The data come to us from Hosmer and Lemeshow (2000). Called the low birth weight (lbw) data, the response is a binary variable, low, which indicates whether the birth weight of a baby is under 2500g (low=1), or over (low=0).
A data frame with 189 observations on the following 10 variables.
1=low birthweight baby; 0=norml weight
1=history of mother smoking; 0=mother nonsmoker
categorical 1-3: 1=white; 2-=black; 3=other
age of mother: 14-45
weight (lbs) at last menstrual period: 80-250 lbs
number of false of premature labors: 0-3
1=history of hypertension; 0 =no hypertension
1=uterine irritability; 0 no irritability
number of physician visits in 1st trimester: 0-6
birth weight in grams: 709 - 4990 gr
lbw is saved as a data frame. Count models can use ftv as a response variable, or convert it to grouped format
Hosmer, D and S. Lemeshow (2000), Applied Logistic Regression, Wiley
Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC
data(lbw) glmbwp <- glm(ftv ~ low + smoke + factor(race), family=poisson, data=lbw) summary(glmbwp) exp(coef(glmbwp)) library(MASS) glmbwnb <- glm.nb(ftv ~ low + smoke + factor(race), data=lbw) summary(glmbwnb) exp(coef(glmbwnb))