R Dataset / Package HistData / CushnyPeebles
On this Rdata statistics page, you will find information about the CushnyPeebles data set which pertains to CushnyPeebles Data: Soporific Effects of Scopolamine Derivatives. The CushnyPeebles data set is found in the HistData R package. You can load the CushnyPeebles data set in R by issuing the following command at the console data("CushnyPeebles"). This will load the data into a variable called CushnyPeebles. If R says the CushnyPeebles data set is not found, you can try installing the package by issuing this command install.packages("HistData") 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 CushnyPeebles R data set. The size of this file is about 224 bytes.
CushnyPeebles Data: Soporific Effects of Scopolamine Derivatives
Description
Cushny and Peebles (1905) studied the effects of hydrobromides related to scopolamine and atropine in producing sleep. The sleep of mental patients was measured without hypnotic (Control
) and after treatment with one of three drugs: L. hyoscyamine hydrobromide (L_hyoscyamine
), L. hyoscine hydrobromide (L_hyoscyine
), and a mixture (racemic) form, DL_hyoscine
, called atropine. The L (levo) and D (detro) form of a given molecule are optical isomers (mirror images).
The drugs were given on alternate evenings, and the hours of sleep were compared with the intervening control night. Each of the drugs was tested in this manner a varying number of times in each subject. The average number of hours of sleep for each treatment is the response.
Student (1908) used these data to illustrate the pairedsample ttest in small samples, testing the hypothesis that the mean difference between a given drug and the control condition was zero. This data set became well known when used by Fisher (1925). Both Student and Fisher had problems labeling the drugs correctly (see Senn & Richardson (1994)), and consequently came to wrong conclusions.
But as well, the sample sizes (number of nights) for each mean differed widely, ranging from 39, and this was not taken into account in their analyses. To allow weighted analyses, the number of observations for each mean is contained in the data frame CushnyPeeblesN
.
Usage
data(CushnyPeebles) data(CushnyPeeblesN)
Format
CushnyPeebles
: A data frame with 11 observations on the following 4 variables.
Control

a numeric vector: mean hours of sleep
L_hyoscyamine

a numeric vector: mean hours of sleep
L_hyoscine

a numeric vector: mean hours of sleep
D_hyoscine

a numeric vector: mean hours of sleep
CushnyPeeblesN
: A data frame with 11 observations on the following 4 variables.
Control

a numeric vector: number of observations
L_hyoscyamine

a numeric vector: number of observations
L_hyoscine

a numeric vector: number of observations
DL_hyoscine

a numeric vector: number of observations
Details
The last patient (11) has no Control
observations, and so is often excluded in analyses or other versions of this data set.
Source
Cushny, A. R., and Peebles, A. R. (1905), "The Action of Optical Isomers. II: Hyoscines," Journal of Physiology, 32, 501510.
Senn, Stephen, Data from Cushny and Peebles, http://www.senns.demon.co.uk/Data/Cushny.xls
References
Fisher, R. A. (1925), Statistical Methods for Research Workers, Edinburgh and London: Oliver & Boyd.
Student (1908), "The Probable Error of a Mean," Biometrika, 6, 125.
Senn, S.J. and Richardson, W. (1994), "The first ttest", Statistics in Medicine, 13, 785803.
See Also
sleep
for an alternative form of this data set.
Examples
data(CushnyPeebles) # quick looks at the data plot(CushnyPeebles) boxplot(CushnyPeebles, ylab="Hours of Sleep", xlab="Treatment")########################## # Repeated measures MANOVA require(car)CPmod < lm(cbind(Control, L_hyoscyamine, L_hyoscine, DL_hyoscine) ~ 1, data=CushnyPeebles)# Assign withinS factor and contrasts Treatment < factor(colnames(CushnyPeebles), levels=colnames(CushnyPeebles)) contrasts(Treatment) < matrix( c(3, 1, 1, 1, 0,2, 1, 1, 0, 0,1, 1), ncol=3) colnames(contrasts(Treatment)) < c("Control.Drug", "L.DL", "L_hy.DL_hy")Treats < data.frame(Treatment) (CPaov < Anova(CPmod, idata=Treats, idesign= ~Treatment)) summary(CPaov, univariate=FALSE)if (require(heplots)) { heplot(CPmod, idata=Treats, idesign= ~Treatment, iterm="Treatment", xlab="Control vs Drugs", ylab="L vs DL drug") pairs(CPmod, idata=Treats, idesign= ~Treatment, iterm="Treatment") }################################ # reshape to long format, add NsCPlong < stack(CushnyPeebles)[,2:1] colnames(CPlong) < c("treatment", "sleep") CPN < stack(CushnyPeeblesN) CPlong < data.frame(patient=rep(1:11,4), CPlong, n=CPN$values) str(CPlong)
Dataset imported from https://www.rproject.org.