keywords: datasets
> 
> ### ** Examples
> 
> library(LOGIT)
> data(badhealth)
> age.std <- scale(badhealth$age)
> summary(myhealth<- glm(badh ~  numvisit + age.std, family=binomial, data=badhealth))

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

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0553  -0.4302  -0.3258  -0.2503   2.7930  

Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -3.07565    0.15679 -19.616  < 2e-16 ***
numvisit     0.22122    0.02628   8.419  < 2e-16 ***
age.std      0.57191    0.10906   5.244 1.57e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 729.66  on 1126  degrees of freedom
Residual deviance: 603.43  on 1124  degrees of freedom
AIC: 609.43

Number of Fisher Scoring iterations: 5

> toOR(myhealth)
                or  delta   zscore pvalue exp.loci. exp.upci.
(Intercept) 0.0462 0.0072 -19.6162      0    0.0339    0.0628
numvisit    1.2476 0.0328   8.4187      0    1.1850    1.3135
age.std     1.7717 0.1932   5.2441      0    1.4307    2.1939
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>