R Dataset / Package MASS / bacteria

On this R-data statistics page, you will find information about the bacteria data set which pertains to Presence of Bacteria after Drug Treatments. The bacteria data set is found in the MASS R package. You can load the bacteria data set in R by issuing the following command at the console data("bacteria"). This will load the data into a variable called bacteria. If R says the bacteria data set is not found, you can try installing the package by issuing this command install.packages("MASS") and then attempt to reload the data with the library() command. 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 bacteria R data set. The size of this file is about 6,588 bytes.

Presence of Bacteria after Drug Treatments

Description

Tests of the presence of the bacteria H. influenzae in children with otitis media in the Northern Territory of Australia.

Usage

bacteria

Format

This data frame has 220 rows and the following columns:

y: presence or absence: a factor with levels n and y.
ap: active/placebo: a factor with levels a and p.
hilo: hi/low compliance: a factor with levels hi amd lo.
week: numeric: week of test.
ID: subject ID: a factor.
trt: a factor with levels placebo, drug and drug+, a re-coding of ap and hilo.

Details

Dr A. Leach tested the effects of a drug on 50 children with a history of otitis media in the Northern Territory of Australia. The children were randomized to the drug or the a placebo, and also to receive active encouragement to comply with taking the drug.

The presence of H. influenzae was checked at weeks 0, 2, 4, 6 and 11: 30 of the checks were missing and are not included in this data frame.

Source

Dr Amanda Leach via Mr James McBroom.

References

Menzies School of Health Research 1999–2000 Annual Report. p.20. http://www.menzies.edu.au/icms_docs/172302_2000_Annual_report.pdf.

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Examples

contrasts(bacteria$trt) <- structure(contr.sdif(3),
 dimnames = list(NULL, c("drug", "encourage")))
## fixed effects analyses
summary(glm(y ~ trt * week, binomial, data = bacteria))
summary(glm(y ~ trt + week, binomial, data = bacteria))
summary(glm(y ~ trt + I(week > 2), binomial, data = bacteria))# conditional random-effects analysis
library(survival)
bacteria$Time <- rep(1, nrow(bacteria))
coxph(Surv(Time, unclass(y)) ~ week + strata(ID),
data = bacteria, method = "exact")
coxph(Surv(Time, unclass(y)) ~ factor(week) + strata(ID),
data = bacteria, method = "exact")
coxph(Surv(Time, unclass(y)) ~ I(week > 2) + strata(ID),
data = bacteria, method = "exact")# PQL glmm analysis
library(nlme)
summary(glmmPQL(y ~ trt + I(week > 2), random = ~ 1 | ID,
family = binomial, data = bacteria))

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

Attachments: csv, json

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