bs1: British Doctors Smoking and Coronary Heart Disease

Description Usage Format Source References Examples

Description

The problem is to investigate the impact of smoking tobacco among British doctors, refer Example 9.2.1 of Dobson. In the year 1951, a survey was sent across among all the British doctors asking them whether they smoked tobacco and their age group Age_Group. The data also collects the person-years Person_Years of the doctors in the respective age group. A follow-up after ten years reveals the number of deaths Deaths, the smoking group indicator Smoker_Cat.

Usage

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Format

A data frame with 10 observations on the following 9 variables.

Age_Group

a factor variable of age group with levels 35-44 45-54 55-64 65-74 75-84

Age_Cat

slightly re-coded to extract variables with Age_Cat taking values 1-5 respectively for the age groups 35-44, 45-54, 55-64, 65-74, and 75-84

Age_Square

square of the variable Age_Cat

Smoker_Cat

the smoking group indicator NO YES

Smoke_Ind

a numeric vector

Smoke_Age

takes the Age_Cat values for the smokers group and 0 for the non-smokers

Deaths

a follow-up after ten years revealing the number of deaths

Person_Years

the number of deaths standardized to 100000

Deaths_Per_Lakh_Years

a numeric vector

Source

Dobson (2002)

References

Dobson, A. J. (1990-2002). An Introduction to Generalized Linear Models, 2e. Chapman & Hall/CRC.

Examples

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library(MASS)
data(bs1)
BS_Pois <-  glm(Deaths~Age_Cat+Age_Square+Smoke_Ind+Smoke_Age,offset=
log(Person_Years),data=bs1,family='poisson')
logLik(BS_Pois)
summary(BS_Pois)
with(BS_Pois, pchisq(null.deviance - deviance,df.null - 
df.residual,lower.tail = FALSE)) 
confint(BS_Pois)

ACSWR documentation built on May 29, 2017, 8:41 p.m.