R Dataset / Package COUNT / lbwgrp

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How To Create a Pie Chart

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How To Compute the Mean

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How To Create a Plot

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How to Compute the Median

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Boxplot

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Correlation Coefficient

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Plot

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Regression

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<iframe src="https://embed.picostat.com/r-dataset-package-count-lbwgrp.html" frameBorder="0" width="100%" height="307px" />
Attachment Size
dataset-57559.csv 153 bytes
Dataset License
GNU General Public License v2.0
Documentation License
GNU General Public License v2.0
Dataset Help

On this Picostat.com statistics page, you will find information about the lbwgrp data set which pertains to lbwgrp. The lbwgrp data set is found in the COUNT R package. You can load the lbwgrp data set in R by issuing the following command at the console data("lbwgrp"). This will load the data into a variable called lbwgrp. If R says the lbwgrp 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 lbwgrp R data set. The size of this file is about 153 bytes.

Documentation

lbwgrp

Description

grouped format of the lbw data. The observation level data come to us form Hosmer and Lemeshow (2000). Grouping is such that lowbw is the numerator, and cases the denominator of a binomial model, or cases may be an offset to the count variable, lowbw. Birthweights under 2500g classifies a low birthweight baby.

Usage

data(lbwgrp)

Format

A data frame with 6 observations on the following 7 variables.

lowbw

Number of low weight babies per covariate pattern: 12-60

cases

Number of observations with same covariate pattern: 30-165

smoke

1=history of mother smoking; 0=mother nonsmoker

race1

(1/0): Caucasian

race2

(1/0): Black

race3

(1/0): Other

low

low birth weight (not valid variable in grouped format)

Details

lbwgrp is saved as a data frame. Count models: count response=lowbt; offset=log(cases); Binary: binomial numerator= lowbt; binomial denominator=cases

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(lbwgrp)
glmgp <- glm(lowbw ~ smoke + race2 + race3 + offset(log(cases)), family=poisson, data=lbwgrp)
summary(glmgp)
exp(coef(glmgp))
library(MASS)
glmgnb <- glm.nb(lowbw ~  smoke + race2 + race3, data=lbwgrp)
summary(glmgnb)
exp(coef(glmgnb))
--

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

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