EVOLUTION-MANAGER
Edit File: bacteria.html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Presence of Bacteria after Drug Treatments</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for bacteria {MASS}"><tr><td>bacteria {MASS}</td><td style="text-align: right;">R Documentation</td></tr></table> <h2> Presence of Bacteria after Drug Treatments </h2> <h3>Description</h3> <p>Tests of the presence of the bacteria <em>H. influenzae</em> in children with otitis media in the Northern Territory of Australia. </p> <h3>Usage</h3> <pre> bacteria </pre> <h3>Format</h3> <p>This data frame has 220 rows and the following columns: </p> <dl> <dt>y</dt><dd><p>presence or absence: a factor with levels <code>n</code> and <code>y</code>.</p> </dd> <dt>ap</dt><dd><p>active/placebo: a factor with levels <code>a</code> and <code>p</code>.</p> </dd> <dt>hilo</dt><dd><p>hi/low compliance: a factor with levels <code>hi</code> amd <code>lo</code>.</p> </dd> <dt>week</dt><dd><p>numeric: week of test.</p> </dd> <dt>ID</dt><dd><p>subject ID: a factor.</p> </dd> <dt>trt</dt><dd><p>a factor with levels <code>placebo</code>, <code>drug</code> and <code>drug+</code>, a re-coding of <code>ap</code> and <code>hilo</code>.</p> </dd> </dl> <h3>Details</h3> <p>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. </p> <p>The presence of <em>H. influenzae</em> was checked at weeks 0, 2, 4, 6 and 11: 30 of the checks were missing and are not included in this data frame. </p> <h3>Source</h3> <p>Dr Amanda Leach <em>via</em> Mr James McBroom. </p> <h3>References</h3> <p>Menzies School of Health Research 1999–2000 Annual Report. p.20. <a href="http://www.menzies.edu.au/icms_docs/172302_2000_Annual_report.pdf">http://www.menzies.edu.au/icms_docs/172302_2000_Annual_report.pdf</a>. </p> <p>Venables, W. N. and Ripley, B. D. (2002) <em>Modern Applied Statistics with S.</em> Fourth edition. Springer. </p> <h3>Examples</h3> <pre> 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)) </pre> <hr /><div style="text-align: center;">[Package <em>MASS</em> version 7.3-51.4 <a href="00Index.html">Index</a>]</div> </body></html>