lbw: lbw In COUNT: Functions, Data and Code for Count Data

Description

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).

Usage

 1 data(lbw)

Format

A data frame with 189 observations on the following 10 variables.

low

1=low birthweight baby; 0=norml weight

smoke

1=history of mother smoking; 0=mother nonsmoker

race

categorical 1-3: 1=white; 2-=black; 3=other

age

age of mother: 14-45

lwt

weight (lbs) at last menstrual period: 80-250 lbs

ptl

number of false of premature labors: 0-3

ht

1=history of hypertension; 0 =no hypertension

ui

1=uterine irritability; 0 no irritability

ftv

number of physician visits in 1st trimester: 0-6

bwt

birth weight in grams: 709 - 4990 gr

Details

lbw is saved as a data frame. Count models can use ftv as a response variable, or convert it to grouped format

Source

Hosmer, D and S. Lemeshow (2000), Applied Logistic Regression, Wiley

References

Hilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC

Examples

 1 2 3 4 5 6 7 8 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))

Example output

Call:
glm(formula = ftv ~ low + smoke + factor(race), family = poisson,
data = lbw)

Deviance Residuals:
Min       1Q   Median       3Q      Max
-1.4052  -1.2238  -1.0950   0.4347   4.1037

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)   -0.01282    0.14073  -0.091    0.927
low           -0.11670    0.19116  -0.610    0.542
smoke         -0.15979    0.18253  -0.875    0.381
factor(race)2 -0.09579    0.24849  -0.385    0.700
factor(race)3 -0.33905    0.19933  -1.701    0.089 .
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

(Dispersion parameter for poisson family taken to be 1)

Null deviance: 264.52  on 188  degrees of freedom
Residual deviance: 260.35  on 184  degrees of freedom
AIC: 480.43

Number of Fisher Scoring iterations: 6

(Intercept)           low         smoke factor(race)2 factor(race)3
0.9872635     0.8898529     0.8523249     0.9086552     0.7124489

Call:
glm.nb(formula = ftv ~ low + smoke + factor(race), data = lbw,
init.theta = 1.896234314, link = log)

Deviance Residuals:
Min      1Q  Median      3Q     Max
-1.260  -1.121  -1.025   0.373   3.062

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept)   -0.01496    0.17190  -0.087    0.931
low           -0.12550    0.22547  -0.557    0.578
smoke         -0.15384    0.21794  -0.706    0.480
factor(race)2 -0.09088    0.29782  -0.305    0.760
factor(race)3 -0.33357    0.23585  -1.414    0.157

(Dispersion parameter for Negative Binomial(1.8962) family taken to be 1)

Null deviance: 195.29  on 188  degrees of freedom
Residual deviance: 192.36  on 184  degrees of freedom
AIC: 471.95

Number of Fisher Scoring iterations: 1

Theta:  1.896
Std. Err.:  0.798

2 x log-likelihood:  -459.954
(Intercept)           low         smoke factor(race)2 factor(race)3
0.9851477     0.8820544     0.8574067     0.9131278     0.7163583

COUNT documentation built on May 2, 2019, 2:37 a.m.