R Dataset / Package MASS / bacteria
Attachment  Size 

dataset80516.csv  6.43 KB 
Documentation 

On this Picostat.com 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. 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 TreatmentsDescriptionTests of the presence of the bacteria H. influenzae in children with otitis media in the Northern Territory of Australia. Usagebacteria FormatThis data frame has 220 rows and the following columns:
DetailsDr 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. SourceDr Amanda Leach via Mr James McBroom. ReferencesMenzies 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. Examplescontrasts(bacteria$trt) < structure(contr.sdif(3), dimnames = list(NULL, c("drug", "encourage"))) ## fixed effects analyses summary(glm(y ~ trt * week, binomial, data = bacteria)) summary(glm(y ~ trt + week, binomial, data = bacteria)) summary(glm(y ~ trt + I(week > 2), binomial, data = bacteria))# conditional randomeffects analysis library(survival) bacteria$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.rproject.org. 
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R Output  Date 

Picostat Output  Simple Linear Regression  Mar 16, 2020 
Picostat Output  Simple Linear Regression  Mar 16, 2020 
Picostat Output  Arithmetic Mean  Mar 16, 2020 
Picostat Output  Correlation Coefficients  Mar 16, 2020 
Picostat Output  Boxplot  Mar 16, 2020 
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