# R Dataset / Package COUNT / lbwgrp

Attachment | Size |
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dataset-57559.csv | 153 bytes |

Documentation |
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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. ## lbwgrp## Descriptiongrouped 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. ## Usagedata(lbwgrp) ## FormatA 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)
## Detailslbwgrp is saved as a data frame. Count models: count response=lowbt; offset=log(cases); Binary: binomial numerator= lowbt; binomial denominator=cases ## SourceHosmer, D and S. Lemeshow (2000), Applied Logistic Regression, Wiley ## ReferencesHilbe, Joseph M (2007, 2011), Negative Binomial Regression, Cambridge University Press Hilbe, Joseph M (2009), Logistic Regression Models, Chapman & Hall/CRC ## Examplesdata(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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