lbw

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

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

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))

Results

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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/lbw.Rd_%03d_medium.png", width=480, height=480)
> ### Name: lbw
> ### Title: lbw
> ### Aliases: lbw
> ### Keywords: datasets
> 
> ### ** Examples
> 
> data(lbw)
> glmbwp <- glm(ftv ~ low + smoke + factor(race), family=poisson, data=lbw)
> summary(glmbwp)

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

> exp(coef(glmbwp))
  (Intercept)           low         smoke factor(race)2 factor(race)3 
    0.9872635     0.8898529     0.8523249     0.9086552     0.7124489 
> library(MASS)
> glmbwnb <- glm.nb(ftv ~ low + smoke + factor(race), data=lbw)
> summary(glmbwnb)

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 
> exp(coef(glmbwnb))
  (Intercept)           low         smoke factor(race)2 factor(race)3 
    0.9851477     0.8820544     0.8574067     0.9131278     0.7163583 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>