R Dataset / Package COUNT / lbw

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How To Compute the Mean

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How to Compute the Median

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<iframe src="https://embed.picostat.com/r-dataset-package-count-lbw.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-33628.csv 4.84 KB
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Documentation

On this Picostat.com statistics page, you will find information about the lbw data set which pertains to lbw. The lbw data set is found in the COUNT R package. You can load the lbw data set in R by issuing the following command at the console data("lbw"). This will load the data into a variable called lbw. If R says the lbw data set is not found, you can try installing the package by issuing this command install.packages("COUNT") and then attempt to reload the data. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the lbw R data set. The size of this file is about 4,955 bytes.


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))
--

Dataset imported from https://www.r-project.org.

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